VTE RISK ASSESSMENT MODELS Sultan Bin Abdul Aziz Humanitarian City experience

Dr Omer S. KhanChief Medical Resident, SBAHC

VTE PREVENTION POLICY  To standardize and provide evidence based

risk assessment for thromboembolism and appropriate intervention for medical and surgical patients admitted to Sultan Bin Abdulaziz Humanitarian City.

 

POLICY

All hospitalized medical, and surgical adult patients will be assessed and prescribed the most appropriate intervention by the treating physician for VTE risk

At the time of admission to hospital At the time of significant change in clinical

status At the time of transfer from one type (level) of

care to another At discharge

INCLUSION AND EXCLUSION CRITERIA

Inclusion: All adult patients admitted to SBAHC will be screened for VTE risk

Exclusion: ER patients (Review based on clinical judgment)

Outpatient Clinic (Review based on clinical judgment)

Paediatrics……..?

SCOPE OF GUIDELINESOur guidelines provides supportive

documentation for the Sultan Bin Abdulaziz Humanitarian City VTE prophylaxis for the hospitalized patients.

Practice based on the best evidence (9thed: ACCP Evidence-Based Clinical Practice Guidelines on February 23, 2012)

SCOPE OF GUIDELINES Rationale for

thromboprophylaxis recommendations

Approaches to be used for VTE risk assessment

Bleeding risk assessment Guide on pharmacological

and non pharmacological prophylaxis in different patient population subsets

Roles and responsibilities of health care providers

SCOPE OF GUIDELINESSBAHC unique patient population and scope Spinal cord injury Brain injury Amputees Stroke and recurrent stroke

Rehabilitated and mobility restored

RAM SELECTION Choice of Risk assessment models.

Which one to follow?

Committee meetings Extensive literature review Discussions and deliberation

RAM SELECTION Questions and challenges VTE prophylaxis was suboptimal in SBAHC in

particular and in KSA hospitals in general despite long-standing evidence-based recommendations

Data from observational studies indicate a lower uptake of effective prophylaxis in patients hospitalized with medical versus surgical conditions

Reluctance to use prophylaxis in medical patients1. Identifying at-risk patients2. Balancing risks of bleeding against occurrence of

VTE

RAM SELECTION Questions and challenges

Several risk-assessment models (RAMs) have been proposed to assist physicians in identifying surgical and non-surgical patients who need prophylaxis

Published RAMs lack 1. Generalizability2. Adequate validation

Validated dynamic RAMs are needed to assess VTE risk at the point-of-care in real time

LITERATURE REVIEW Caprini risk assessment tool Padua prediction score Kucher IMPROVE prediction model IMPROVE Associative Score for VTE Intermountain Roger’s score NSW Health Clinical Pathway Goldhaber

LITERATURE REVIEW

Surgical Patients Caprini risk assessment tool

Medical Patients

Caprini risk assessment tool

Vs

Padua prediction score

Comparison between Caprini and Padua risk assessment models for hospitalized medical patients at risk for venous thromboembolism: a retrospective study Xiaohan Liua, Chengyuan Liua,†, Xi Chena, Wenwen Wub and Gendi Lua,Department of Nursing, Changzheng Hospital, Second Military Medical University, Shanghai, China , Department of

Cardiothoracic Surgery, Changzheng Hospital, Second Military Medical University, Shanghai, China Received December 6, 2015. Revision received March 22, 2016. Accepted April 9, 2016.

OBJECTIVES This study aimed to evaluate the validity of the risk assessment model

(RAM) of Caprini and Padua in identifying venous thromboembolism (VTE) among hospitalized medical patients.

METHODS This retrospective study reviewed a total of 320 VTE and 320 non-

VTE patients The Caprini and Padua RAMs were implemented and the individual

scores of each risk factor were summed to generate a cumulative risk score

Sensitivity, specificity, and positive and negative predictive values of these two models were analysed

Receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) and the Youden index.

RESULTS Significant differences were observed in risk factors between VTE and non-VTE patients

More VTE patients were classified into the high–superhigh risk level by the Caprini RAM than the Padua RAM (70.9 vs 23.4%, P < 0.01)

The sensitivity and +ve and -ve predictive values in the Caprini RAM > Padua RAM (P < 0.05)

Specificity of the Caprini < Padua RAM (P < 0.01)

The AUC and the Youden index Caprini > Padua RAM (P < 0.01)

CONCLUSIONS The Caprini RAM was suggested to be more

effective than the Padua RAM for identification of hospitalized medical patients at risk for VTE

Validation of the Caprini Score for Risk Assessment of Venous Thromboembolism in Hospitalized Medical Patients

P. Grant, MD; S. A. Flanders, MD; M. T. Greene, PhD; S. J. Bernstein, MD

University of Michigan, Ann Arbor, MIVA Ann Arbor Healthcare System, Ann Arbor, MI

Meeting: SHM Annual Meeting 2014

Although the Caprini RAM has been validated in surgery patients, it was sought to determine its predictive value for VTE events in hospitalized medical patients.

METHODSUsing web‐based data entry, a nurse abstractor at each

participating hospital (n=40) collected detailed demographic and clinical data, including all known risk factors for VTE and use of pharmacologic prophylaxis.

The occurrence of VTE events during hospitalization and 90‐day post‐discharge follow‐up were determined by medical record review and follow‐up phone calls.

Non‐parametric test for trend across ordered groups and logistic regression to determine if increasing Caprini score values were associated with VTE events.

Caprini point scoring system was used to define low (0 ‐1 points), moderate (2‐4 points) and high (5+ points) risk categories.

RESULTSAmong the 52,989 patients included in this

analysis, 299 (0.56%) had a VTE event by 90 days

Significant increase in VTE with incremental increases in Caprini scores (p for trend <0.001)

Compared to low risk patients, the odds of having a VTE event was 3‐fold greater among high risk patients (p=0.039).

However…. The odds of VTE did not differ between the

low and moderate risk groups.

Among patients with a Caprini score of 5 or greater, the rate of VTE for patients on pharmacologic prophylaxis (0.72%) did not differ from those not on pharmacologic prophylaxis (0.86%), chi‐squared = 1.29, p = 0.26

CONCLUSIONSIn a large cohort of hospitalized medical patients, an

increasing Caprini score was predictive of a greater risk for VTE

The odds of developing VTE was only significant among high risk patients, however, exposure to pharmacologic prophylaxis did not affect the event rate

Although the Caprini RAM appears to be a valid predictor of VTE risk, it did not effectively discriminate populations for which pharmacologic prophylaxis was useful

Venous Thromboembolism Risk Assessment Models for Hospitalized Medical Patients M. T. Greene, PhD; S. Kaatz, DO; S. J. Bernstein, MD,; P. Grant, MD; J. N.

Wietzke, MHSA, MLS; S. A. Flanders, MDUniversity of Michigan, Ann Arbor, MIHurley Medical Center, Flint, MIVA Ann Arbor Healthcare System, Ann Arbor, MISHM Annual Meeting 2014

The Michigan Hospital Medicine Safety Consortium (HMS), a state‐based quality collaborative aimed at preventing adverse events in hospitalized medical patients, reviewed various RAMs in an effort to determine which model(s) has the most utility for medical patients

METHODS Using web‐based data entry, hospitals collected demographic and

clinical data, including known risk factors, use of pharmacologic prophylaxis, and VTE events through 90 days after discharge for 760 patients annually

VTE outcomes were determined by medical record review and follow‐up phone calls and included all DVT and pulmonary embolism events.

The Intermountain, IMPROVE, Padua, Kucher, and Caprini RAMs were applied to the HMS population and risk was classified as “at risk” or “low risk” based on the respective published cutpoints

Backward stepwise logistic regression was used to develop a simple HMS RAM

To determine the predictive capabilities of the various RAMs, VTE events were regressed against each RAM

Respective model discrimination was assessed via the c‐statistic

RESULTSThe performance of each

of the RAMs was assessed on data collected on a total of 52,989 patients. In general, model discrimination was poor, with c‐statistics ranging from 0.51 – 0.65

A simple 3‐element RAM yielded the best model discrimination (c‐statistic = 0.65).

CONCLUSION The ability to predict VTE risk among a large

cohort of hospitalized medical patients using existing and developed RAMs was limited

Parsimonious RAMs may have greater predictive ability and have the added advantage of facilitating risk assessment due to their simplicity.

Additional work to determine which medical patients are at greatest risk for VTE and require appropriate thromboprophylaxis is warranted

FINAL CONSENSUS

Hybrid approach

FINAL CONSENSUS Upon admission, the initial assessment will be

done within 24 hours of the patient admission time by the physician, using Screening and Prophylaxis VTE Risk Assessment tool

PADUA prediction score model for medical patients

Caprini assessment tool for surgical patients

PROCEDURE The treating team will order the VTE prophylaxis according

to the guideline recommendations. During the hospitalization the Nurse should do VTE

reassessment if new events occur such as infection, surgery, fracture, transfer from level of care to another etc. then the score should be updated.

Any changes or fluctuation in the score, physician shall be notified immediately.

Upon discharge, the VTE assessment will be done by the treating team and will order the VTE prophylaxis according to the patients need, and guideline recommendations.

The physician may override the clinical guidelines based on his clinical Judgment

The physician may override the clinical guidelines based on his clinical judgment

Where firm recommendations are available, the physician should treat according to the evidence

Where evidence is lacking, the physician should assess each patient based on their medical and clinical status and use a risk factor model to help stratify patients according to risk

Combining guidelines with intelligent clinical practice, more patients should receive appropriate prophylactic treatment tailored to their individual risk

MEASURES Percentage of staff compliance to implement

VTE risk assessment tool

Interrater reliability test for the VTE risk assessment tool

Percentage of patients who receive VTE prophylaxis following VTE clinical pathway

Current VTE prophylaxis indicator for surgical patients

SBAHC VTE PROPHYLAXIS PROJECT TEAMAbdulilah FayyadClinical Resource Nurse Dr. Ahmer WaheedQuality and Risk Management Specialist Dr. Elfateh ElkhatibConsultant Internal MedicineManar SweissClinical PharmacistDr. Omer Khan Medical Resident Dr. Khazim Sakalla Consultant Orthopedic Surgeon

Thank you