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Delivering improved outcomes for chronic disease patients

Prof. Lionel Tarassenko PhD CBE FREng Chair of Electrical Engineering

Institute of Biomedical EngineeringUniversity of Oxford

23 October 2012

Wireless Health 2012

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Links between San Diego and Oxford

Qualcomm Scholarships

Prof. Shu Chien, UCSD

Oxford Advisory Board

Oxford city centre

Institute of Biomedical Engineering

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Acknowledgements

Wireless Health group: Prof. Andrew Farmer, Dr Kazem Rahimi, Dr Kirsty Bobrow, Dr Oliver Gibson, Dr Mark Larsen, Dr Carmelo Velardo, Dr Ahmar Shah, Lise Loerup, Arvind Raghu, David Springer

· Clinical trials outside the UK:· Assessing cardiovascular risk in rural India

· Management of hypertension (South Africa)

· Screening for Rheumatic Heart Disease using m-stethoscope (South Africa)

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Chronic disease in the developed world

· Chronic disease management accounts for 80% of the growth in healthcare spending in the developed world in the last 50 years.

· Chronic diseases are health problems that require on-going management for years or decades (e.g. diabetes, hypertension, heart failure or Chronic Obstructive Pulmonary Disease – COPD) .

· In the US, chronic diseases affect 130 million people, generating healthcare costs of approximately $1.4 trillion a year overall.

· There are 17.5 million people in the UK with a chronic disease (32% of the population). Two-thirds of these are aged 75 or above.

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Chronic disease in the UK

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Cell phoneBlood Glucose

meter

Data review by healthcare professional

Readings automatically transmitted by the phone

internet

SERVER

Feedback to patient

• GPRS (and later 3G) services switched on in the UK ten years ago enabling real-time transmission of self-monitoring data

• Bluetooth transmission from device to cell phone ensuring reliability of data

Wireless health architecturefor chronic disease management

Incoming data stored on secure server

Evidence-based medicine18 clinical studies and trials of wireless health

Asthma COPD

Heart Failure Type 1 diabetes

Type 2 diabetes

Hypertension

Cystic fibrosis Cancer

Health Economics

3 published clinical studies, 1 Randomized Controlled Trial (Asthma UK)

1 trial at Bristol Royal Infirmary published in Respiratory Medicine 1 cohort study + Randomized Controlled Trial in Oxfordshire 1 cohort study

1 Randomized Controlled Trial published in Diabetes Care 1 Gestational Diabetes study on-going in Oxford 2 published clinical studies (Informatics in Primary Care) 1 study completed in Oxfordshire GP Practices

1 trial presented at European Stroke Conferences 1 study on-going in Oxfordshire GP Practices 1 published clinical trial

1 study published in Annals of Oncology 1 study completed at Churchill Hospital Whole-System Demonstrator with Department of Health

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Targeted interventions using wireless health for short periods of time (up to six months) deliver maximum benefit in terms of improved patient outcomes:

· Blood pressure monitoring after a stroke· Insulin titration in Type 2 diabetes· Management of gestational diabetes· Self-titration of oral medication in Type 2 diabetes· Toxicity monitoring during chemotherapy

Evidence-based medicineKey results from clinical studies and trials

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· Lowering Blood Pressure (BP) is highly effective to reduce adverse cardiovascular events after a stroke or a Transient Ischemic Attack (TIA). A reduction of 10 mmHg systolic reduces the risk of stroke by ~40%.

· Anti-hypertensive therapy is therefore recommended in these patients.

· Hypothesis: Hypertension may be under-estimated as a result of making only one or two BP measurements in the outpatient clinic on discharge, as opposed to multiple measurements in the home.

Monitoring blood pressure in stroke patients

Patients are therefore given a BP monitor linked via Bluetooth to a cell phone to take home to measure their blood pressure.

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· For the last four years, all patients with acute TIAs and minor stroke in the Oxford Vascular Study (OXVASC) have been monitored post discharge.

· After leaving hospital with a prescription of standard BP lowering therapy, these patients measure their blood pressure three times daily at home with a Bluetooth BP Monitor for one to three months, depending on control.

· Measurements transmitted automatically in real time by the cell phone are checked daily on a secure web page in the Stroke Unit.

Monitoring blood pressure in stroke patients

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BP in outpatients: 130/70 mmHg

Intervention79 year old female

Monitoring blood pressure following a TIA

mmHg

SysBP

DiasBP

1 month

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· 203 (92.3%) of 220 patients (mean age = 70; 29% ≥80 years) were willing and able to undertake Bluetooth and cell phone home monitoring, and all continued for at least one month.

· Monitoring led to 192 changes in BP lowering medication in 128 patients (63%).

· Mean systolic BP was 148/82 mmHg at entry and 127/72 mmHg at the 6-month follow-up clinic (21 mmHg reduction in systolic BP).

· Patient satisfaction (0 poor to 100 excellent) with home monitoring was high (mean score = 88.3), with 90% approving of intensive monitoring and 88% being reassured by the automated surveillance.

Monitoring blood pressure in stroke patientsData analysis after three years

Fischer, Wilson, Paul, Bull, Welch, Tarassenko & Rothwell. Bluetooth blood pressure home-monitoring in patients with TIA and minor stroke: feasibility, acceptability and control. European Stroke Conference, Barcelona, 2010

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· The conversion from oral medication to insulin injections is an important stage in the progression of the management of type 2 diabetes for many patients.

· Insulin initiation can restore good glycemic control, reducing the risks of complications such as retinopathy or amputation.

· Patients are often reluctant to commence insulin treatment because of anxiety about needles and injections, or concern about side-effects such as hypoglycemia or weight gain.

· Self-monitoring of fasting blood glucose integrated into a wireless solution (Bluetooth BG meter + cell phone + titration protocol) was offered to patients wanting extra support.

Insulin titration in Type 2 diabetes

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· Patients with type 2 diabetes recruited from 15 General Practices in Oxfordshire

· Mean age = 58 years, with an average diabetes duration of 6 years

· Mean body weight = 97 kgs (mean BMI of 32)

· Mean HbA1c at the start = 9.5% (target is 7%)

· Mean insulin dose at the start = 48 units

Insulin titration in Type 2 diabetes

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Insulin initiation after 3 weeks of BG monitoring

Understanding patient psychology

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· A telehealth nurse working remotely reviewed results and provided phone advice according to a set protocol.

· Practice Nurses saw a reduction in the time required to support insulin titration.

· Blood glucose control improved, as reflected by a mean decrease in HbA1c of 0.66% (P = 0.05), with the mean insulin dose increasing by 17 units (P = 0.006).

· (A 1% drop in HbA1c leads to a 33-37% reduction in the risk of microvascular complications.)

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50

55

60

65

70

8.4

8.6

8.8

9.0

9.2

9.4

9.6

9.8

10.0

Baseline 3m 6m

Impact on HbA1c and insulin dose

HbA1c (%) Insulin dose (U)

• Turner J, Larsen M, Tarassenko L, Neil A and Farmer A. • Informatics in Primary Care (May 2009)

• Larsen M, Turner J, Neil A , Farmer A. and Tarassenko L.• Journal of Telemedicine & Telecare (December 2010)

n = 23

Insulin titration in Type 2 diabetes

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· In 2011, the threshold for an abnormal Fasting Blood Glucose test result was reduced from 7.0 mmol/l (126 mg/dl) to 5.1 mmol/l (92 mg/dl) in line with the International Association of Diabetes and Pregnancy Study Groups (IADPSG) recommendations.

· It is estimated that the number of women diagnosed with GDM, according to the new criteria, will increase four-fold (500 women a year in Oxford).

· Wireless health technology has the benefit of allowing the diabetic team to view blood glucose results and to institute an intervention between clinic visits, improving glycemic control and pregnancy outcome (lower risks of type 2 diabetes and obesity).

· This technology also has the potential to reduce the number of clinic visits by 50%, producing a significant cost saving.

Gestational Diabetes Mellitus (GDM)

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Wireless health for Gestational Diabetes

Smartphone

Android Tablet with SIM card

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Wireless health for Gestational DiabetesFeedback screen

Emphasis on relationship between pre- and post-prandial readings

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· Technology (if properly designed) is acceptable across the age spectrum (from pregnant women to elderly patients).

· Patients like the reassurance provided by wireless health (provided that a call from the nurse in the event of clinical deterioration is part of the protocol).

· Improved health outcomes can be achieved in time-limited targeted interventions (e.g. lower HbA1c in diabetes and decreased systolic blood pressure after a stroke).

· The evidence for wireless health in long-term monitoring is much less clear.

Wireless health – the first decadeWhat have we learnt?

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Long-term monitoring using wireless health

· The goals of long-term monitoring of patients with heart failure (HF) or Chronic Obstructive Pulmonary Disease (COPD) are to optimize disease management, enhance quality of life and reduce unplanned hospital admissions.

· Two recent large-scale trials of wireless health have found little or no benefit.

· UK Whole-System Demonstrator trial· Reduction in mortality in telehealth group (small numbers) but

improvement in admission rate can be accounted solely by increased admission rates in control group during first three months.

· Mayo Clinic study· No difference in primary outcome of hospitalizations and ED visits

between the telemonitoring group and the usual care group.

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Requirements for long-term monitoring Understanding patient psychology

• The need to monitor is a daily reminder, for the rest of their lives, that patients have a chronic illness for the rest of their lives.

• The “telehealth box” in the living room is a badge/symbol of illness.

Use state-of-the-art, multi-purpose tablet technology

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Android tablet for COPD patients

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Symptom diary for COPD patients

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Bluetooth finger probe for pulse oximetry

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Understanding patient psychology

· Multi-purpose tablet technology, with easy-to-use application· Adaptive symptom diary· Self-monitoring of pulse rate and oxygen saturation using pulse

oximeter (30 seconds maximum)

· Maximum information at minimal cost to the patient· The breathing rate can be acquired at no extra cost to the

patient by processing the light transmission waveform from the pulse oximeter probe (photoplethysmogram – PPG)

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Respiration modulates the PPG waveform amplitude and frequency (heart rate variability)

Amplitude Frequency

Respiratory-induced intensity variation (RIIV) is the amplitude modulation of the pulsatile PPG waveform by respiration. During inspiration, there is a reduction in tissue blood volume.

Respiratory sinus arrhythmia (RSA) is the cyclic variation of heart rate associated with respiration (heart rate variability). The vagus nerve is stimulated during expiration, which slows down the heart rate.

Resp

PPG

Breathing rate from PPG waveform

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Algorithms must be tested in target populationHeart Rate Variability in different groups

· The magnitude of Heart Rate Variability varies with age

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· The magnitude of Heart Rate Variability varies with age, and to a lesser extent with disease

Algorithms must be tested in target populationHeart Rate Variability in different groups

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Data fusion for estimating breathing ratefrom PPG waveform

Heart rate is modulated by breathing (slows down during expiration)

Amplitude modulation of light transmitted through the finger

Reference

Inter-beat Interval

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· An auto-regressive (AR) model is used to model the time series extracted from the PPG waveform.

· The poles of this model correspond to the spectral peaks in the windowed time series.

· Select pole with highest magnitude from all candidate models (both AM and RSA). Angle of this pole gives breathing rate.

Data fusion for estimating breathing ratefrom PPG waveform

AM/RSAWaveformExtraction

PPG

Bank of Band-pass

Filters

(0.1-0.6 Hz)

Multiple ARmodels

Estimation of most likelyrespiratory

rate

Maximum information at minimal cost to the patientNext-generation data collection: “Healthskype”

• Measurement of light reflected from a region of interest on the human face (e.g. forehead) using a simple webcam and ambient light can give real-time values of heart rate, breathing rate and oxygen levels

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• The blood volume reflectance signal in the Red, Green and Blue (R, G, B) channels of the camera is masked by artificial light “interference” which dominates the spectrum in all three bands.

Non-contact vital sign monitoring

• The 50 Hz frequency component is aliased down to (varying) frequencies close to the heart rate (sampling rate of the camera is between 12 and 25 Hz).

• Solution: use auto-regressive (AR) models to model both the region of interest (forehead) and the background and cancel the poles corresponding to the light interference.

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Pole cancellation for removalof ambient light interference

Frequency-domain representation z-domain representation

Background

Forehead

After pole cancellation

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Non-contact vital sign monitoringGreen channel time series and AR model

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Non-contact vital sign monitoringValidation study in Oxford Kidney Unit

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Heart rate, breathing rate and SpO2 estimationPatient with Obstructive Sleep Apnoea

Good correlation between the camera estimates (in red) and reference values of HR, BR and SpO2 (in black) – patients double monitored

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Understanding patient psychology

· Use state-of-the-art, multi-purpose tablet technology (with Bluetooth sensors or webcam)

· Maximum information at minimal cost to the patient

· “Episodic monitoring” (a combination of low-level background monitoring with occasional, more intensive monitoring)

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Understanding patient psychologyEpisodic monitoring

· Adaptive symptom/quality of life diaries· Daily measurements (e.g. weight in heart failure) as well

as weekly measurements (e.g. blood pressure)

· Occasional sleep studies using wearable patch

Courtesy of Proteus Digital Health

· Adaptive symptom/quality of life diaries· Daily measurements (e.g. weight in heart failure) as well

as weekly measurements (e.g. blood pressure)· Regular feedback to encourage self-monitoring (use of

personalized text messages or videos)

Understanding patient psychologyLong-term monitoring

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Understanding patient physiology

· Disease management also requires alerting to detect patient deterioration.

· Global thresholds (e.g. 92% for SpO2 for COPD patients) generate many false alerts.

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Understanding patient physiology

Learn patient physiology in an open-loop phase (typically four weeks) before switching patient-specific alerting on.

· Disease management also requires alerting to detect patient deterioration.

· Global thresholds (e.g. 92% for SpO2 for COPD patients) generate many false alerts.

· Alerting algorithms need to learn individual patient variability.

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· Targeted interventions using wireless health deliver improved patient outcomes (e.g. lower HbA1c in diabetes and decreased systolic blood pressure after a stroke).

Requirements for long-term monitoring· State-of-the art, multi-purpose tablet technology· Maximum information at minimal cost to the patient

· Adaptive diaries· Signal processing and data fusion to extract clinically useful information

from patient data· “Healthskype”?

· Patient-specific models for reliable alerting

Delivering improved outcomes for chronic disease patientsConclusions

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Extra slides

Definition: Carbohydrate intolerance resulting in hyperglycemia of variable severity with onset or first recognition during pregnancy1

Instantaneous effect2:

1. WHO and Dept.of Noncommunicable Disease Surveillance: Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO consultation. Part 1: diagnosis and classification of diabetes mellitus. Geneva,1999.

2. P. Tenzer-Iglesias et al: Managing postprandial glucose levels in patients with diabetes. Journal of Family Practice, vol. 57, no. 1 suppl: S17-S24, 2008

Long-term effect:• Raised HbA1c

• Fetal and maternal complications

• Long-term risks of type 2 diabetes and obesity

Gestational Diabetes Mellitus (GDM)

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Amplitude modulation of photoplethysmographic (PPG)

waveform by respiration

· During inspiration, there is a reduction in tissue blood volume as a result of two distinct mechanisms:

· reduction in cardiac output causing a reduction in arterial blood flow and, therefore, tissue perfusion;

· reduction in intra-thoracic pressure transmitted through the venous system.

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· Under resting conditions, the heart rate of a healthy individual is not constant.

·

· During expiration, the vagus nerve (which innervates the sino-atrial node) is stimulated, which slows down the heart rate.

· This gives rise to a phenomenon known as respiratory sinus arrhythmia (RSA); cardio-acceleration during inspiration, cardio-deceleration during expiration.

· RSA, the change in heart rate during the breathing cycle, provides another means of estimating the respiratory rate from the PPG waveform.

Frequency modulation of PPG waveform by respiration Respiratory Sinus Arrhythmia

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PPG peak

detection

Interpolated and filtered RSA waveform

RSA waveform

Referencesignal

Interpolated and filtered PPA waveform

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Data fusion for estimating respiratory ratefrom PPG waveform

49The pole with the highest magnitude is selected. Its angle θ gives the respiratory frequency.

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θ

Pole-zero plot for AR models(one for AM and one for RSA)

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Fit AR models to consecutive 20-second windows in the time series, for both ROIr and ROIs .

Identify the poles (peaks in frequency spectrum) in ROIr which are the poles corresponding to the ambient light interference and find the identical poles in ROIs so that they can be cancelled in ROIs.

p

kk neknxanx

1

)()()(

Pole cancellation for removalof ambient light interference

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Data fusion can identify trends that signify clinical deterioration before individual parameters would generate an alert.

Data fusion technology previously developed for monitoring jet engines

Heart rateHeart rate

Respiratory rateRespiratory rate

Oxygen saturationOxygen saturation

Blood PressureBlood PressureTemperatureTemperature

Fusion Fusion Patient status indexPatient status index

Data fusion for early warning of patient deterioration

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Data fusion to amplify signal of interest

Physiological variables over time

Physiological variability over time