Inter-facility Transport of Critically Ill Children in Ontario · 2017. 7. 18. · Inter-facility Transport of Critically Ill Children in Ontario Janice Tijssen Masters of Science

Inter-facility Transport of Critically Ill Children in Ontario

Janice Tijssen

A thesis submitted in conformity with the requirements

for the degree of Masters of Science in Health Services Research

Institute of Health Policy, Management, and Evaluation

University of Toronto

© Copyright by Janice Tijssen, 2017

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Inter-facility Transport of Critically Ill Children in Ontario

Janice Tijssen

Masters of Science in Health Services Research

Institute of Health Policy, Management, and Evaluation

University of Toronto

2017

Abstract

Inter-facility transport to centralized centres with paediatric expertise is an established

practice. Patient outcomes and resources consumed are not well understood. We performed a

retrospective multicentre observational study of critically ill children who underwent inter-

facility transport to a paediatric intensive care unit (PICU) in Ontario from 2004 to 2012. We

identified 4074 transports. The annual absolute number of transports increased each year. The

system is used by a young population with heavy health care use prior to transport, who

required a significant amount of resources for transport, in the PICU, and for hospitalization

following transport. The PICU mortality rate for transported children was almost double the

general PICU mortality rate. Almost half of ICU deaths occurred in the first 24 hours

following transport. Availability of a paediatrician at the referral hospital was associated with a

lower PICU mortality.

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Acknowledgements

My deepest gratitude is extended to all those who contributed and collaborated on this study.

Data collection: Sheila Berdan (LHSC), Wendy Seidlitz and Katie Spadoni (MCH), Helena

Frndova (HSC), Katie O’Hearn (CHEO), Mahvareh Ahghari and Flo Veel (Ornge). Advice

and facilitation of data transfer: Dr. J. Singh (Toronto Western), Dr. R. MacDonald (Ornge),

Dr. F. Alnaji (CHEO, Ornge), Dr. C. Cupido (MCH), Dr. K-S Lee (HSC). ICES liaison and

analytic support: Salimah Shariff (ICES Western). Finally, this work could not have completed

without the ceaseless support of my committee: Dr. C. Parshuram (supervisor), Dr. T. To, and

Dr. L. Morrison.

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Contents

1 Glossary of Terms .................................................................................................................. vi

2 Background .............................................................................................................................. 1 2.1 Factors that Influence the Decision to Transfer ............................................................... 1

2.1.1 Diagnosis Category ................................................................................................... 1

2.1.2 Illness severity .......................................................................................................... 2

2.1.3 Referral Hospital Characteristics Related to Inter-facility Transfers ....................... 3

2.1.4 Other Factors in the Decision to Transfer ................................................................. 3

2.2 Factors Influencing the Decision about How to Transfer ................................................ 4

2.2.1 Team Composition .................................................................................................... 4

2.2.2 Stabilization Time ..................................................................................................... 6

2.2.3 Mode of transport and distance ................................................................................. 7

2.3 Outcomes Associated with the Transported Population .................................................. 9

2.3.1 Patient Outcomes ...................................................................................................... 9

2.3.2 Resource Utilization of the Transported Population ............................................... 10

2.3.3 Costs Associated with the Transported Population ................................................ 11

2.4 Summary ........................................................................................................................ 12

3 Setting .................................................................................................................................... 14 3.1 The Patients .................................................................................................................... 14

3.2 The Transfer Process ...................................................................................................... 15

3.3 The Receiving Facilities ................................................................................................. 17

3.4 Study Rationale .............................................................................................................. 18

4 Objectives .............................................................................................................................. 20 5 Methods ................................................................................................................................. 21

5.1 Study Design .................................................................................................................. 21

5.2 Eligibility ........................................................................................................................ 21

5.3 Study Outcomes ............................................................................................................. 22

5.4 Data Sources ................................................................................................................... 22

5.5 Variables Abstracted ...................................................................................................... 26

5.5.1 Descriptive Variables .............................................................................................. 26

5.5.2 Outcome Variables.................................................................................................. 28

5.6 Data Management .......................................................................................................... 30

5.6.1 Patient Identification ............................................................................................... 30

5.6.2 Linkages of datasets ................................................................................................ 31

5.7 Conduct and approvals ................................................................................................... 32

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5.8 My Role .......................................................................................................................... 32

6 Analyses................................................................................................................................. 34

6.1 Frequency and Nature of Inter-Facility Transports ........................................................ 34

6.2 Patient Outcomes and Resource Utilization ................................................................... 34

6.3 Factors Associated with Patient Outcomes and Resource Utilization ........................... 34

6.4 Sensitivity Analyses ....................................................................................................... 35

6.5 Validating the DAD CCI codes ...................................................................................... 36

6.6 Sample Size Calculation................................................................................................. 36

6.7 Strengths of the Analytic Plan ........................................................................................ 36

7 Results ................................................................................................................................... 38

7.1 Eligible Transport Episodes ........................................................................................... 38

7.2 Data Completeness ......................................................................................................... 39

7.3 Cohort Description ......................................................................................................... 40

7.4 Temporal Trends ............................................................................................................ 44

7.5 Outcomes ........................................................................................................................ 45

7.5.1 Mortality ................................................................................................................. 45

7.5.2 Length of Stay ......................................................................................................... 48

7.6 Sensitivity Analyses ....................................................................................................... 52

7.7 Validation of the DAD CCI Codes ................................................................................ 52

8 Discussion .............................................................................................................................. 54

8.1 Frequency of Inter-facility Transports ........................................................................... 54

8.2 Nature of Inter-facility Transports ................................................................................. 55

8.3 Patient Outcomes............................................................................................................ 57

8.3.1 Primary Mortality Outcome .................................................................................... 57

8.3.2 Secondary Mortality Outcomes .............................................................................. 61

8.4 Resource Utilization ....................................................................................................... 63

8.5 Limitations of the Study ................................................................................................. 65

8.6 Future Directions ............................................................................................................ 67

9 Conclusions ........................................................................................................................... 70

10 References ............................................................................................................................. 71 11 Appendix ............................................................................................................................... 76 12 Endnotes ................................................................................................................................ 81

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1 Glossary of Terms

AMOSO: Academic Medical Organization of Southwestern Ontario

CAHO: Council of Academic Hospitals of Ontario

CCI codes: Canadian Classification of Health Interventions

CHEO: Children’s Hospital of Eastern Ontario

CH-LHSC: Children’s Hospital of London Health Sciences Centre

CIHI: Canadian Institute for Health Information

CRRT: continuous renal replacement therapy

DAD: Discharge Abstract Database

ECLS: extra-corporeal life support (also known as ECMO: extra-corporeal membrane

oxygenation)

EMS: Emergency Medical Services

GCS: Glasgow Coma Score (assessment of neurologic status)

HBT: Hospital-based teams

HCN: Health Care Number

HSC: The Hospital for Sick Children

ICES: Institute for Clinical Evaluative Sciences

IKN: ICES Key Number (unique identifier)

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IQR: Interquartile Range

ISS: Injury Severity Score

LHIN: Local Health Integration Networks

LHRI: London Health Research Institute

LOS: Length of stay

MCH: McMaster Children’s Hospital

MOHLTC: Ministry of Health and Long Term Care

M-SOFA: Modified Sequential Organ Failure Assessment score

NACRS: National Ambulatory Care Reporting System

NICU: Neonatal Intensive Care Unit

PEWS: Paediatric Early Warning System score

PICU: Paediatric Intensive Care Unit

PIM: Pediatric Index of Mortality score

PRISA: Pediatric Risk of Admission score

PRISM: Pediatric Risk of Mortality score

RPDB: Registered Persons Databases

SD: Standard Deviation

SDS: Same Day Surgery

1

2 Background

Regionalized care networks with centralized expertise make inter-facility transport a necessary

element of modern health care. Critically ill paediatric patients have improved outcomes when

treated in a tertiary care centre rather than a community hospital,1-5

providing a compelling

rationale for the centralization of care. Paediatric critical care is one of the most centralized acute

services. The time sensitive nature of acute severe illness and the need for specialized equipment

and personnel underscore the relevance of the transport system to the care pathway of the

critically ill child.

A decision to transfer a patient to a higher level of care is made based on a number of

characteristics, including the patient’s diagnosis and illness severity and the referral hospital’s

resources. The risks to the patient associated with the transport are a function of the patient’s

illness severity, the duration of stabilization by the transport team, the distance for travel, the

transport team composition, and the mode of transport. This study has been conducted because

these factors and their associated outcomes are poorly studied in critically ill children despite this

population consuming significant health care resources. Furthermore, the paediatric transport

system in Ontario has never been evaluated.

2.1 Factors that Influence the Decision to Transfer

2.1.1 Diagnosis Category

A large U.S. multicentre study reported the most common indications for inter-facililty transport

to a PICU were traumatic brain injury (47.4%), general trauma (23.3%), and asthma/wheezing

(22.9%).6 Other large studies in the U.S. and U.K found respiratory disease to be the leading

cause for inter-facility transfer to a PICU. 7-9

In a single centre study from Michigan, 15% of

2

ICU admissions were the result of an inter-facility transfer and 64% of these were for medical

diagnoses.10

In a study from the United Kingdom, 34% of transported patients had a significant

chronic co-morbidity.11

In a tertiary centre in Australia, 13% of paediatric transfers for trauma

were admitted to the PICU.12

Paediatric trauma patients who were of younger age, had lower

Glasgow Coma Score, mechanism of injury of burn or non-accidental trauma, or with injury to

head/neck region were more likely to be transferred to a higher level of care.13

2.1.2 Illness severity

The severity of illness is an important indicator for the need for inter-facility transport. One study

compared 3 scoring tools (M-SOFA, modified Sequential Organ Failure Assessment Score, an

adult based mortality score; PEWS (Paediatric Early Warning Score), a paediatric based tool to

identify impending cardiopulmonary arrest; and the PRISA (Pediatric Risk of Admission score),

a tool to predict hospital admission in emergency room patients) and found that all 3 had fair-to-

good ability to discriminate between those patients likely to require PICU admission within 48

hours of transfer versus those that would not.14

In one study, the initial PRISM (Pediatric Risk of

Mortality) underestimated the PRISM from when the transport team arrived.8 In another study,

PRISA did not adequately predict which patients would require ICU admission when performed

prior to transfer to the accepting hospital.15

In one study from the United Kingdom, 94.4% of

inter-facility transfers of critically ill paediatric patients were deemed appropriate as they needed

at least one ICU-dependent therapy on the day of admission or had an estimated mortality risk of

>1% (by PRISM-II, “Pediatric Risk of Mortality”).11

Thus, there is some disagreement on

whether the various available illness severity scores are of value in predicting which children

will need admission to a PICU. This speaks to the complexity of how decisions to transfer a child

to a PICU are made, including consideration of referral hospital characteristics.

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2.1.3 Referral Hospital Characteristics Related to Inter-facility Transfers

In one study, patients presenting to a community hospital with paediatric in-patient services who

underwent transfer to a higher centre were more likely to use air transport and require ICU

admission at the accepting hospital.15

This finding suggests that the threshold for transfer may be

higher (i.e. more complex or higher severity of illness) in centres with dedicated in-patient

services when compared to community hospitals without paediatric in-patient services.

Paediatric patients with a history of trauma are more likely to be transferred than adults as

referral hospitals more often lack the resources required for pediatric trauma patients.16, 17

Community hospitals are more often have limited equipment and less well trained personnel to

manage sick children compared to adults. This finding was reinforced in a Canadian study that

found that the majority of community emergency departments in Canada are ill-equipped to

manage critically ill children.18

2.1.4 Other Factors in the Decision to Transfer

Several studies have demonstrated that there are additional variables that influence the decision

to transfer a patient. Insurance status can be a relevant factor in the United States.17, 19-21

In the

U.S., uninsured adult trauma patients were more likely to be transferred to a higher-level centre.

This is unlikely to be a factor in Canada. More geographically isolated hospitals were less likely

to transfer patients in an undifferentiated adult trauma cohort. For every 10 miles away from a

higher-level centre, patients had an OR (IQR) of 0.63 (0.52-0.77) of transfer.17

Time of day was

not associated with transfer decisions in another study.16

In summary, a decision to transfer a patient to a higher-level centre is based on the diagnosis, the

illness severity, characteristics of the referral hospital, including its location. The next important

4

decision to be made is how the patient should be transferred. This includes consideration of team

composition, stabilization time, and mode of transport.

2.2 Factors Influencing the Decision about How to Transfer

2.2.1 Team Composition

Several studies have suggested that the use of teams specialized in paediatric critical care was

associated with improved outcomes.7, 22-25

The rate of unplanned events and/or death were

increased for patients transported by non-specialized teams,23

even when controlling for severity

of illness.7, 22, 25-27

However, a well-powered U.S. study in 2016 showed that after propensity

score matching, a specialized transport team was not associated with lower 48 hour PICU

mortality compared to a generalist transport team.27

The reality is that a specialized team may

not always be available when there is a high volume of critical care transports at once or when

the patient is in a very remote location with a time-sensitive critical illness. Nonetheless, the

most recent American Academy of Pediatrics recommendations states that the transport team

should always be composed of a nurse with at least 3-5 years of paediatric critical care

experience as well as one of a respiratory therapist, paramedic or a physician.28

Most transports,

however can be safely performed without a physician.29-31

Team members who have received

extensive paediatric-specifc training would be able to build their expertise, comfort and

experience managing paediatric patients because they concentrate their transport hours on this

population.32

Members of the transport team must be proficient in performing a number of critical

interventions. In one study, on average the team performed 2.8 interventions per transport with a

third of them considered major (e.g. advanced airway, central line, chest tube insertions).9

Another study demonstrated that 82% of paediatric critically ill patients had an intervention

5

(75% of these were procedural, the rest pharmacologic) performed by the transport team.33

In

one paediatric study of critically ill children, 67% had a clinical problem observed that was

secondary to undertreatment on arrival of the transport team.34

Five percent of critically ill

children had a physiologic deterioration (increase in PRISM score by >2) en route to the ICU,

while 20% had a critical incident (e.g. hypoxia, hypotension, hyperthermia).35

In an adult cohort

of critically ill patients transported by air in Ontario, the rate of critical events (cardiac or

respiratory arrest, hypotensions, inadvertent extubation, an event that requires a major procedure,

or death) was 5.1%.36

This study also demonstrated a 2% increase in critical events for every 10

minute increase in duration of transport. In all children (not just critically ill) transported by the

same provincial service in Ontario, critical events occurred in 12.3% of transports and had an OR

of 5.4 (95%CI 4.3-6.8) if the patient was mechanically ventilated and had cardiac instability

prior to transport.37

Depending on the level of training of the transport team, the team will have access to a range of

equipment. These include, but are not limited to: airway equipment (from simple oxygen

delivery systems to advanced airways and ventilators), circulatory support (from intravenous,

intraosseus, and infusion pump equipment to central lines, inotropes, drugs and defibrillators),

transport and personnel equipment (backboards, stretchers, isolettes, uniforms for all weather

conditions, helmets, boots), and communication devices (phones, including satellite phones, and

pagers).

In summary, if available, a specialized team should be used to transport a critically ill child

because of the likelihood for critical interventions and the team’s familiarity with critically ill

children and complex equipment and drugs. The potential for deterioration is always considered

when a decision for transport is made. The decision must balance availability, timeliness, and

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expertise of the team against the risk of delay in getting the patient to the PICU. The time needed

to safely transport the patient also incorporates the stabilization time.

2.2.2 Stabilization Time

The stabilization time is the time the team takes to stabilize the patient and prepare them and

their equipment prior to transporting the patient. The team must balance the risk of delaying

definitive intensive care with the benefit of reducing the likelihood of further deterioration en

route. Spending more time at the referral hospital allows the transport team more opportunity to

familiarize themselves with the patient’s history and clinical exam as well as to secure existing

equipment and perform interventions when indicated. However, while these may likely benefit

the patient immediately, they may also delay important decisions and interventions that can only

occur in a centre with a higher level of care. In one study, the stabilization time for more severely

injured patients was 5 times longer compared to milder cases despite no increase in the need for

interventions.12

Furthermore, this study showed that stabilization times were double for

paediatric specialized teams as compared to non-specialized teams.12

A prolongation of

stabilization time was demonstrated in another study in more severely ill paediatric patients and

with certain diagnoses.9 In this study the number of interventions performed by the transport

team was associated with a longer time to departure. A significant finding from this study was

that prolonging the stabilization time was not associated with an increase in early mortality.

Another study demonstrated that stabilization time was longer for patients with a medical

diagnosis than those with trauma diagnosis in one single centre study.38

Furthermore, younger

patients and those requiring a major procedure by the transport team had longer stabilization

periods.

7

In summary, stabilization times are prolonged for sicker patients, those who require more

interventions, and those with certain diagnoses. Longer stabilization time has not been shown to

be associated with increased mortality.

2.2.3 Mode of transport and distance

The major factor in choosing to transport the patient by air (rotor or fixed wing) or land is the

distance to travel. In a cohort of adult trauma patients, the risk of mortality was increased in

patients originating from more distant rural compared to metropolitan regions when controlling

for severity of injury and age.39

The risk increased more than four-fold for every 1000

kilometres from the trauma centre.39

The authors hypothesized that this finding was due to a

delay in arrival of ambulance services at the scene of the injury, as once a retrieval service

arrived, the mortality rate was equivalent. This finding was also demonstrated in the paediatric

trauma population, where survival was similar when controlling for injury type and severity

between rural patients who survived to be transported and urban patients who survived the first

24 hours after the injury.40

In another study, distance was not associated with outcome in a

population of undifferentiated critically ill paediatric patients, but distances traveled were small

(median distance approximately 30 kilometres).26

In trauma patients, emergency response teams

consider bypassing a community hospital in order to obtain earlier specialized care at a trauma

centre when the distance isn’t substantially larger. A paediatric trauma study found that for

patients with major trauma (Injury Severity Score, ISS>=15), mortality was lower (26.7 v 15.5%,

p=0.009) in those transported directly to a trauma centre compared to those initially stabilized in

a community hospital.41

For distances greater than 200 kilometres, air transport is preferred as the difference in duration

of travel becomes significant. Several studies have investigated the use of air transport for

8

shorter distances. In a subgroup analysis of a Cochrane review, there were 4 studies which

demonstrated improved outcomes in adult trauma patients who underwent inter-facility transport

by helicopter compared to ground transport.42

The theories applied to explain this finding were

that the crews on helicopter transport were better trained and the transport itself was shorter. In

paediatric trauma patients, there was no difference in mortality, however there was a reduced

hospital length of stay for patients transported by helicopter compared to ground transport when

matched on propensity scores.43

In another paediatric study, there was no difference in the risk of

adverse events or physiologic deterioration between air and ground transport for critically ill

patients undergoing inter-facility transport.35

The time it took for the transport team to arrive at

the referring hospital was longer for air transport.

An important consideration when choosing air transport is the fall in pressure with climbing

altitudes. This is especially relevant when the patient has air trapped in a closed compartment

(e.g. pneumothorax), which will expand and potentially cause further damage with falling

pressures. Cabins can be pressurized to mitigate this problem to a certain degree when air

transport is the only option. Furthermore, critically ill paediatric patients are also at increased

risk for desaturation at higher altitude due to a reduction in the partial pressure of inspired

oxygen, as demonstrated in one study which utilized Near-Infrared spectroscopy to monitor

oxygen levels.44

In summary, a number of factors are considered when choosing mode of transport, including

distance, diagnosis (e.g. trauma, pneumothorax, hypoxia), clinical urgency, and availability of

the various modes. Mode of transport may be associated with differences in level of expertise,

both of which may influence outcome.

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2.3 Outcomes Associated with the Transported Population

2.3.1 Patient Outcomes

Children undergoing inter-facility transport for specialized PICU care, are by definition critically

ill and have a real and perceived risk of mortality in the short term. PICU mortality following

transport has been reported as 4-8% from studies in the U.S., and the U.K..26, 45

The large multi-centre study from the United States did not show a difference in crude or

severity adjusted mortality between patients transported and those admitted to ICU from within

the hospital. The U.K. study, however, showed that transported patients had a higher crude

mortality (8%) than patients admitted directly to the (6%) but also had a higher severity of

illness (by Pediatric Index of Mortality score, PIM).26

The PIM-2 is a score performed at the start

of ICU contact, including at arrival of the specialized transport team. This was also shown to be

the case in a large single centre study from Michigan.46

The latter two studies included patients

who had been transferred from another PICU. Both demonstrated no difference in mortality

when controlling for severity of illness on admission to the receiving PICU.

A high PRISM in a referral hospital accurately predicted mortality for patients subsequently

transferred to a PICU,47

whereas a low PRISM (<=10) did not necessarily translate to the

opposite with a negative predictive value of 57%.8 In one paediatric study, the PIM-2, number of

major interventions by the team, and referral category (e.g. sepsis, respiratory, neurologic,

trauma) were associated with early PICU mortality. Of note, non-acute transfers were included

as a referral category in the analysis. Stabilization time and adverse physiologic events en route

were not independently associated with early PICU mortality (1st 24 hours of PICU admission).

9

In a study of acute and non-acute inter-facility paediatric transfers, the in-hospital mortality was

6% and was not independently associated with discordance in diagnosis category between the

10

referring and receiving physicians.48

In a cohort of transported trauma patients ≥ 15 years of age,

mortality was associated with injury severity, age, and type of trauma (but not distance of

travel).49

In addition to length of stay and mortality, other patient-relevant outcomes include the rate of

new morbidities, and the social and economical burden on the patient and family (from being

separated from their support network and from other children, comforts of home, and absence

from work, travel and lodging costs). Though discussed in the literature,50-52

these areas have not

yet been studied in the transported paediatric population and are out of the scope of this study.

2.3.2 Resource Utilization of the Transported Population

Critical care therapies that are most relevant when discussing resource utilization include, but are

not limited to, mechanical ventilation, inotrope therapy, continuous renal replacement therapy

(CRRT), extra-corporeal life support (ECLS, also known as ECMO) and nursing workload.

There are other critical care interventions, such as cardiopulmonary resuscitation, hypothermia

therapy, and line insertion. Cardiopulmonary resuscitation is very resource intense but is brief

and uncommon in paediatrics (1% of admitted patients in PICU).53

Hypothermia therapy is

resource intense and can last several days; however, it remains a therapy looking for a proven

indication after the neonatal period and thus is inconsistently used. Line insertion (chest tube,

central line, arterial line) can be resource intensive at initiation but generally does not require the

same intensity of ongoing monitoring. A higher intensity of monitoring (such as for CRRT)

results in frequent re-evaluation of the patient, their support settings, and blood work with

regular adjustments and re-evaluations. Thus, it is the use of and duration of these therapies that

is most relevant for this study.

11

Before regionalization of intensive care services was standard, resource use in a non-tertiary

intensive care setting compared to a tertiary care’s ICU for critically ill paediatric patients was

significantly lower as demonstrated by lower Therapeutic Intervention Scoring System scores

(TISS, consists of 76 therapeutic and monitoring modalities) after adjusting for severity of

illness.4 In the United Kingdom, critically ill paediatric patients who had been transported to the

PICU had higher resource utilization (invasive ventilation, renal replacement therapy, vasoactive

drugs, etc) and longer length of stay compared to those admitted to the PICU from within the

same hospital.26

Similarly, in a large cohort of 21 PICUs in the United States, the use of

mechanical ventilation and vasoactive drugs within 24 hours of admission to the PICU was

higher in transported patients compared to patients admitted from within the same hospital.45

Length of stay was longer in those transported from another hospital’s in-patient ward,

compared to those admitted directly from the same hospital’s inpatient ward. Furthermore, the

patients admitted from a referral hospital’s inpatient ward compared to a referral hospital’s

emergency department had higher mechanical ventilation and lower vasoactive drugs use in the

first 24 hours of admission and also had higher PRISM-III scores. Average PICU length of stay

from these studies for transported patients ranged from 3 to 6 days.

In summary, the important resource utilization outcomes of the transported population include

length of stay and duration of critical therapies.

2.3.3 Costs Associated with the Transported Population

The training, provision and maintenance of equipment, salaries (including overtime dues), and

transportation make this a very costly operation. It costs approximately $1.5 million to operate

and maintain a transport team for 1 of the 4 catchment areas of Ontario for one year. On a per

transport basis, this amounts to over $8000 per transport. A team must always be available, even

12

though transports may not occur on each shift of every day. This, of course, does not account for

vehicles (planes, helicopters, land ambulance) and the human resources required for operation or

maintenance of these. The transport team budget is often absorbed within other PICU costs. The

costs of running other transport teams in Ontario are not available, easily interpretable or

necessarily transferrable. There are no cost-effectiveness studies of transport systems in Canada

or elsewhere.

Additionally, the non-comprehensive cost of a PICU admission is high. In one multicentre U.S.

study of traumatic brain injury, the median cost was 60,000 USD54

and in another single U.S.

centre, the average daily cost of any patient was 5432 USD.55

2.4 Summary

Centralization of care is established ‘best’ practice, and necessitates inter-facility transport. A

decision to transport a patient is based on clinical factors and physical and human resources

available at the referring centre. The patients presenting with critical illness range from the

previously well child with traumatic injuries, to those with chronic co-morbidities and conditions

associated with their acute presentation. Previous large observational studies from other

jurisdictions suggest that transported patients are more likely to have respiratory disease or

trauma, have co-morbidities, and have a higher severity of illness; specialized teams are

preferred, as advanced interventions are commonly provided and in-transport adverse events

affect 12- 20% of tranported children, however the use of these teams is associated with

increased stablization times. Most observational studies suggest that children have better risk-

adjusted outcomes when transported by specialized teams, to specialized centres. Mode of

transport is dependent on distance, which can be up to 1500 kilometres in Ontario. The realities

13

of operationalizing these findings are complex and have great potential to consume significant

healthcare resources.

There is a paucity of data on the patient and transport factors that are associated with outcomes,

particularly in Ontario and Canada. As a geographically large area with a growing population, a

formal evaluation of this population is needed.

14

3 Setting

3.1 The Patients

Inter-facility transfer for paediatric patients includes the spectrum of extremely pre-term

newborns and adolescents up to 18 years. This study focuses on children, from newborn age to

late adolescence, who are transported from one facility (non-PICU) and are admitted to a PICU.

Newborns with predefined diagnoses (e.g. congenital cardiac disease, congenital diaphragmatic

hernia) that are admitted to PICU are included in this study.

Newborns that are transported to a neonatal intensive care unit (NICU) are beyond the scope of

this project as they are in many ways different to the transported paediatric population.56

For

example, they present with entirely different range of diagnoses (e.g. related to prematurity, i.e.

respiratory distress syndrome and intraventricular hemorrhage), require a different set of skills

by the transport team (e.g. insertion of an umbilical venous catheter), and above all, can be

admitted to many more locations (i.e. level 2 or 3 NICUs) than a critically ill paediatric patient.

Inter-facility transports between PICUs for critically ill children and retro-transfers from PICUs

back to the referring hospitals for non-critically ill children are beyond the scope of this study as

well.

It is not known how many children undergo inter-facility transport in Ontario annually. In the

U.S., about a third of all PICU patients (n=4,414) were admitted following an inter-facility

transfer.45

In the London, UK region, approximately 1000 children are transported to one of 4

PICUs annually.i The transported population faces additional and unique challenges, including

substantial transportation distances and inclement weather. Based on the current literature in

15

Canada, the rate and the impact on outcomes of inter-facility transport of critically ill children in

Ontario or other Canadian jurisdictions are unknown.

3.2 The Transfer Process

There are fourteen LHINs (Local Health Integration Networks) in Ontario, each with various

health services, from nursing stations predominantly found in LHINs 13 and 14 to teaching

hospitals found in the LHINs with the larger urban centres (e.g. 2, 4, 7, 11, Figure 1).

There are over 200 referral health care facilities in Ontario. Since 2010, the referral process

between these facilities and the PICUs has been based on these LHINs. Prior to 2010, the referral

process was based on shortest distance and pre-existing informal relationships between clinicians

and facilities.

Figure 1. Local Health Integration Networks of Ontario

16

The process for transporting a paediatric patient to a centre with a higher level of care is well

established in Ontario, which is not necessarily the case in other regions.57

CritiCall was

developed in 1996 as a means of connecting a referring physician in a community hospital with a

subspecialist at the regional centre. The subspecialist is available to their region by phone 24

hours a day, 7 days a week, to provide advice and arrange for transport and a bed at the regional

centre. The roles and responsibilities for each party are well defined, including the selection of

the type and urgency of transport which is done by the accepting physician in discussion with the

referring physician. In the case where only advice is requested, the referring physician and

subspecialist can remain in contact through the course of the patient’s illness. In the case where

the subspecialist cannot accept the patient (e.g. due to lack of resources), the subspecialist

remains involved until an accepting physician can be located. Regular quality assessment

reviews occur in order to ensure that timely transfer occurs when indicated. Based on severity of

illness and planned disposition, an appropriate transport team is triaged by the accepting

physician and transport program delegate. Because these vary, the team composition also varies.

However, once the team is dispatched, it is logistically challenging to alter the team, with the

exception that an additional team member from the referring hospital (e.g. emergency physician)

can be added. This situation could occur if the patient unexpectedly deteriorates and the

emergency physician can leave his/her post to escort the patient with the transport team.

Operational limitations preclude this sort of alteration from happening routinely. The team’s

arrival at the patient’s bedside can take several hours for more remote areas. Once at the bedside,

the team is responsible for ensuring that the patient remains sufficiently stable for transportation.

This often requires more interventions, including diagnostic or therapeutic, which can take

several hours. Once the patient is transferred to a stretcher or transport isolette (for young

17

infants), the transport team connects with a transport physician to review the patient and to draw

up plans for transport, including contingency plans in case the patient deteriorates. Transporting

the patient to the accepting hospital can involve multiple modes of transport and take several

hours.

In Ontario, most of the nursing stations are fly-in only (i.e. no road access), making air transport

a must for these locations. Furthermore, many remote locations are simply too far to make land

transport a feasible option. However, air transport, especially rotor, is affected by weather

conditions such as thunder storms, major snow storms, or high wind velocities. Thus, operational

decisions often impede the transport preferences that are made based on clinical urgency.

3.3 The Receiving Facilities

Receiving PICUs differ significantly from each other based on the proportion of patients

admitted following inter-facility transport, the use of mechanical ventilation, vaso-active and

inotropic infusions, efficiency, severity of illness scores, and severity-adjusted mortality.45

Ontario’s critically ill paediatric population is served by 4 paediatric intensive care units located

in Ottawa, Hamilton, London, and Toronto. Ornge is a provincial transport system that is

involved in all air transports in Ontario, either providing the transport alone or with medic

support. Ornge can transport a patient from any location in Ontario to any of the 4 PICUs. When

land transportation only is provided, the medical support is provided by one of the hospital-based

paediatric critical care teams (HBT). Many land transports for critically ill children are also

facilitated by Ornge. In the cases where Ornge is not involved, the HBT from one of the four

hospitals transports the patient.

18

3.4 Study Rationale

Over the last decade there have been significant changes to the delivery of health services to

Ontario’s critically ill paediatric population. There has been an increase in regionalization of

critical services (e.g. cardiovascular surgeries at The Hospital for Sick Children in Toronto,

Ontario) and a reduction in the number of available centers, as Kingston General Hospital

withdrew from accepting critically ill children (officially in 2007).

There has also been an increase in the use of specialized transport teams. The provincial

transport system expanded to include the transport of children, and the hospital-based team from

The Hospital for Sick Children expanded to transport older children. Referral patterns changed

with the introduction of the LHINs. Finally, the teams have learned to use newer technologies

(e.g. heated humidified high flow nasal cannulae) and adopt changing transport medicine

guidelines (e.g. hypothermia therapy post cardiac arrest).

Despite these changes, the last study of paediatric transport outcomes in Ontario was based on

data from a single center and was conducted more than 20 years ago.33

While the population of

all children in Ontario has not grown in the last decade,ii there has been an increase in the

number of children with chronic and complex medical conditions that rely on PICU-level

support in the home (e.g. nursing and home ventilation). In previous times, death or, less

frequently, institutionalization were the only possible outcomes for these technology-dependent

patients. When these patients fall ill, they usually need to be admitted to a PICU due to their

technology dependence via inter-facility transport. A recent Canadian study found that there are

12.9/100,000 children on home ventilation support,58

the result of an exponential growth in

number of children on ventilation support at home affiliated with one Canadian centre over the

last 20 years.59

Furthermore, the care of certain high risk patients has been de-centralized in the

19

last decade, for example there are now satellite cancer centres that treat paediatric patients.

Previously, these patients would have been treated at the PICU hospital and so if a complication

or deterioration occurred, would already by on-site. Now, these patients require inter-facility

transport to the PICU at the tertiary care centre. In summary, we anticipate that there has been an

increase in the need for specialized transportation of critically ill children in Ontario due to

increased regionalization of critical care services, reduced number of PICUs, and increased high-

needs patients in the communities. To date, despite all of these changes, no evaluation of the

transport system has been undertaken.

In order to determine the effectiveness of a program one must obtain a better understanding of

the program itself, its impact, the resources utilized, and the people it involves. It is fiscally

irresponsible to continue to operate at the status quo or make changes to it without the relevant

information. Opportunities for improvement of the system surely exist but as it stands, changes

get implemented without data relevant to the system as a whole. This can lead to unintended

consequences. For example, as one critical care transport team increases their capacity,

another’s transport volumes may be reduced. This may lead to poorer quality of care, or even

discontinuation of services, which would have great implications on the patients requiring

transport in the far reaches of the province that are less accessible by HBTs.

Understanding the service demands and patient outcomes is essential to making our healthcare

system effectively support a geographically diverse settlement.

20

4 Objectives

The objectives of this study are to:

1. Describe the frequency and nature of inter-facility transports,

2. Measure the patient outcomes and resource utilization,

3. Evaluate the factors associated with patient outcomes and resource utilization,

in critically ill children undergoing inter-facility transports to a PICU in Ontario.

21

5 Methods

5.1 Study Design

We conducted a population-based observational study of critically ill children who underwent

inter-facility transport to a critical care unit in Ontario in the period of 2004-2012 (Figure 2) to

address the three study objectives.

Figure 2. Study Design

5.2 Eligibility

Eligible patients included all paediatric patients (newborn to less than 18 years of age) with a

direct admission to an academic paediatric hospital (McMaster Children’s Hospital (MCH),

Children’s Hospital of London Health Sciences Centre (CH-LHSC), The Hospital for Sick

Children (HSC), Children’s Hospital of Eastern Ontario (CHEO)) and had one or more transports

during the study period.

Eligible transports included any transport from a referral centre that was not a PICU or the scene

of a trauma during the study period. Furthermore, transports were excluded if the patient was

transfered to a PICU out of Ontario, if death occurred during transport, or if they were a direct

admission from an on-site Labour and Delivery suite.

22

5.3 Study Outcomes

Primary Outcome: ICU mortality for the PICU admission following transport

Secondary Outcomes:

1. Patient outcomes: Mortality at 24-hours and 6-months after transport

2. Resource utilization: PICU and hospital length of stay (LOS), intensive care life support

utilization (i.e. invasive and non-invasive mechanical ventilation, dialysis, inotropes,

ECMO)

3. Transport frequency: number of transportations for critically ill children per year, number

of children who undergo more than one transport in the study period

Mortality was evaluated following the date of the transport and reported at 24 hours after ICU

admission, ICU discharge (for the ICU admission following transport) and at 6 months after

index admission. Six month mortality was reported for the first transport only for the patients

who underwent more than one transport during the study period. PICU LOS and interventions

were reported for the ICU admission following transport. Hospital LOS was also reported for the

hospitalization following transport. Patients were followed for 6 months for outcomes.

5.4 Data Sources

Data were obtained from 6 sources (#1-6 listed below and summarized in

Table 1) to support identification of eligible patients, transport events, and variables. An

additional 2 sources (#7, 8) were used for outcome variables. Three nationally reported data

sources were used (#5, 6, 7), one provincially (#1), and four hospital-based (#1-4).

23

1. Ornge: Every child who was transported by Ornge over the study period is included in the

database. The linkage with Canadian Institute for Health Information’s Discharge Abstract

Database (CIHI DAD) allowed us to identify eligible patients: all those transported and

admitted to a critical care unit without a visit to the emergency department (National

Ambulatory Care Reporting System, NACRS) in the same hospital as the PICU on the same

day.

2. CH, LHSC: The transport dataset from Children’s Hospital, London Health Sciences Centre

identified all eligible patients transported by the hospital team. For patients transported to

CH, LHSC by Ornge, a linkage was performed with the Ornge dataset and CH, LHSC’s

datasets (Critical Care Information Systems, PICUe database, admission registry, the

respiratory therapy database, and medical records). Patients transported by teams other than

the hospital team and Ornge (e.g. local emergency medical services, EMS) were not

identified.

3. MCH: The transport dataset from McMaster Children’s Hospital was obtained by merging

two transport datasets: NeoTRAC (Ontario’s Neonatal Transport Collaborative of MCH) and

the PICU transport database. This database included all eligible patients transported by the

hospital team. Medical records were reviewed for ICU resource utilization. Prior to 2006,

McMaster did not have a transport team.

4. HSC: The transport dataset from the Hospital for Sick Children identified all eligible patients

transported by the hospital team. This dataset was linked with the PICU database

(“ORACLE” to which data is extracted from the electronic medical chart) to obtain ICU

resource utilization.

24

5. CIHI-NACRS and Same Day Surgery (SDS): These databases were reviewed for 2 purposes:

1) to identify prior health care contact by frequency of emergency department visits and same

day surgeries in the 6 months prior to the transport, excluding the visit linked to the transport

and 2) to rule out a visit to the emergency department of one of the 4 PICUs on the day of the

admission to the PICU. These were identified by dates of visits which are mandated for

reporting.

6. CIHI-DAD: This database was reviewed for health care contact (admissions) in the 6 months

prior to the transport and for outcomes (mortality and length of stay) within 6 months of the

transport for all study patients.

7. RPDB: The Registered Persons Database was used to confirm sex and to determine if a

patient died within the 24 hours of PICU admission or the 6 month follow-up period outside

of a hospital setting.

8. CHEO: The data obtained from CHEO was used to describe the ICU resource utilization of

the transported population to CHEO. This was obtained from both electronic and paper-based

charts. As CHEO did not have a transport team, the transported population was identified

from CHEO’s Health Information Analysts Decision Support by “ICU admissions from

Other Institutions”. Once the ICU resource utilization data was transferred to ICES, the

records were merged by a unique identifier with the other datasets. Inter-facility transports

would have been completed by HBT from other centres, Ornge, and EMS. However, of

these, only Ornge had transport data available for linking due to REB and DSA restrictions.

Any CHEO records that were non-linkable were for patients transported by EMS and HBT.

25

Table 1. Data Sources

Dataset Study years

available

Health

care

contact

Transport

variables

ICU

interventions

LOS Mortality

Ornge 2004-2012

CH, LHSC-

Transport

Health

records

2004-2012

2004-2012

MCH-

Neotrac

PICU

Health

records

2007-2012

2006-2011

2006-2012

HSC-

ACTS

Health

records

09/2004-

2012

2004-2012

CHEO-

Health

Records

Health

Information

Analyst’s

decision

Support

2004-2012

CIHI-DAD 2004-2012

CIHI-NACRS 2004-2012

RPDB 2004-2012

CH, LHSC: Children’s Hospital, London Health Science Centre; MCH: McMaster Children’s

Hospital; HSC: Hospital for Sick Children, ACTS:Acute Care Transport System; CHEO:

Children’s Hospital of Eastern Ontario; CIHI-DAD: Canadian Institute for Health Information-

Discharge Abstract Database, NACRS: National Ambulatory Care Reporting System; RPDB:

Registered Persons Database

26

5.5 Variables Abstracted

To ascertain the frequency of inter-facility transports (Objective 1), we reported the number of

transports by year for each study year and as a rate per 100,000 population of children 0-14

years.

Figure 3. Variables

5.5.1 Descriptive Variables

To address the nature of the transfers (Objective 1), we stratified transfers by the patients’

characteristics (age, sex, diagnosis), transport characteristics (team, mode of transport, time to

arrival of team, stabilization time, transport time, total transport team contact time, distance of

transfer, and time of day, season), and hospital characteristics (type, availability of a

paediatrician, and LHIN of the referral hospital, and accepting PICU) (as summarized in Figure

3).

Prior health care contact was described in several ways: number of emergency department visits,

same day surgeries, hospitalizations as separate variables, the sum of these but with

27

hospitalizations changed to days of hospitalizations. An age-weighted variable was defined to

reflect the disproportionate opportunity for health care contact in a 6 month period for a child

less than 6 months old compared to an older child. This age-weighted variable was defined as the

sum in total days in the 6 months prior to the index visit divided by age in days for patients less

than 6 months and by 6 months for those older than 6 months at the time of the index event.

Total days of hospitalizations may have overlapped the 6 month cut-off for the look-back

window if the admission started before 6 months. Furthermore, if there was an emergency

department visit on the same day as a hospital admission, it was counted as 2 contact episodes.

Diagnosis for every record was manually assigned one of 8 categories based on major system

(e.g. neurologic) or type of admitting diagnosis (e.g. trauma) by the author, thus consistency was

maintained. The eight diagnoses included respiratory, cardiac, neurologic, sepsis, trauma, toxic,

metabolic, or other. Any general surgical patient or other organ failure (e.g. liver failure) were

included in ‘other’.

The time to arrival of the transport team reflects the time to critical care contact and is the time

interval between the team being dispatched and arriving at the patient’s bedside at the referral

hospital. The stabilization time is the time the transport team spends at the referral hospital

stabilizing and preparing the patient for transport. The transport time is the interval from

departure from the referral hospital and arrival at the PICU. The total transport team contact with

the patient is the interval between arrival at the patient’s bedside at the referral centre and arrival

in the PICU. All the transport intervals were computed from the various event times.

The distance was calculated in kilometers and is the shortest driving distance without traffic for

hospitals that have street addresses using Bing maps by CritiCall’s website.iii

Nursing stations

without street addresses instead used “as the crow flies” approach. The type of referral hospital

28

included teaching, community, small, and nursing station. Teaching hospitals included acute and

paediatric hospitals that have membership in the Council of Academic Hospitals of Ontario

(CAHO) and provide highly complex patient care, are affiliated with a medical or health sciences

school and have significant research activity and postgraduate training. Small hospitals are single

community providers with total annual patient load under 2700 admissions, complex care cases,

and same day surgeries. Community hospitals included those hospitals not otherwise defined as

teaching or small.iv

A nursing station is a Health Canada clinic in Northern Ontario located in

remote First Nations communities and is staffed by a registered nurse with telephone contact

with physicians. The availability of a paediatrician was determined by whether the center has a

level 2 or above neonatal unit.v

5.5.2 Outcome Variables

5.5.2.1 Patient outcomes

Mortality reporting is mandatory for DAD. Length of stay (LOS) was derived by calculating the

difference between the SCU (special care unit) admit date and time and the SCU discharge date

and time (thus LOS is provided in hours). A “SCU hours” variable where this calculation has

been performed. Reporting is mandatory for these variables. “Calculated Length of Stay” for

time spent in hospital is the difference in days between the admit and the discharge dates. This

variable is also mandatory. Hospital length of stay has been validated and is 100% accurate but

validation of the SCU variable is lower. A validation study analyzing SCU admit and discharge

dates from 2001 and 2002 found that the SCU variable had a positive predictive value of 91%

but a sensitivity of only 26%. Also, when tested against the gold standard database (Critical Care

Research Network), there was an 81% agreement for ICU length of stay.60

However, SCU

29

coding was only made mandatory in Ontario part-way through the above study (April, 2002) so

one would expect a much improved validity for this study’s time frame.

5.5.2.2 Resource utilization

Hospital administrative data collected by the CIHI has the intervention codes for mechanical

ventilation, CRRT, and ECLS and are based on the Canadian Classification of Health

Interventions (CCI codes). These are considered mandatory in Ontario but have not been

validated in paediatric age ranges.

There are 2 methods to determine if a patient has received mechanical ventilation using the

DAD- one by the CCI code (Incode 1-20: IGZ31 CBND for non-invasive mechanical

ventilation and IGZ31 CAEP, CAND, and CAPK for invasive) and the other by the mechanical

ventilation flag (Flag_mvent_GE96 or LT96). The former distinguishes between invasive and

non-invasive ventilation but not location of these (e.g. a patient may have received non-invasive

ventilation on the general paediatric ward). The distinction between invasive and non-invasive

mechanical ventilation support is important. Non-invasive mechanical ventilation usually

represents milder disease severity and typically does not require concurrent sedation or

analgesia. The mechanical ventilation flag does not make the mode of ventilation distinction but

describes the duration of ventilation- as less than 96 hours or greater than or equal to 96 hours.

The CCI code for CRRT is IPZ21HDBS and for ECMO it is ILZ37GPQM and LAQM (which

should not be used for cardiopulmonary bypass in the operating room or ventricular assist device

use). The CCI does not include inotrope or vaso-active therapy. Therefore, by limiting the

resource utilization outcome to the data available in CIHI, the results would be incomplete.

Furthermore, the CCI data have not been validated in paediatric age ranges. Thus, in addition to

the CCI data, chart abstraction (as describe in data sources) was performed. We measured

30

duration of critical care therapies to describe resource utilization (Objective 2). ICU resource

utilization was defined as use of whole or part days of mechanical ventilation (invasive and non-

invasive separately), continuous renal replacement therapy (CRRT), inotrope use (epinephrine,

dopamine, norepinephrine, dobutamine, vasopressin, milrinone), and extra-corporeal membrane

oxygenation (ECMO). The sum of the use of these interventions was computed to reflect overall

ICU intervention utilization. Chart abstraction data were compared to CCI to ascertain the

validity of CCI data. Nursing workload, such as frequent suctioning, multiple medication

delivery, and wound care, are not consistently captured across centres and thus were not

included.

Some variables that were not studied included critical events en route, interventions performed

by the transport team, time spent in overtime (i.e. more than 12 hour shift) by the transport team,

and costs. These variables were rarely documented and thus could not be analyzed.

5.6 Data Management

5.6.1 Patient Identification

The eligible population was identified from the transport databases of the Children’s Hospital,

London Health Sciences, Acute Care Transport System (ACTS, The Hospital for Sick Children,

HSC), McMaster Children’s Hospital and the Canadian Institute for Health Information’s (CIHI)

Discharge Abstract Database (DAD) with linkage to the Ornge database. Defining the population

within the Ornge dataset required linkage with CIHI’s DAD and NACRS to exclude patients

who were seen in the emergency department at one of the 4 PICU hospitals in the same time

frame.

31

5.6.2 Linkages of datasets

After identification of the population, linkage was established between transport datasets

(described below), including that of Ornge, with CIHI’s DAD, SDS, and NACRS, and the

Registered Persons (RPDB) databases through the Institute for Clinical Evaluative Sciences

(ICES).

Once datasets were securely transferred to ICES, a secure version of the Ontario RPDB acted as

the standard for matching unique identifiers (ICES Key Number, IKN) with each person.

Records had personal health information (PHI) removed and an IKN was assigned to each file.

An IKN for each person’s record allowed the newly de-identified data set to be linked with all

ICES health services databases. Previous experience suggests matching of the PHI to obtain the

IKN is 75-80% successful based on health card number (HCN) alone and an additional 15-20%

with probabilistic linkage using first name, last name, gender, and date of birth.vi

CIHI’s DAD and NACRS were linked in ICES to the hospital-based datasets (CH, LHSC, HSC,

MCH) by HCN and to Ornge (2006-2012) by HCN and by probabilistic methods for Ornge for

the earlier study years. In an adult study using the Ornge database, 85% of patients could be

linked by this method.36

The remaining 15% were almost entirely from scene-to-hospital

transports, whereby the name of the patient was missing. Scene-to-PICU transports were

excluded in this study, thus we anticipated that this method of probabilistic linkage would be

almost 100% inclusive.

Each transport during the study period was assigned a unique identifier. Therefore, patients with

more than one transport had more one than such identifier. This variable was subsequently used

to merge the hospital-based transport team datasets with the provincial transport dataset. In the

event of a duplicated record (i.e. when a HBT and Ornge collaborated on a transport), the Ornge

32

record was excluded. In these cases, the primary transporting team was the hospital team and

Ornge simply provided vehicle support and occasionally a primary level paramedic.

5.7 Conduct and approvals

Research ethics approvals for this study were obtained from London Health Research Institute

(LHRI, University of Western Ontario), McMaster University, The Hospital for Sick Children,

the Children’s Hospital of Eastern Ontario, and the University of Toronto. Additionally, all ICES

studies are approved by the institutional review board at Sunnybrook Health Sciences Centre.

Data sharing agreements were obtained between Ornge and ICES, and LHRI and ICES.

This study was supported by the ICES Western site. ICES is funded by an annual grant from the

Ontario Ministry of Health and Long-Term Care (MOHLTC). Core funding for ICES Western is

provided by the Academic Medical Organization of Southwestern Ontario (AMOSO), the

Schulich School of Medicine and Dentistry (SSMD), Western University, and the Lawson Health

Research Institute (LHRI). Specific project funding was provided by the peer-reviewed AMOSO

Opportunities Fund and the Western University Department of Paediatrics New Faculty Award.

The opinions, results and conclusions are those of the authors and are independent from the

funding sources. No endorsement by ICES, AMOSO, SSMD, LHRI, CIHR, or the MOHLTC is

intended or should be inferred.

5.8 My Role

In addition to the project conceptualization and design collaboratively with my thesis committee,

I was responsible for submitting applications to all of the ethics boards, with the exception of

CHEO as I did not hold a hospital appointment for this institution. I applied for funding to

AMOSO and the Department of Paediatrics at Western University. I reviewed and approved the

33

data sharing agreements. I held at least 2 in-person meetings with all of the data collectors (at

CHEO, MCH, Ornge in Mississauga, the Hospital for Sick Children, and at CH-LHSC). I

collated and cleaned the data from MCH, HSC, and CH-LHSC so that variables names were

identical, values followed the same scales, and values were logical (e.g. no negative ages). I also

performed quality checks on the data from CH-LHSC and MCH by checking the data at random

against the values in the electronic medical records at each of these two institutions. I submitted

the data from MCH, HSC, and CH-LHSC to ICES. Once all the data was in ICES, I added the

data for paediatrician availability and referral hospital type for all records. I met with the data

management team at ICES on numerous occasions to discuss the Data Creation Plan and cohort

build. I liaised with our administrative officer and ensured that the ICES and CHEO bills were

paid in a timely manner. I performed all of the analyses using SAS 9.4. I wrote the thesis and

built the tables and figures (except figure 1), with regular feedback from my committee. I met

with my committee at least 3 times per year since project initiation in 2012. I presented the

project for my thesis defence proposal, CH-LHSC Research Day, a poster at the World Congress

on Pediatric Intensive and Critical Care (Toronto, 2016), and Critical Care Research Rounds at

the Hospital for Sick Children.

34

6 Analyses

6.1 Frequency and Nature of Inter-Facility Transports

We compared the transport rates per year and used available Ontario population census data to

calculate rates. Patient, transport, and hospital characteristics were represented using descriptive

statistics. Continuous variables were described using mean and standard deviation (SD) or

medians and interquartile ranges (IQRs), dependent on data distribution. For categorical

variables, frequencies and proportions were used. Checks for normalcy were performed. It was

determined that all variables were not normally distributed.

6.2 Patient Outcomes and Resource Utilization

Mortality, LOS, and resource utilization outcomes were computed and are represented using

absolute number and percentage of deaths for mortality, and hours or days for LOS and resource

utilization. A survival analysis with the Kaplan-Meier curve was performed for mortality up to 6

months after the patients’ first transport during the study period. We reported total PICU

resource utilization for the patients with non-missing data. To extrapolate total PICU resource

utilization, we assigned the mean number of days of intervention use calculated from the group

of patients with this data, to each patient that had missing resource utilization data and added

these to patients with completed data.

6.3 Factors Associated with Patient Outcomes and Resource Utilization

Bivariate analyses were performed to describe the association of patient, transport, and hospital

predictors on outcomes (Figure 3). For continuous data, the Mann Whitney test was used because

the data was not normally distributed. For categorical data, the chi-squared test was used. We

excluded variables from our regression analyses where more than 20% of values were missing.

35

Multivariable regression models were built using variables that were statistically significantly

associated with each of the outcomes at p<0.05 on bivariate screening. The outcomes examined

included: ICU and hospital mortality, PICU and hospital LOS, PICU and hospital resource

utilization. Age, sex, and cohort (HBT or Ornge) were forced into the models regardless of

statistical significance. Logistic regression analyses were performed for binary outcomes and

linear regression for continuous outcomes. Type III SS (sums of squares) results were reported.

Assessment of collinearity (using the Spearman test due to non-normally distributed variables)

and outcome frequency informed our regression models. Interaction terms were included when

indicated (i.e. if the correlation coefficient was strong or very strong (>0.6)).

6.4 Sensitivity Analyses

A sensitivity analysis was performed to understand potential differences between hospital-based

transport teams (HBT) and the provincial transport system (Ornge). We identified key

differences between cohorts in patient, hospital, and transport factors (Appendix Table 2) and

thus cohort was included in the regression models regardless of significance on bivariate

screening.

Further sensitivity analyses were performed with imputed variables where there was significant

missingness (>5% but <20%). The lower and upper IQRs, as well as the median were imputed

for each transport dataset and these were included in the regression models when relevant on

bivariate screening. Significant changes were reported where the coefficient changed by more

than 10%.

36

6.5 Validating the DAD CCI codes

Analyses were performed to determine the level of agreement between DAD intervention codes

and a gold standard. ICU resource utilization from the Hospital for Sick Children was used as the

gold standard. These data were documented electronically over the entire study period. The data

were entered in real-time by bedside providers since the electronic record served as the patient’s

bedside chart. The entries were reliable as they were governed by nursing standards and by

hospital policies. Regular quality assurance checks were mandated as well. These results were

compared to CCI data and mechanical ventilation flags for the index admission. Because non-

invasive ventilation can be provided on the general ward at HSC and thus affect the CCI and

mechanical ventilation flag. We hypothesized that there may have been an overestimation by the

CCI data.

6.6 Sample Size Calculation

A sample size of 1534 was calculated to have an 80% power to measure a difference of 1.5% in

mortality compared to the average PICU mortality of 3%, assuming an α of 0.05 and a two-sided

test.vii

One and a half percent was chosen as this would represent a significant change in

mortality (i.e. fifty percent). A sample size of 3826 was calculated to measure a 1% change in

mortality.

6.7 Strengths of the Analytic Plan

Due to the anticipated large number of records, there would be a strong power to detect a

difference in bivariate screening, despite anticipated mortality outcomes of less than 10%. The

assessment of collinearity to deal with the potential relationship between factors, especially

explicitly associated items such as transport times, team type, and distance, was an important

inclusion.

37

We anticipated that missingness of data could be an issue. For example, the transport team may

be less inclined to fill in the data due to fatigue if they have just completed a long transport or

alternatively they may be more inclined to input data to demonstrate their dedicated time. Other

missing values could be more random, such as the diagnosis as there may have been confusion

about what the primary diagnosis was at the time of transport. Various strategies to address

missingness of data were considered, including imputation methods, and deletion. The median

value could be imputed for the variables and assigning these to the missing records was one

option. We found for transport variables, a subgroup of the HBT cohort was more likely than the

other two subgroups to have missing values. It was recognized that any one imputation method

may have limitations. Assigning an imputed value of the whole group to this subgroup may have

biased the variables as there are differences within HBTs. Otherwise, assigning an imputed value

of this subgroup to the missing records within each hospital may still bias this group as the

values may have been missing for particularly long transports. Therefore, we elected to impute

the lower and upper IQRs and the medians for each hospital transport dataset separately and use

these for sensitivity analyses of the regression models as described previously (p. 41).

38

7 Results

7.1 Eligible Transport Episodes

Of the 9426 records from the MCH, CH-LHSC, HSC, and Ornge datasets, 8623 (91.5%) were

successfully linked to the hospital administrative databases. Of the linked records, 6851 (79.5%)

could be linked by deterministic linkage and the remaining 1772 (20.5%) by probabilistic

linkage. Ornge data was linked for 6292 (93.7%) records, HSC for 747 (87.2%) and combined

MCH/CH-LHSC for 1584 (85.3%) records. There were a total of 8,216 transport episodes within

the accrual period. Following sequential exclusions, we identified 4,074 eligible transport

episodes (Figure 4). Of the 346 CHEO records which were used solely for ICU interventions,

338 (97.7%) were successfully assigned an IKN.

Over the study period, from the 3 HBT datasets we identified 2,536 transports. After excluding

those without a valid IKN, sex, and age values, and age >18 years, there remained 2,524 (99.5%)

records. After excluding those without a DAD record indicating a PICU admission +/- 1 day of

transport, there were 2,359 (93.0%) remaining. Finally, after excluding those without a DAD

record corresponding to the correct institution, 2,355 (92.9%) remained. For the Ornge dataset,

17 transports were excluded after those without a valid IKN, sex, and age values, and age >18

years were excluded; however 3,332 (58.8%) transports were excluded after those without a

DAD record indicating a PICU admission +/- 1 day of transport were removed, indicating that

the majority of Ornge paediatric transports were not for children who were transferred to a PICU.

A further 414 were excluded when patients were seen in an emergency department at the same

hospital as the PICU, prior to their PICU admission. Finally, the HBT and Ornge cohort were

combined and a further 198 transports were excluded due to duplications (Figure 4).

39

Figure 4. Flow Diagram

7.2 Data Completeness

Mortality, ICU and Hospital LOS outcomes were available for all transports. Of the 16

descriptive variables, 2 had significant missingness (>20%) and 2 variables had moderate

missingness (>5% and <20%) for which imputation methodology was applied in the sensitivity

analyses (Appendix Table 3). LHSC had the most missingness, with 54% of some of the

transport time variables missing. There was significant missingness for ICU intervention use

mainly because we did not have Data Sharing Agreements between Ornge and each of the 4

40

PICUs. This meant that we could not retroactively identify patients transported by Ornge to each

PICU once all patients were de-identified in ICES. Thus, we were unable to obtain their medical

records. Although CHEO did not have a HBT we did obtain ICU resource utilization data from

the CHEO PICU electronic database by identifying all patients who were transported in.

However, even though 264 patients were transported by Ornge to CHEO, only 194 could be

matched with the hospital records from CHEO. The remaining CHEO patients transported by

Ornge may not have been directly admitted to the PICU. After exclusions of patients with no

intervention data, ICU intervention use was analysed as an outcome for a total of 2549 (62.6%)

transport records.

7.3 Cohort Description

We evaluated 4074 transport episodes. Patients had a median (IQR) age of 1.6 (0.1, 8.3) years at

the time of transport, and had a wide range of diagnoses. Respiratory diagnoses were the most

common (n=1483, 36.9%). More than half had undergone nighttime transfers (n=2,531, 62.2%),

most originated from a community hospital (n=3181, 78.4%) and most originated from a hospital

where a paediatrician was on staff (n=3,269, 80.5%) (Table 2). Three hundred and forty seven

patients had more than one transport in the study period, comprising 8.5% of the transport

episodes.

There were 48,006 episodes of health care utilization in the 6 months prior to transport for all

transport episodes. Younger children had more health care use in the 6 months prior to transport

compared to older children, adjusted for age (Figure 5). The number of emergency department

visits was 10,398, same-day surgeries was 326, and hospital admission days was 6,265.

Most transports (n=1244, 30.5%) were completed by land. The mode of transport had significant

missingness (n= 1867 (45.8%)) (Appendix Table 3). The majority of transports occurred in the

41

fall and winter. The median stabilization time was almost twice as long as the transport time. The

median (IQR) transport distance was 66.2 (IQR: 32.4, 183.6) kilometers.

Hospital-based teams (HBT) completed 2355 (57.8%) transports and Ornge completed 1719

(42.2%). The transport episodes differed significantly based on the type of transport teams

(Ornge or HBT) (Appendix Figure 2). In particular, the HBTs transported younger patients with

more frequent diagnoses of respiratory illness and less trauma, had more prior health care

contact, travelled longer distances and were more frequently transported at nighttime (all

p<0.0001).

42

Table 2. Cohort Description

Characteristics

Categorical Variables

N (%)

Total= 4074

PICU Mortality

Yes

N(%)

PICU Mortality

No

N (%)

All transport episodes 4074 233 (5.7) 3841 (94.3)

Sex

Male

Female

2375 (58.3)

1700 (41.7)

143 (6.0)

90 (5.3)

2232 (94.0)

1610 (94.7)

Diagnosis*

Respiratory

Neurologic

Cardiac

Other

Metabolic

Sepsis

Trauma

Toxic Ingestion

Missing

1486 (36.9)

914 (22.7)

544(13.5)

297 (7.4)

271 (6.7)

235 (5.8)

196 (4.9)

85 (2.1)

47 (1.2)

47 (3.2)

44 (4.8)

69 (12.7)

280 (5.7)

<5

23 (9.8)

26 (13.3)

<5

1439 (96.8)

870 (95.2)

475 (87.3)

17 (94.3)

--

212 (90.2)

170 (86.7)

--

Cohort

Hospital Based Transport Team

Ornge

2355 (57.8)

1719 (42.2)

1236(5.5)

104 (6.0)

2226 (94.5)

1616 (94.0)

Time of day

Day

Night

1543 (37.9)

2532 (62.1)

95 (6.2)

138 (5.5)

1448 (93.8)

2394 (94.6)

Season

Winter

Spring

Summer

Fall

1122 (27.5)

982 (24.1)

892 (21.9)

1079 (26.5)

63 (5.6)

56 (5.7)

54 (6.1)

60 (5.6)

1059 (94.4)

926 (94.3)

838 (93.9)

1019 (94.4)

Mode

Land

Rotor

Fixed wing

Missing

1244 (56.3)

669 (30.3)

295 (13.4)

1867 (45.8)

67 (5.4)

47 (7.0)

20 (6.8)

1177 (94.6)

622 (93.0)

275 (93.2)

Referral Hospital Type

Community

Teaching

Small

Nursing station

Missing

3181 (78.4)

517 (12.7)

351 (8.7)

10 (0.25)

16 (0.4)

172 (5.4)

31 (6.0)

28 (8.0)

<5

3009 (94.6)

486 (94.0)

323 (92.0)

--

Paediatrician available at the

referral hospital*

Yes

No

Missing

3269 (80.5)

790 (19.5)

16 (0.4)

175 (5.4)

56 (7.1)

3094 (94.7)

734 (92.9)

43

Admitting PICU*

CH-LHSC

MCH

HSC

CHEO

1468 (36.0)

610 (15.0)

1728 (42.4)

269 (6.6)

77 (5.25)

25 (4.1)

108 (6.3)

23 (8.6)

1391 (94.8)

585 (94.9)

1620 (93.7)

246 (91.5)

Continuous Variables

Median (IQR) PICU Mortality

Yes

Mean (SD)

PICU Mortality

No

Mean (SD)

Age (years) 1.6 (0.1, 8.3) 4.8 (6.1) 4.6 (5.9)

Prior health care contact∫*

0.005 (0, 0.1) 0.08 (0.2) 0.1 (0.1)

Distance transported (Km)*

Missing n (%)

66.2 (32.4, 183.6)

45 (1.1)

179.6 (313.7) 159.6 (258.2)

Time to team contact (hours)

Missing n (%)

1.1 (0.7, 1.8)

1396 (34.3)

1.5 (1.3) 1.5 (1.4)

Stabilization time (hours)

Missing n (%)

1.7 (0.8, 1.8)

738 (18.1)

1.5 (1.4) 1.4 (1.4)

Transport time (hours)

Missing n (%)

0.9 (0.7, 1.4)

732 (18)

1.2 (1.1) 1.2 (1.1)

Total time of team contact

(hours)

2.2 (1.6, 3.2) 2.7 (2.4) 2.5 (2.5)

* Indicates p<0.05 on bivariate screening of characteristic’s association with primary outcome ∫ This age weighted variable is defined as the sum in total days in the 6 months prior to the index

visit divided by age in days for patients less than 6 months and by 6 months for those older than

6 months at the time of the index event.

N.b. LHIN of referral hospital not displayed due to large number of LHINs (n=14)

There was 100% data completeness unless otherwise indicated by “Missing n(%)”

44

Figure 5. Health Care Use in the 6 Months prior to Index Event, Adjusted for Age.

This age-weighted variable was defined as the sum in total days in the 6 months prior to the

index visit divided by age in days for patients less than 6 months and by 6 months for those older

than 6 months at the time of the index event. Box represents median (middle line) and inter-

quartile range, whiskers represent 1st and 99

th%.

7.4 Temporal Trends

The number of transports increased over the study period (Figure 6). Ontario population census

data were available for 2006 and 2011 and for 0-14 years.viii

The populations of Ontario children

aged 0-14 years in 2006 and 2011 were 2,210,800 and 2,180,775 respectively.ix

The numbers of

transports were 340 and 508 in 2006 and 2011 respectively. Thus, the rate of transports increased

over the study period for children age 0 to 14 years by 53%, from 15/100,000 to 23/100,000 from

2006 to 2011, respectively.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% of Prior 6

Months With

Health Care

Use

Age Group

45

Other notable trends include time to team contact which was reduced by 50% (median 1.5 to 1

hour) and stabilization time by 25% (median 1.6 to 1.2 hours) between 2004 and 2012. The

median distance had decreased over the study period by 47 kilometres (or 43%).

Figure 6. Temporal Trends

7.5 Outcomes

7.5.1 Mortality

Two hundred and thirty three (5.7%) patients died during their PICU admission following

transport, 104 (2.6%) died within 24 hours of admission to the PICU and 311 (8.3%) died in the

6 months following the index transport. The Kaplan-Meier plot of 6 month survival after PICU

arrival is shown in Figure 7.

152

286

369

419

542 564

538 564

641

0

100

200

300

400

500

600

700

2004 2005 2006 2007 2008 2009 2010 2011 2012

Tran

spo

rts

Year

46

Time (Days)

Figure 7. Six Month Survival Following First Transport

7.5.1.1 Factors Associated with Mortality

On bivariate screening, the factors significantly associated with ICU mortality were: prior health

care contact, diagnosis, availability of a paediatrician at the referring hospital and the admitting

PICU (Table 2). Early ICU mortality was significantly associated with the same factors plus

stabilization time and transport team type. Bivariate screening for associations between 6 month

mortality after the first transport identified diagnosis and admitting PICU as the significantly

associated factors.

Surv

ival

47

Regression models for each mortality outcome identified 3 factors independently associated with

ICU mortality, 4 factors were associated with early ICU mortality, and only 1 factor associated

with 6 month mortality after (index) transport. There were no strong correlations between

included variables.

PICU mortality was independently associated with diagnosis, healthcare contact, and availability

of a paediatrician. Specifically, PICU mortality was greater in patients with a ‘Cardiac’ or

‘Trauma’ diagnosis, and a metabolic diagnosis (e.g. diabetic ketoacidosis) was protective

(p<0.0001). Greater healthcare contact was independently associated with decreased ICU

mortality, as was the availability of a paediatrician at the referral hospital. The Hosmer-

Lemeshow Goodness-of-fit test was 0.5.x

Early PICU mortality was independently associated with prior healthcare contact, diagnosis,

availability of a paediatrician at the referral hospital and transport team. Trauma and cardiac

diagnoses were associated with a higher early mortality. A respiratory diagnosis was

independently associated with lower mortality. Prior health care contact was associated with a

lower early mortality. The Hosmer-Lemeshow Goodness-of-fit test was 0.9.

Only diagnosis was independently associated with 6 month mortality (Table 3). The Hosmer-

Lemeshow Goodness-of-fit test was 0.3.

48

Table 3. Multivariable Regression Models for Mortality

Variable PICU Mortality Early Mortality 6 Month Mortality

OR (95th

CI) P-value OR (95th

CI) P-value OR (95th

CI) P-value

Age 1.0 (0.97, 1.0) 0.5 1.0 (1.0, 1.0) 0.9 1.0 (0.98, 1.0) 1.0

Sex 0.9 (0.7, 1.2) 0.6 1.0 (0.7, 1.6) 0.9 0.96 (0.8, 1.2) 0.8

Cohort 1.0 (0.6, 1.5) 0.9 0.8 (0.6, 1.2) 0.06 1.1 (0.8, 1.6) 0.2

Diagnosis*

Cardiac

Trauma

Sepsis

Neurologic

Respiratory

Metabolic

2.6 (1.5, 4.6)

2.2 (1.1, 4.4)

2.0 (1.0, 3.9)

0.8 (0.5, 1.5)

0.6 (0.3, 1.0)

0.3 (0.08, 0.8)

<0.0001

2.5 (0.9, 6.7)

3.0 (1.0, 8.7)

1.5 (0.4, 5.2)

0.8 (0.3, 2.2)

0.2 (0.07, 0.7)

0.1 (0.02, 1.2)

<0.0001

2.5 (1.5, 4.2)

2.0 (1.1, 3.6)

1.6 (0.9, 2.9)

0.8 (0.5, 1.3)

0.7 (0.4, 1.1)

0.2 (0.06, 0.5)

<0.0001

Prior Health

Care Use 0.3 (0.2, 0.6) 0.002 0.06 (0.006,

0.5)

0.01 ----- -----

Paediatric

expertise 0.7 (0.5, 1.0) 0.05 0.6 (0.4, 1.0) 0.05 ----- -----

Stabilization

time

----- ----- 1.0 (0.8, 1.4) 0.8 ----- -----

Transport

team

----- ----- 1.6 (1.0, 2.5) 0.05 ----- -----

Admitting

PICU∫

1

2

3

0.7 (0.4, 1.4)

0.5 (0.3, 0.9)

0.7 (0.4, 1.1)

0.2

1.6 (0.4,5.6)

0.5 (0.2, 1.2)

0.7(0.4, 1.5)

0.2

0.7(0.4, 1.1)

0.5 (0.3, 0.8)

0.8 (0.5, 1.3)

0.3

Independent predictors in bold

* Reference is “Other Diagnosis”

∫Reference is CHEO; 1=CH-LHSC, 2=MCH, 3=HSC

N.b. Age, sex, and cohort (HBT vs Ornge) were forced into all models.

7.5.2 Length of Stay

The median (IQR) PICU LOS was 2 (1, 5) and hospital LOS was 7 (3, 14) days.

7.5.2.1 Factors Associated with Length of Stay

On bivariate screening, PICU LOS was associated with 7 variables: prior health care contact,

diagnosis, cohort, time of day of the transport, the LHIN of the referring hospital, the type of

referral hospital, and availability of a paediatrician at the referral hospital. On bivariate

49

screening, hospital LOS was associated with 11 variables: prior health care contact, diagnosis,

cohort, time of day of the transport, mode of transport, the LHIN of the referring hospital, the

type of referral hospital and availability of a paediatrician, the admitting PICU, and the distance

between the referral hospital and the admitting PICU. An interaction terms was included for

LHIN and admitting PICU as well as cohort and admitting PICU for the regression models.

PICU LOS was independently associated with 3 factors on regression analysis: diagnosis, prior

health care contact, and referral hospital type (Table 4). Cardiac, and respiratory, followed by

trauma diagnoses, and teaching hospital status, followed by a small hospital and nursing station

were associated with a longer ICU LOS.

Diagnosis, prior health care contact, time of day, LHIN of the referring hospital, and referral

hospital type were independently associated with hospital LOS on regression modeling (Table

4). “Other”, followed by trauma and cardiac diagnoses were associated with longer hospital

LOS. A daytime admission was associated with a 3 day longer hospital admission. Teaching

hospital status was associated with a 7 day longer hospital stay than a nursing station.

7.5.2.2 Resource Utilization

The most common ICU intervention was invasive mechanical ventilation (n=711, 27.9%),

followed by inotrope medication (n=507, 19.8%) (Table 5). Just over half (n=1364, 53.5%) of

patients did not receive any ICU intervention. Extrapolating this to total PICU resource

utilization with all records contributing data, the total based on a mean of 3.2 days per record

would be 13,037 days of ICU intervention use or 35.7 PICU-years.

50

Table 4. Multivariable Regression Models for LOS and Interventions outcomes

PICU LOS Hospital LOS Interventions

Days∫ P-value Days P-value Days† P-value

Age - 0.6 - 0.6 0.3

Sex

Female

Male

5

5

0.3

19

18

0.2

5

4

0.5

Cohort

Ornge Hospital Team

5

5

0.7

19

18

0.3 ----- -----

Diagnosis

Cardiac

Trauma

Sepsis

Neurologic

Respiratory

Metabolic

Other

7

6

5

5

7

4

5

<0.0001

19

23

18

17

18

14

25

<0.0001

7

5

5

3

5

3

4

<0.0001

Prior Health Care

Use - <0.0001 - <0.0001 - 0.002

Time of Day of

Admission

Day Night

6

5

0.1

20

17

0.0004

5

4

0.002

Paediatrician

available Yes

No

5

5

0.2

19

18

0.4

4

4

0.9

LHIN of Referral

Hospital

- 1.0

- 0.01 - 1.0

Type of Referral

Hospital

Teaching

Community

Small

NSα

8

4

4

4

0.001

27

13

14

20

<0.0001

5

4

3

5

0.2

Stabilization time ----- ----- ----- ----- - 0.07

Transport time ----- ----- ----- ----- - 0.6

Total time with

transport team

----- ----- ----- ----- - 0.9

Distance to the

PICU

----- ----- - 0.007 - 0.9

Admitting PICU§

1

2

3

----- -----

21

18

17

0.2

1

4

7

<0.0001

51

4 18 4

Independent predictors in bold

∫ Mean

§Reference is CHEO; 1=CH-LHSC, 2=MCH, 3=HSC, 4=CHEO α

NS: nursing station

†: whole or part days of intervention use in the PICU

Table 5. ICU Intervention Use

Intervention Mean (SD) N (%) with at least

1 day

Total number of

days of use

Non-invasive mechanical

ventilation

0.5 (2) 386 (15.1) 1222

Invasive mechanical

ventilation

1.7 (5.5) 711 (27.9) 4240

Inotropes 0.9 (3.2) 507 (19.8) 2401

CRRT 0.05 (0.8) 20 (0.8) 131

ECMO 0.02 (0.4) 6 (0.2) 43

Sum of all interventions 3.2 (8.5) 1185 (46.4) 8037

Mean(SD) denotes mean number of whole or part days of intervention use

CCRT = continuous renal replacement therapy

ECMO = extracorporeal membrane oxygenation

7.5.2.3 Factors Associated with Resource Utilization

On bivariate screening, age, diagnosis, prior health care contact, time of day of the transport,

availability of a paediatrician at the referral hospital, referral hospital type and LHIN,

stabilization time, transport time, distance to the PICU, admitting PICU were all significantly

associated with resource utilization. Daytime transport, teaching and community referral

hospitals, availability of a paediatrician, and the admitting PICU being either HSC or CHEO

were all associated with more intervention use. An interaction term was included for distance

and transport time as well as stabilization time and total time with the transport team.

Diagnosis, prior health care contact, time of day and admitting PICU were independently

associated with PICU intervention use (Table 4). The leading diagnoses for intervention use were

52

cardiac, respiratory and sepsis. A daytime transfer was associated with an additional day of PICU

interventions. Patients transferred to HSC had 2 days more of PICU intervention use compared

to the next highest use centre (CHEO).

7.6 Sensitivity Analyses

Sensitivity analyses for the mortality and LOS models did not change with imputations of the

median, lower or upper bounds of the interquartile range. Specifically the p-values remained

either <0.05 or >0.05. The coefficient variable changed for the imputed models by less than 1%.

For PICU interventions, the results did not change with imputations of the median, lower and

upper IQRs with the exception of stabilization time which became significant for all three

imputation models (p=0.03). The coefficient changed by 0.1%.

7.7 Validation of the DAD CCI Codes

The DAD underestimated the number of patients who required mechanical ventilation (despite

not specifying location) using the mechanical ventilation flag and the non-invasive mechanical

ventilation intervention codes. The mechanical ventilation flag (both < and >= 96 hours) picked

up 341 of the patients who were transferred to the PICU at The Hospital for Sick Children. The

gold standard electronic data record showed that 470 unique patients had mechanical ventilation,

thus the DAD was only 72.6% sensitive. When we used the IGZ31 codes, we found that DAD

picked up only 60 of 87 (69% sensitive) of the non-invasive mechanical ventilation episodes and

picked up 499 of invasive ventilation episodes, which is 12.9% more than the gold standard. The

latter incongruity likely reflects those who required intubation outside of the PICU (e.g. the

operating room). The DAD also underestimated those who received CRRT (sensitivity: 5%),

and overestimated the number of patients who required ECMO (by 60%). Thus, based on this

53

preliminary validation work, the DAD should not be used as a reliable source of ICU

intervention data in paediatric critical care in Ontario.

54

8 Discussion

In critically ill children transported to one of four PICUs in Ontario, we evaluated 4074 transport

episodes between 2004 and 2012 for which the PICU mortality was 5.7%.

8.1 Frequency of Inter-facility Transports

The rate of transports for critically ill children in Ontario increased over the study period. This

may be due to increased awareness about the benefits of using a specialized team as studies

demonstrating this were published during the study period.7, 26

This may also be due to improved

access to services. In 2010, Ornge introduced a dedicated paediatric team which was responsible

for 261 (22%) of the transports in 2011 and 2012. This may have resulted in improved quality of

service. Furthermore, the service may have improved efficiency as the time to team contact was

reduced by 50% and stabilization time by 25% between 2004 and 2012. A more efficient system

with better skilled clinicians is more likely to be embraced by providers in referral centres,

making utilization of the specialized transport system more common. On the other hand, we

found that median distance had decreased over the study period. So rather than a more efficient

system, perhaps the median time to arrival simply reflects that patients were originating in

centers closer to the tertiary care center. This is plausible as the majority of transfers were to

HSC (42.4%) which services a large number of referral centers within the Greater Toronto Area,

which is expanding.

The reduced stabilization time could also be explained by a lower severity of illness rather than

improved efficiency of the team and the transport process. A lower severity of illness translates

into a lower threshold by referring physicians to transfer to a higher level of care. This could also

explain the increase in the rate of transports over the study period. Paediatric and family

55

medicine resident training programs across the province have almost doubled in size between

2004 and 2014xi

which is overpacing the rate of paediatric population growth. Perhaps this has

resulted in a diluted training experience and thus less skill and confidence in managing sick

paediatric patients, leading to clinicians who are more likely to transfer because they are

uncomfortable. This was highlighted in a study evaluating referrals to CH-LHSC’s emergency

department that showed the majority of transfers from a regional emergency department to the

Children’s Hospital were for a second opinion rather than definitive specialized care.

Furthermore, we found that physician specialty did not affect likelihood to pursue transfer.xii

All

in all, this area of research requires further evaluation.

Finally, the increased rate of transports could also mean an increase in the number of critically ill

patients presenting to referral centres. Although this has not been evaluated, it has been discussed

in the literature that though there has been improved accident prevention (e.g. helmet laws, water

safety awareness, Sudden Infant Death Syndrome prevention campaigns) there has been also

fluctuating immunization rates, an increase in survivors from critical illness and more medically

fragile children in the community which could all translate into more critically ill children

presenting to referral centres.50

8.2 Nature of Inter-facility Transports

The age of transferred critically ill children was low with 25% of the study population 1 month

or less, and 42.4% aged less than one year. This finding is similar to other studies.9, 26

Males

accounted for more than half (58%) of the study population which is also similar to other

studies.10, 26

Likewise, respiratory illness was the most common primary problem.7-9, 26, 45

Days of

health care use in the 6 months prior to transport was impressive with the total number of days

for the study population equivalent to 131.5 years of hospital care. Interestingly, patients less

56

than 6 months of age at the time of transport used a median of 4 more days of health care

compared to patients older than 6 months despite having less opportunity to do so because of

their age (Appendix Figure 1). Of these four additional days, one to two of them could be

explained by the typical 24 to 48 hour admission following birth. Nonetheless, the health care

utilization is high in this young population, likely reflecting the vulnerability of this population.

There were more transports at night (1900-0700). The opposite was demonstrated in the U.K.

where 62.1% occurred between 0700 and 1900.9 A study in the U.S. showed that critically ill

paediatric patients present to the emergency department more uniformly throughout the day and

night compared to non-critically ill children who preferentially present in the evening hours.61

Our result could suggest a lower threshold for transfer at night. This hypothesis was supported by

the finding that nighttime transfers were associated with less PICU intervention use and a shorter

hospital stay, when adjusting for other significant factors from the bivariate analyses, including

diagnosis.

Not surprisingly, seasonal effects were associated with transport volumes. Transports which

occurred in the fall and winter accounted for more than half of all transports, likely due to the

increase in respiratory infections during these seasons. Most transfers originated in community

hospitals (78.4%) and paediatrician availability was high (80.5%). More than half of transports

were completed by land ambulance, however, there was a significant amount of missing data

(45.8%), thus this variable was not included in multivariable regression models, regardless of

significance on bivariate analyses. Half of transport distances were between 32 and 183

kilometers, however, 10% were for distances greater than 350km. The median distance from the

PICU in this study (66.2 km) is twice the distance reported in a study of one of the largest

57

transport networks in the world (U.K.).26

The total distance traveled during the study period was

647,339.6 kilometers, which is equivalent to 16 times around the Earth.

We found that stabilization time was almost double transport time (1.7 vs 0.9 hours). Our

transport times were similar to a region in the U.K. despite the difference in distance.9 This may

reflect the impact that traffic may have on prolonging transport time for shorter distances, and

conversely, highlight that air transport reduces transport time for greater distances. The total

median (IQR) time that the transport team was away from the admitting PICU was 3.3 (2.3 - 5)

hours. During the study period, the total time away from the PICU without accounting for

missing data was 12,450.5 hours which is equal to 518.8 days, or 16% of the total study period.

For most HBTs and for Ornge, a transport team must be available at all times, however, most of

the team’s shift is not spent caring for a patient, even if the 16% underestimates the true value. In

times of fiscal restraint, this is an inefficient use of skills and expertise. The team members have

a high level of expertise but also a broad skill set that could serve hospital in-patients well, e.g.

for intramural transports, relieving nurses for breaks, covering for short procedures. Hospital

administrators, must be creative in how to optimize the time the team is not occupied with a

transport, being mindful of their need to ‘drop and go’ and the potential for accruing significant

overtime if sent out on a transport late in the shift.

8.3 Patient Outcomes

8.3.1 Primary Mortality Outcome

PICU mortality of 5.7% is higher than the often quoted PICU mortality rate of 3% for all patients

admitted to the PICU.62, 63

This almost doubling of mortality rate can likely be explained by the

more urgent nature of transport admissions compared to all-comers to the PICU, which includes

58

elective admissions, e.g. following elective surgery. Evidence supports a higher mortality for

urgent PICU admissions.10, 64

PICU mortality following inter-facility transport in our study was lower (5.7%) compared to the

U.K (8%)26

and higher compared to the U.S (4%).45

These differences align well with the

respective ICU intervention use, which may be a surrogate for severity of illness. For example,

vasoactive use in the U.K. for transported children was 32%, for our study it was 20% and for

the U.S. (in the first 24 hours) it was 8%. However, in our study, intervention use in respiratory

patients was significantly higher than all but cardiac and trauma diagnoses, yet, a respiratory

diagnosis had the second lowest OR for PICU mortality. The point is that intervention use as a

marker for severity of illness has its limitations, perhaps similar to scoring tools. For example,

mechanical ventilation has many indications, including many that are relatively benign, such as

for status epilepticus, which resolves in the vast majority of patients within a couple of hours,

making mechanical ventilation no longer necessary. Another example is bronchiolitis, where

non-invasive ventilation is often required, but with excellent outcomes.

We found PICU mortality was independently associated with diagnosis, prior health care use and

availability of a paediatrician at the referral hospital. We found that cardiac and trauma

diagnoses had the highest risk for death and a metabolic diagnosis was associated with a lower

mortality. Cardiac and trauma diagnoses also had the highest risk for early PICU mortality and 6

month mortality. This finding is not surprising as the treatment options can be limited in the

severe spectrum of these diagnostic categories, such as severe traumatic brain injury and

cardiomyopathies. In the U.S., cardiac and neuromuscular conditions had the highest risk of

PICU mortality in all admissions to the PICU.65

High-risk diagnoses (not further defined) were

associated with an OR of 2.5 for PICU mortality in the U.K.26

59

Prior health care contact (as a continuous variable) in the 6 months before transport appeared to

be associated with both PICU mortality and early PICU mortality. Perhaps this is indicative of

better management of chronic conditions, earlier health care contact in the course of an illness,

more familiarity with the patient or the disease by the health care teams, and/or earlier referrals

by the health care provider. A study of 54 U.S. PICUs found that children with chronic

conditions that are not complex (i.e. did not involve more than one organ system or was not

severe enough to require specialty care and hospitalization) had a lower risk of PICU mortality

compared to children with no chronic condition.65

Availability of a paediatrician was associated with both lower PICU mortality and lower early

PICU mortality. This may be due to better pre-transport management from increased familiarity

with a condition or patient, better hospital resources for managing a sick paediatric patient

(equipment, paediatric trained nursing, respiratory therapy), and/or simply more familiarity with

the referral processes. Further evaluation may help isolate specific factors. This is in contrast to a

U.S. study that did not show a difference in mortality for patients transferred from hospitals with

paediatric in-patient services compared to community hospitals that lacked a similar service.15

However, this finding is consistent with other studies in terms of other important outcomes. The

positive effect of specialization in paediatrics was shown in the population of all young children

who visited emergency departments in Ontario for respiratory complaints. This study

demonstrated that hospitals with front-line paediatrician availability in the ED had higher rates of

paediatric guideline adherence (specifically radiograph ordering), and similarly, hospitals with

paediatrician availability (not necessarily frontline in the ED) also had improved guideline

adherence.66

A U.S. study showed similar results; pediatric emergency physicians ordered less

tests than general emergency physicians, however, the number of physicians included was

60

small.67

Similarly, in Taiwan, general emergency physicians ordered more tests, kept paediatric

patients in the ED for longer, and admitted more paediatric patients who then had a shorter

hospital LOS compared to paediatricians for similar patients.68

Our findings suggesting that health care contact and availability of a paediatrician are protective

even when controlling for patient and transport factors is very interesting. This speaks to the

potential value of maintaining health care accessibility, particularly for populations living in

smaller or more remote regions of the province (which are more vulnerable to fiscal pressures).

Ontario had the third lowest concentration of academic paediatricians per child (11.7/100,000

population) compared to the other provinces between 2003 and 2006.69

An academic

paediatrician is one that is affiliated with a medical school, thus this number does not include

most paediatricians working in community hospitals. Currently, there are 1779 registered

paediatricians in Ontario. Based on the only available census data (from 2011) of approximately

2,200,000 children between the ages of 0-14 years, this represents 81 paediatricians/100,000

population. This ratio of 1237 children to one paediatrician is similar to the ratio of children to

general paediatricians in the U.S. in the early 1990’s, however, drawing conclusions from this

comparison has its limitations due to significant health service model differences (e.g. a

pediatrician in the U.S. is more of a generalist than in Canada). There are no studies or

recommendations on ideal ratios, but with the accumulating evidence to support access to a

paediatrician (including this study), we must advocate for sustainable residency training

programs, fair distribution of paediatricians throughout the province, and hospital paediatrician

consulting models. This is possible, even in smaller hospitals, with the use of telemedicine. For

example, a paediatrician can be on-call to several smaller hospitals at once via

videoconferencing. The technology exists such that real-time virtual consultations are currently

61

possible using the Ontario Telemedicine Network. This is already being implemented for smaller

adult ICUs in Northern Ontario.

8.3.2 Secondary Mortality Outcomes

The distribution of timing of mortality is interesting and has not previously been described. Of

the transported critically ill children who died within 6 months of transport, approximately one

third died within 24 hours of transport, an additional one third died during the remainder of their

PICU admission, and a final third died sometime in the remainder of the 6 months since the

transport (Appendix Figure 1).

The 24 hour mortality risk of 2.6% is similar to a large multicenter study performed in the U.K.

where 24 hour mortality was 2.4%.9 Almost half of children who died in the PICU died within

24 hours of their PICU admission following transport. Despite this, only one of the transport

factors (transport team) was independently associated with early mortality. Because this variable

included both HBTs and Ornge teams (primary, advanced, critical, and paediatric), one cannot

interpret the OR of 1.6 in any meaningful way. A higher mortality associated with certain teams

is less likely to be explained by a causal relationship but rather by indication bias. When Ornge is

responsible for the transport, higher expertise will be assigned to more critically ill patients, as

far as it is possible. We know that for Ornge transports, more critical events (which includes vital

sign instability) occurred in patients being transported by the teams with greater expertise.37

It is

thus not surprising that different teams transport more or less sick patients with higher or lower

risks of mortality. Though we did not have severity of illness scores available, we attempted to

control for other confounding variables that could be associated with mortality, such as

diagnosis, age, and prior health care contact. It is reassuring that none of the other transport

factors, such as time intervals or distance, were associated with early mortality. This suggests

62

that the transport process is working well, especially since the converse was found in adults who

were urgently transported in Ontario. Every increase in transport time by 10 minutes was

associated with an increased risk for a critical event by 2%.36

There are no transport studies to compare our study’s 6 month mortality rate of 8.3% to. The

only significant independent predictor for 6 month mortality was diagnosis. In fact, distance,

stabilization and transport times, time of day, admitting PICU, referral hospital type and cohort

were not independent risk factors for any mortality outcomes. Distance was also not a factor in

other paediatric transport studies.26, 49, 70

The stabilization time did not result in increased early

PICU mortality in the smaller U.K study either.9 This may be due to important factors that

influence stabilization time both positively and negatively. There are critical conditions where a

rapid turnover is indicated (e.g. for certain neurosurgical conditions) and others that are just as

life threatening but that benefit from several interventions prior to transport in order to optimize

the condition prior to transport (e.g. severe respiratory failure). It is reassuring that time of day of

transfer was not associated with mortality. This trend has been demonstrated in other studies.

Though not specific to a transported population, in a large multicentre U.S. study, there was no

increased risk of mortality for nighttime admissions.62

In an older (2004) study from the U.S.,

adjusted 48 hour mortality in all PICU patients was increased for nighttime admissions.64

Reassuringly, the admitting PICU was not an independent factor for any of the mortality

outcomes, this despite HSC and CHEO managing cardiac-surgical patients with a higher risk of

mortality. It was interesting to find that cohort was not independently associated with mortality

(whereas it was with LOS) even though we discovered that the HBTs transported a significantly

different and complementary population compared to the provincial transport system. The two

systems are organized differently, have different training, work-hours, and even professions

63

(specifically, the HBTs have nurses and respiratory therapists, whereas the provincial system

functions mainly with paramedics).

8.4 Resource Utilization

PICU length of stay (median 2 days) in this study was shorter than both the large U.S. and U.K.

studies (4 and 3 days, respectively)26, 45

but longer than 2 studies of 70 and 99 U.S. PICUs62, 65

(all-comers, not just transported patients). Three of these studies had not modeled LOS as an

outcome. Edwards et al. reported that the presence of a complex condition was associated with

an increased length of stay, whereas one chronic (non-complex) condition was associated with a

shorter LOS.65

Length of stay is dependent on many factors, including bed capacity both in the

PICU and hospital, severity of illness, resources and training of ward staff, and repatriation

models. Given the complexity of patient movement out of the PICU, it would be presumptuous

to draw conclusions from this finding; rather, it can be used as baseline for future studies of

similar systems.

Our study is the first study to look at factors associated with PICU and hospital LOS in

transported critically ill children. The factors independently associated with PICU LOS in our

study were diagnosis, prior health care use, and type of referral hospital. In addition to these,

time of day, LHIN of the referring hospital, and distance were independently significant for

hospital LOS.

It is unclear why time of day is associated with LOS given that we did not find a mortality

difference between day and night admissions. We discussed earlier that a lower threshold for

transfer may exist at night as there may be an exaggerated lack of resources available to

community hospitals at night. A lower threshold may mean less complex patients get transferred

64

at night resulting in a shorter hospital LOS. Alternatively, these patients may have arrived after

midnight which may have led to biased estimates of LOS given our use of calendar days.

It makes sense that a referral hospital that is a teaching hospital was associated with higher LOS

as these institutions were likely transferring patients with more complex and/or chronic

conditions, or those that were sicker. Similarly, the association of hospital LOS with LHIN is not

surprising, since discharging a paediatric patient to a more remote location may require more

time for clinical stability, and to organize community resources or transportation. It is interesting

that distance is independently associated with hospital LOS, when controlling for LHIN, as the

same arguments can be used to explain this finding. It suggests that there may be differences in

resources or repatriation planning by LHIN. Further evaluation is required to understand this

better.

PICU intervention use by transported patients is variable between the U.S.,45

the U.K.26

and our

study with higher rates in the U.K. and unclear rates in the U.S. (reported for the first 24 hours

and mechanical ventilation not defined). The independent factors associated with PICU

interventions use were diagnosis, prior health care contact, time of day and admitting PICU. In

view of the interventions that were measured, it is not surprising that the three diagnoses

(cardiac, respiratory, and sepsis) that required the most interventions were the ones that typically

require inotropes and mechanical ventilation. HSC and CHEO are the two centres with the

highest intervention use which is consistent with the fact that these these two centres are the

provincial cardiac centres. The fact that these two factors (diagnosis and admitting PICU) are

independent of each other suggests that there are other unmeasured factors involved. These could

include severity of illness, or lower threshold for ventilation support. The type of transport

65

service was a significant predictor of ICU intervention use in the U.K..26

Our study did not

demonstrate this.

It is interesting that just over half of patients did not require any PICU interventions. This

highlights two important points. First, many patients are admitted to the PICU for more reasons

than the studied ICU interventions. These reasons include intensive monitoring and/or nursing

care (such as frequent suctioning). Second, a number of patients may have been admitted to the

PICU that in retrospect may have been unnecessary. Telephone communication between the

referring and accepting physicians has its limitations. For example, there can be tension in

telephone consultations from a fragmented clinical process due to missing visual cues, gut

feelings and incomplete or inaccurate information.71

In a large review of neurosurgical

consultations there is often inadequate recording of advice provided over the phone, which often

prompts re-initiating contact with the consulting team.72

Sound understanding of the patient’s

clinical state is a pre-requisite to the provision of informed recommendations, including whether

or not the patient should be admitted to a PICU. HSC has been able to use the emergency

department to triage children in whom it is not clear whether or not they will need PICU. Not

every hospital has this option, making the threshold for admission lower elsewhere, to be on the

safe side. These results help to support the use of tele-medicine, specifically videoconferencing

with the referring physician and the patient. This technology can augment available information,

which will provide a better estimation of the need for transfer, the need for ICU admission and

the use of immediate therapies.

8.5 Limitations of the Study

Though this was a descriptive study of all critically ill children transported to a PICU in Ontario

by either a HBT or Ornge, as discussed, there likely still existed a selection bias for which

66

patients were referred for transfer as there are different thresholds for referring and for accepting

patients between the clinicians who refer and who accept the transfer. This systematic difference

was highlighted in the between-cohort (HBT vs Ornge) analyses (Appendix Table 2).

We were unable to adjust for severity of illness at ICU admission. Even though all patients

studied were admitted to a PICU, less than half required one of the ICU interventions studied.

Further adjustment could have been provided by severity of illness scores, such as the PIM or

PRISM. These scores were not available for analysis for this study but even if they were, they

would have to be used with caution as there is conflicting evidence on whether these scores can

be accurately applied to this population (discussed in the introduction (p.8)). In the two largest

transport studies to date, the PRISM3 over-estimated the risk of mortality in the U.S. 45

and the

PIM under-estimated mortality in the U.K..26

In addition, the important variable of paediatrician availability at the referral hospital did not

specify whether or not a paediatrician actually saw the patient. This would be an interesting

additional variable for inclusivity. Paediatrician availability is linked with availability of hospital

resources for caring for children, e.g. a neonatal intensive care unit, paediatric-sized equipment,

nurses and respiratory therapists trained in paediatrics. Paediatric consultation is a surrogate for

the expertise of an individual physician and could be an additional resource for a sick child that

has been shown to improve outcomes in other studies but was not available to analyse for this

study.

Missing data was an important limitation. Not all sites had data available for the start of the study

period, i.e. HSC only reliably collected transport data from September 2004. Furthermore,

Kingston General Hospital was not included in the study as it did not officially have a designated

67

academic paediatric critical care unit from 2007 onward, but admitting restrictions based on

severity of illness were imposed in 2005.

Data on deaths that occurred during transport were not available. Anecdotally, this is an

exceedingly rare event in paediatrics.

Due to significant missingness for transport times, imputation methodology was utilized for

transport times, specifically the median, lower and upper IQR were calculated for each transport

team and Ornge separately and imputed for the sensitivity analyses. The results of these analyses

did not differ from the results of any of the mortality or LOS models. This highlights the

strength of the models, which was further supported by a minimal change in the coefficients. As

for the PICU intervention use, with imputation modeling, stabilization time became significant.

Finally, we only had data for transport until and including 2012 with follow-up period to June,

2013 due to hospital administrative data transfer to ICES at the time that the statistical analyses

were performed.

8.6 Future Directions

Creation of a cohesive provincial transport database to describe program and patient outcomes is

supported by this study. So long as transport data entry is guided by institutional processes, it

will continue to be an onerous, expensive, and time consuming exercise to collate, clean,

transfer, and analyse in the context of provincial health administrative data. While we

demonstrated feasibility, it is not ideal for ongoing quality assessment initiatives or program

evaluation. This study lays the foundation work for future studies in Ontario and the rest of

Canada. It is generalizable to Canada and other countries (such as Australia) in so far as systems

of care, patient population, and geography are similar.

68

Alternatively, a validation of algorithms to identify this population using the DAD would

perhaps be simpler. However, this would not provide insight in terms of specific transport

processes, such as mode, and time intervals that only transport datasets would provide. This

study showed that several factors prior to admission to the PICU are important for patient

outcomes. This is why a provincial or national transport database, like the neonatal one that

currently exists, is important to implement.

Another interesting area of future research is analyzing the patients’ pre-transport and post-

transport medical conditions in more detail to determine the presence of new comorbidities post-

transport. Even though mortality is arguably the most important outcome to analyze, it is rare.

This makes this outcome impossible for smaller transport studies to analyze due to low power to

detect a difference. Further, we discussed the limitations of LOS outcomes, in terms of

generalizability to different transport systems. New comorbidities and health care utilization are

understudied but extremely valuable for patient counseling and also for health care resource

planning. The DAD has data on all patient diagnoses, but as of yet, no validated aggregated

variables. The Charlson Comorbidity index is an adult variable that describes comorbidity status

but though available, has not been validated in paediatrics. The Resource Intensity Weight is a

weighted variable that describes hospital resource use, but has also not been validated in

children. The opportunities for contributing to the field of health administrative database

research in a meaningful way are abundant.

An important contribution may also be the validation of the CCI codes. Within this study we

performed a modest validation of the codes against an acceptable gold standard. However, we

did not go on to calculate positive and negative predictive values of these codes. This would be

the next step and would then meet the recommendations for a valuable validation study.73

With

69

this information, future researchers may be persuaded to not rely on DAD records for ICU level

intervention use, at least in paediatrics, for the time being. This will mean continued need for

chart abstraction.

Cost effectiveness studies have not yet been conducted for this population. We could evaluate

telemedicine use to avoid transport in a less critically ill selection of patients. One could also

analyze the cost of providing a paediatrician at a referral hospital in terms of saving one

paediatric life.

Finally, this study demonstrated important findings that should be considered by provincial child

health decision-makers and potentially translated into policy. How can we prepare for an

increasing demand for critical care transports? We can broaden the scope of practice of existing

teams. We can use Geographic Information Systems to map patient origin and link to population

dwelling census data in order to predict future trends. We can use telemedicine to better discern

which patients can safely stay at the referral hospital and thus avoid transport, and to provide

ongoing medical support for those that do stay.

How can one improve paediatrician availability across the province? We can promote general

paediatrics training, provide incentives for rural and remote practices, improve paediatric-

specific hospital resources, and develop access to paediatricians by telemedicine are some ideas.

We should explore novel methods to increase expertise and expert-guided care.

How can we best train and equip our transport teams? They should have sufficient knowledge

and skills to manage infants in particular and should be equipped to support this population at all

times. For transport personnel in-training, they can maximize their downtime while they are

buddied for transports by spending time in the NICU helping to look after sick infants.

70

9 Conclusions

This was the first study to link medical records to population data to evaluate Ontario’s

paediatric critical care transport system. We have demonstrated that the system is growing, is

used by a young population with heavy health care use preceding transport, which required a

significant amount of resources for transport, in the ICU, and for hospitalization following

transport. Over a third of transports were for respiratory disease and for infants under 6 months

of age, thus transport teams should be well-prepared to manage these patients. Mortality rates are

higher than the general PICU population. Almost half of ICU deaths occurred in the first 24

hours following transport. Availability of a paediatrician at the referral hospital was associated

with lower ICU mortality outcomes, and may be a modificable factor to improve short and

longer term outcomes of critically ill children requiring inter-facility transport to a PICU.

71

10 References

1. Holmes MJ and Reyes HM. A critical review of urban pediatric trauma. The Journal of trauma. 1984;24:253-5. 2. Pracht EE, Tepas JJ, 3rd, Langland-Orban B, Simpson L, Pieper P and Flint LM. Do pediatric patients with trauma in Florida have reduced mortality rates when treated in designated trauma centers? Journal of pediatric surgery. 2008;43:212-21. 3. Newgard CD, McConnell KJ, Hedges JR and Mullins RJ. The benefit of higher level of care transfer of injured patients from nontertiary hospital emergency departments. The Journal of trauma. 2007;63:965-71. 4. Pollack MM, Alexander SR, Clarke N, Ruttimann UE, Tesselaar HM and Bachulis AC. Improved outcomes from tertiary center pediatric intensive care: a statewide comparison of tertiary and nontertiary care facilities. Critical care medicine. 1991;19:150-9. 5. Pearson G, Shann F, Barry P, Vyas J, Thomas D, Powell C and Field D. Should paediatric intensive care be centralised? Trent versus Victoria. Lancet. 1997;349:1213-7. 6. Li J, Monuteaux MC and Bachur RG. Interfacility transfers of noncritically ill children to academic pediatric emergency departments. Pediatrics. 2012;130:83-92. 7. Orr RA, Felmet KA, Han Y, McCloskey KA, Dragotta MA, Bills DM, Kuch BA and Watson RS. Pediatric specialized transport teams are associated with improved outcomes. Pediatrics. 2009;124:40-8. 8. Orr RA, Venkataraman ST, Cinoman MI, Hogue BL, Singleton CA and McCloskey KA. Pretransport Pediatric Risk of Mortality (PRISM) score underestimates the requirement for intensive care or major interventions during interhospital transport. Critical care medicine. 1994;22:101-7. 9. Borrows EL, Lutman DH, Montgomery MA, Petros AJ and Ramnarayan P. Effect of patient- and team-related factors on stabilization time during pediatric intensive care transport. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2010;11:451-6. 10. Odetola FO, Rosenberg AL, Davis MM, Clark SJ, Dechert RE and Shanley TP. Do outcomes vary according to the source of admission to the pediatric intensive care unit? Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2008;9:20-5. 11. Goh AY and Mok Q. Centralization of paediatric intensive care: are critically ill children appropriately referred to a regional centre? Intensive care medicine. 2001;27:730-5. 12. Soundappan SV, Holland AJ, Fahy F, Manglik P, Lam LT and Cass DT. Transfer of pediatric trauma patients to a tertiary pediatric trauma centre: appropriateness and timeliness. The Journal of trauma. 2007;62:1229-33. 13. Ross DW, Rewers A, Homan MB, Schullek JR, Hawke JL and Hedegaard H. Factors associated with the interfacility transfer of the pediatric trauma patient: implications for prehospital triage. Pediatric emergency care. 2012;28:905-10. 14. Sweney JS, Poss WB, Grissom CK and Keenan HT. Comparison of severity of illness scores to physician clinical judgment for potential use in pediatric critical care triage. Disaster medicine and public health preparedness. 2012;6:126-30. 15. Freishtat RJ, Klein BL, Teach SJ, Johns CM, Arapian LS, Perraut ME and Chamberlain JM. Admission predictor modeling in pediatric interhospital transport. Pediatric emergency care. 2004;20:443-7.

72

16. Crichlow RJ, Zeni A, Reveal G, Kuhl M, Heisler J, Kaehr D, Vijay P and Musapatika DL. Appropriateness of patient transfer with associated orthopaedic injuries to a Level I trauma center. Journal of orthopaedic trauma. 2010;24:331-5. 17. Newgard CD, McConnell KJ and Hedges JR. Variability of Trauma Transfer Practices among Non-tertiary Care Hospital Emergency Departments. Academic Emergency Medicine. 2006;13:746-754. 18. Seidel JS and Gausche-Hill M. Pediatric equipment availability and emergency preparedness. Annals of emergency medicine. 2001;37:388-9. 19. Garwe T, Cowan LD, Neas B, Cathey T, Danford BC and Greenawalt P. Survival benefit of transfer to tertiary trauma centers for major trauma patients initially presenting to nontertiary trauma centers. Academic emergency medicine : official journal of the Society for Academic Emergency Medicine. 2010;17:1223-32. 20. Koval KJ, Tingey CW and Spratt KF. Are patients being transferred to level-I trauma centers for reasons other than medical necessity? The Journal of bone and joint surgery American volume. 2006;88:2124-32. 21. Archdeacon MT, Simon PM and Wyrick JD. The influence of insurance status on the transfer of femoral fracture patients to a level-I trauma center. The Journal of bone and joint surgery American volume. 2007;89:2625-31. 22. Edge WE, Kanter RK, Weigle CG and Walsh RF. Reduction of morbidity in interhospital transport by specialized pediatric staff. Critical care medicine. 1994;22:1186-91. 23. Macnab AJ. Optimal escort for interhospital transport of pediatric emergencies. The Journal of trauma. 1991;31:205-9. 24. Britto J, Nadel S, Maconochie I, Levin M and Habibi P. Morbidity and severity of illness during interhospital transfer: impact of a specialised paediatric retrieval team. Bmj. 1995;311:836-9. 25. Bellingan G, Olivier T, Batson S and Webb A. Comparison of a specialist retrieval team with current United Kingdom practice for the transport of critically ill patients. Intensive care medicine. 2000;26:740-4. 26. Ramnarayan P, Thiru K, Parslow RC, Harrison DA, Draper ES and Rowan KM. Effect of specialist retrieval teams on outcomes in children admitted to paediatric intensive care units in England and Wales: a retrospective cohort study. Lancet. 2010;376:698-704. 27. Meyer MT, Mikhailov TA, Kuhn EM, Collins MM and Scanlon MC. Pediatric Specialty Transport Teams Are Not Associated With Decreased 48-Hour Pediatric Intensive Care Unit Mortality: A Propensity Analysis of the VPS, LLC Database. Air medical journal. 2016;35:73-8. 28. Woodward GA, Insoft RM, Pearson-Shaver AL, Jaimovich D, Orr RA, Chambliss R, Abramo TJ, Bose C, Gomez MA and Westergaard F. The state of pediatric interfacility transport: consensus of the second National Pediatric and Neonatal Interfacility Transport Medicine Leadership Conference. Pediatric emergency care. 2002;18:38-43. 29. King BR, King TM, Foster RL and McCans KM. Pediatric and neonatal transport teams with and without a physician: a comparison of outcomes and interventions. Pediatric emergency care. 2007;23:77-82. 30. Beyer AJ, 3rd, Land G and Zaritsky A. Nonphysician transport of intubated pediatric patients: a system evaluation. Critical care medicine. 1992;20:961-6. 31. van Lieshout EJ, Binnekade J, Reussien E, Dongelmans D, Juffermans NP, de Haan RJ, Schultz MJ and Vroom MB. Nurses versus physician-led interhospital critical care transport: a randomized non-inferiority trial. Intensive care medicine. 2016;42:1146-54. 32. Harrison TH, Thomas SH and Wedel SK. Success rates of pediatric intubation by a non-physician-staffed critical care transport service. Pediatric emergency care. 2004;20:101-7.

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33. Kronick JB, Frewen TC, Kissoon N, Lee R, Sommerauer JF, Reid WD, Casier S and Boyle K. Pediatric and neonatal critical care transport: a comparison of therapeutic interventions. Pediatric emergency care. 1996;12:23-6. 34. Henning R and McNamara V. Difficulties encountered in transport of the critically ill child. Pediatric emergency care. 1991;7:133-7. 35. Gunnarsson B, Heard CM, Rotta AT, Heard AM, Kourkounis BH and Fletcher JE. Use of a physiologic scoring system during interhospital transport of pediatric patients. Air medical journal. 2001;20:23-6. 36. Singh JM, MacDonald RD, Bronskill SE and Schull MJ. Incidence and predictors of critical events during urgent air-medical transport. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne. 2009;181:579-84. 37. Singh JM, Gunz AC, Dhanani S, Aghari M and MacDonald RD. Frequency, Composition, and Predictors of In-Transit Critical Events During Pediatric Critical Care Transport. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2016. 38. Beddingfield FC, 3rd, Garrison HG, Manning JE and Lewis RJ. Factors associated with prolongation of transport times of emergency pediatric patients requiring transfer to a tertiary care center. Pediatric emergency care. 1996;12:416-9. 39. Fatovich DM, Phillips M, Jacobs IG and Langford SA. Major trauma patients transferred from rural and remote Western Australia by the Royal Flying Doctor Service. The Journal of trauma. 2011;71:1816-20. 40. McCowan CL, Swanson ER, Thomas F and Handrahan DL. Outcomes of pediatric trauma patients transported from rural and urban scenes. Air medical journal. 2008;27:78-83. 41. Larson JT, Dietrich AM, Abdessalam SF and Werman HA. Effective use of the air ambulance for pediatric trauma. The Journal of trauma. 2004;56:89-93. 42. Galvagno Jr Samuel M, Thomas S, Stephens C, Haut Elliott R, Hirshon Jon M, Floccare D and Pronovost P. Helicopter emergency medical services for adults with major trauma. Cochrane Database of Systematic Reviews. 2013. 43. Stewart CL, Metzger RR, Pyle L, Darmofal J, Scaife E and Moulton SL. Helicopter versus ground emergency medical services for the transportation of traumatically injured children. Journal of pediatric surgery. 2015;50:347-52. 44. Stroud MH, Gupta P and Prodhan P. Effect of altitude on cerebral oxygenation during pediatric interfacility transport. Pediatric emergency care. 2012;28:329-32. 45. Gregory CJ, Nasrollahzadeh F, Dharmar M, Parsapour K and Marcin JP. Comparison of critically ill and injured children transferred from referring hospitals versus in-house admissions. Pediatrics. 2008;121:e906-11. 46. Odetola FO, Clark SJ, Gurney JG, Dechert RE, Shanley TP and Freed GL. Effect of interhospital transfer on resource utilization and outcomes at a tertiary pediatric intensive care unit. Journal of critical care. 2009;24:379-86. 47. Kanter RK, Edge WE, Caldwell CR, Nocera MA and Orr RA. Pediatric mortality probability estimated from pre-ICU severity of illness. Pediatrics. 1997;99:59-63. 48. Philpot C, Day S, Marcdante K and Gorelick M. Pediatric interhospital transport: diagnostic discordance and hospital mortality. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2008;9:15-9. 49. Valenzuela TD, Criss EA, Copass MK, Luna GK and Rice CL. Critical care air transportation of the severely injured: does long distance transport adversely affect survival? Annals of emergency medicine. 1990;19:169-72.

74

50. Duncan AW. The burden of paediatric intensive care: an Australian and New Zealand perspective. Paediatric respiratory reviews. 2005;6:166-73. 51. Argus BM, Dawson JA, Wong C, Morley CJ and Davis PG. Financial costs for parents with a baby in a neonatal nursery. J Paediatr Child Health. 2009;45:514-7. 52. Rennick JE, Dougherty G, Chambers C, Stremler R, Childerhose JE, Stack DM, Harrison D, Campbell-Yeo M, Dryden-Palmer K, Zhang X and Hutchison J. Children's psychological and behavioral responses following pediatric intensive care unit hospitalization: the caring intensively study. BMC pediatrics. 2014;14:276. 53. de Mos N, van Litsenburg RR, McCrindle B, Bohn DJ and Parshuram CS. Pediatric in-intensive-care-unit cardiac arrest: incidence, survival, and predictive factors. Critical care medicine. 2006;34:1209-15. 54. Graves JM, Kannan N, Mink RB, Wainwright MS, Groner JI, Bell MJ, Giza CC, Zatzick DF, Ellenbogen RG, Boyle LN, Mitchell PH, Rivara FP, Wang J, Rowhani-Rahbar A, Vavilala MS, Pediatric Guideline A and Outcomes S. Guideline Adherence and Hospital Costs in Pediatric Severe Traumatic Brain Injury. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 2016;17:438-43. 55. Hsu BS and Brazelton TB, 3rd. A Comparison of Costs Between Medical and Surgical Patients in an Academic Pediatric Intensive Care Unit. WMJ : official publication of the State Medical Society of Wisconsin. 2015;114:236-9. 56. Whyte HE, Jefferies AL, Canadian Paediatric Society F and Newborn C. The interfacility transport of critically ill newborns. Paediatrics & child health. 2015;20:265-75. 57. Fendya DG, Genovesi A, Belli K, Page K and Vernon DD. Organized interfacility transfer processes: An opportunity to improve pediatric emergency care. Pediatric emergency care. 2011;27:900-906. 58. Rose L, McKim DA, Katz SL, Leasa D, Nonoyama M, Pedersen C, Goldstein RS, Road JD and Group CA. Home mechanical ventilation in Canada: a national survey. Respiratory care. 2015;60:695-704. 59. Amin R, Sayal P, Syed F, Chaves A, Moraes TJ and MacLusky I. Pediatric long-term home mechanical ventilation: twenty years of follow-up from one Canadian center. Pediatric pulmonology. 2014;49:816-24. 60. Scales DC, Guan J, Martin CM and Redelmeier DA. Administrative data accurately identified intensive care unit admissions in Ontario. Journal of clinical epidemiology. 2006;59:802-7. 61. Sacchetti A, Warden T, Moakes ME and Moyer V. Can sick children tell time?: emergency department presentation patterns of critically ill children. Academic emergency medicine : official journal of the Society for Academic Emergency Medicine. 1999;6:906-10. 62. McCrory MC, Gower EW, Simpson SL, Nakagawa TA, Mou SS and Morris PE. Off-hours admission to pediatric intensive care and mortality. Pediatrics. 2014;134:e1345-53. 63. Du Pont-Thibodeau G, Robitaille N, Gauvin F, Thibault L, Rivard GE, Lacroix J and Tucci M. Incidence of hypotension and acute hypotensive transfusion reactions following platelet concentrate transfusions. Vox sanguinis. 2016;110:150-8. 64. Arias Y, Taylor DS and Marcin JP. Association between evening admissions and higher mortality rates in the pediatric intensive care unit. Pediatrics. 2004;113:e530-4. 65. Edwards JD, Houtrow AJ, Vasilevskis EE, Rehm RS, Markovitz BP, Graham RJ and Dudley RA. Chronic conditions among children admitted to U.S. pediatric intensive care units: their prevalence and impact on risk for mortality and prolonged length of stay*. Critical care medicine. 2012;40:2196-203. 66. Guttmann A, Weinstein M, Austin PC, Bhamani A and Anderson G. Variability in the emergency department use of discretionary radiographs in children with common respiratory conditions: the mixed effect of access to pediatrician care. Cjem. 2013;15:8-17.

75

67. Weiner SG, Ruffing RP and Barnewolt BA. A comparison of resource utilization between emergency physicians and pediatric emergency physicians. Pediatric emergency care. 2012;28:869-72. 68. Chang YC, Ng CJ, Chen YC, Chen JC and Yen DH. Practice variation in the management for nontraumatic pediatric patients in the ED. The American journal of emergency medicine. 2010;28:275-83. 69. Filler G, Piedboeuf B and Paediatric Chairs of C. Variability of the pediatric subspecialty workforce in Canada. The Journal of pediatrics. 2010;157:844-7 e1. 70. Fatovich DM, Phillips M, Langford SA and Jacobs IG. A comparison of metropolitan vs rural major trauma in Western Australia. Resuscitation. 2011;82:886-90. 71. Wadhwa A and Lingard L. A qualitative study examining tensions in interdoctor telephone consultations. Medical education. 2006;40:759-67. 72. Cartmill M and White BD. Telephone advice for neurosurgical referrals. Who assumes duty of care? British journal of neurosurgery. 2001;15:453-5. 73. Benchimol EI, Manuel DG, To T, Griffiths AM, Rabeneck L and Guttmann A. Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. Journal of clinical epidemiology. 2011;64:821-9.

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11 Appendix

Appendix Table 1. Variables Abstracted

Variable Description Type

Age In years Continuous

Sex M/F Binary

Primary Problem Transport datasets: Respiratory,

cardiac, neurologic, sepsis, metabolic,

trauma, toxic ingestion, other

Categorical

Prior Health Care Contact # ER visits (NaCRS)+ # same day

surgeries (SDS) + duration in days of

hospitalizations (DAD) in the 6

months prior to transport

- divided by age for patients <=6

months

- divided by 6 months for patients >

6months

Continuous

Time of day Day= 0700-1900

Night= 1900-0700

Binary

Season Winter= January, February, March

Spring= April, May, June

Summer= July, August, September

Fall= October, November, December

Categorical

Duration of stabilization by crew Arrival time minus departure time at

referral hospital (hours)

Continuous

Duration of transport Arrival time in minus departure time

from referral hospital (hours)

Continuous

Type of crew Hospital Based Team (HBT)

Paediatric Team (Ornge)

Critical Care Paramedic (Ornge)

Advanced Care Paramedic (Ornge)

Primary Care Paramedic (Ornge)

Categorical

Mode of transport Rotor, fixed, land Categorical

Distance traveled Kilometer Continuous

Referral hospital with

paediatrician availability

Y/N Binary

Referral hospital type Teaching, Community, Small, Nursing

Station

Categorical

Admitting 1 of 4 s in Ontario Categorical

Mortality (ICU and hospital) Y/N: date of death within index

admission

Binary

Early mortality Y/N: date of death within 24h of index

admission

Binary

Length of stay (ICU and In days Continuous

77

hospital)

Duration of invasive mechanical

ventilation

1- Transport dataset:

- Whole or part days

2- DAD:

- FLAG_MVEBT_GE96

- FLAG_MVENT_LT96

- INCODE: 1GZ31 CAEP, CAND, and

CAPK

Continuous

Duration of non-invasive

mechanical ventilation



2- DAD: IGZ31 CBND for non-

invasive mechanical

Continuous

Duration of continuous renal

replacement therapy



2- DAD:

- INCODE: IPZ21HDBS

Continuous

Duration of ECMO 1- Transport Dataset:

-Whole or part days

2- DAD:

- INCODE: ILZ37GPQM and LAQM

Continuous

Duration of inotropes 1- Transport dataset:


Continuous

78

Appendix Table 2. Descriptive Characteristics of Transport Systems

Characteristic HBT Ornge P-value

Patient

Age (years) 0.3 (0.01, 2.6) 5.5 (1.7, 12.9) <0.0001

Sex male n (%) 1392 (59.1) 982 (57.1) NS

Prior health care

contact

0.17 (0.3) 0.07 (0.2) <0.0001

Diagnosis n (%)

Respiratory

Cardiac

Neurologic

Sepsis

Trauma

Toxic ingestion

Metabolic

Other

1022 (44.1)

341 (14.7)

443 (19.1)

139 (6.0)

47 (2.0)

6 (0.3)

116 (5.0)

204 (8.8)

464 (27.2)

202 (11.8)

471 (27.6)

96 (5.6)

149 (8.7)

79 (4.6)

155 (9.1)

93 (5.4)

<0.0001

Transport

Daytime transport

n (%)

752 (31.9) 790 (46.0) <0.0001

Season n (%)

Winter

Spring

Summer

Fall

673 (28.6)

576 (24.5)

486 (20.6)

620 (26.3)

449 (26.1)

406 (23.1)

405 (23.6)

459 (26.7)

NS

Mode n (%)

Land

Rotor

Fixed wing

582 (87.1)

46 (6.9)

40 (6.0)

662 (43.0)

622 (40.4)

255 (26.7)

<0.0001

Distance (kms) 100 (39.1, 183.6) 53.4 (28.1, 167.7) <0.0001

Time to team contact

(hrs)

1.25 (0.8, 2.2) 0.8 (0.6, 1.3) <0.0001

Stabilization time 1.7 (1.2, 2.3) 0.8 (0.5, 1.2) <0.0001

Transport time 0.9 (0.6, 1.5) 1.0 (0.7, 1.3) 0.5

Total time of team

contact

2.8 (2.0, 3.6) 1.8 (1.4, 2.4) <0.0001

Referral Hospital

Type

Teaching

Community

Small

Nursing station

264 (11.2)

1868 (79.5)

213 (9.1)

<5

253 (14.8)

1312 (76.8)

138 (8.1)

6 (0.4)

0.004

Pediatrician available

n (%)

1913 (81.4) 1355 (79.3) NS

79

Appendix Table 3. Missingness for Descriptive Characteristics

Patient Characteristics N (%) missing

Age (years) median (IQR) 0 (0)

Sex male n (%) 0 (0)

Prior health care contact 0 (0)

Diagnosis n (%) 47 (1.2)

Transport Characteristics

Daytime transport n (%) 0 (0)

Season n (%) 0 (0)

Mode n (%) 1867 (45.8)

Distance (kms) 45 (1.1)

Time to team contact (hrs) 1396 (34.2)

Stabilization time (hrs) 738 (18.1)

Transport time (hrs) 732 (18.0)

Referral Hospital Characteristics

Type 16 (0.4)

Pediatrician available n (%) 16 (0.4)

Appendix Figure 1. Health Care Use in the 6 Months Prior to Transport

80

Appendix Figure 2. Percent of Transported Children who Died Following Transport

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

First 24 hours Remaining PICUadmission

Remainder of 6 monthssince transport

%

81

12 Endnotes

i Children’s Acute Transport Service: CATS Annual Report 2012/2013. Available at: http://site.cats.nhs.uk/wp-

content/uploads/2013/07/ cats_annual_reportvers2.4.pdf ii Focus on Geography Series: StatsCan[https://www12.statcan.gc.ca/census-recensement/2011/as-sa/fogs-spg/Facts-

pr-eng.cfm?Lang=Eng&GK=PR&GC=35] iii

CritiCall Ontario, [https://www.phrs.criticall.org/User/LogIn?ReturnUrl=%2f]. iv Hospital Report Research Collaborative, 2007, Canadian Institute for Health Informatics,

[https://secure.cihi.ca/free_products/OHA_ED_07_EN_final_secure.pdf], page 8. v Ontario Maternal and Newborn Level of Care Designations, 2016, Provincial Council for Maternal and Child

Health, [http://www.pcmch.on.ca/health-care-providers/maternity-care/pcmch-strategies-and-initiatives/loc/] vi ICES Data Integration FAQ, October 2011.

vii Hypothesis Testing: One-Sample Inference - One-Sample Inference for a Binomial Proportion in Bernard

Rosner's Fundamentals of Biostatistics

ix

Focus on Geography Series: StatsCan[https://www12.statcan.gc.ca/census-recensement/2011/as-sa/fogs-spg/Facts-

pr-eng.cfm?Lang=Eng&GK=PR&GC=35] x Hosmer DW, Lemeshow S. Applied logistic regression. 2013 New York: Wiley. ISBN 978-0-470-58247-3

xi Canadian Medical Association, 2016 [https://www.cma.ca/Assets/assets-library/document/en/advocacy/Pediatrics-

e.pdf] xii

Peebles EJ, Miller MR, Lynch T, Tijssen JA, Factors Associated with Discharge Home after Transfer to a

Pediatric Emergency Department. Accepted for publication to Pediatric Emergency Care, 2016

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Inter-facility Transport of Critically Ill Children in Ontario · 2017. 7. 18. · Inter-facility Transport of Critically Ill Children in Ontario Janice Tijssen Masters of Science