Lionel Tarassenko- Delivering Improved Outcomes for Chronic Disease Patients


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CBE, FREng, DPhil-Chair in Electrical Engineering at Oxford University and Director of the Oxford Institute of Biomedical Engineering

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  • Explain the difference in hyperglycaemia that everyone experience following a meal, and hyperglycaemiaoccuring in diabetes. Show graph of typical blood glucose values pre/post meal
  • Lionel Tarassenko- Delivering Improved Outcomes for Chronic Disease Patients

    1. 1. Wireless Health 2012Delivering improved outcomes for chronic disease patients Prof. Lionel Tarassenko PhD CBE FREng Chair of Electrical Engineering Institute of Biomedical Engineering University of Oxford 23 October 2012 1
    2. 2. Links between San Diego and OxfordProf. Shu Chien, UCSD Oxford Advisory Board Institute of Biomedical Engineering Oxford city centreQualcomm Scholarships 2
    3. 3. AcknowledgementsWireless Health group: Prof. Andrew Farmer, Dr Kazem Rahimi, DrKirsty 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) 3
    4. 4. Chronic disease in the developed worldChronic disease management accounts for 80% of the growth inhealthcare spending in the developed world in the last 50 years.Chronic diseases are health problems that require on-going managementfor years or decades (e.g. diabetes, hypertension, heart failure or ChronicObstructive Pulmonary Disease – COPD) .In the US, chronic diseases affect 130 million people, generatinghealthcare costs of approximately $1.4 trillion a year overall.There are 17.5 million people in the UK with a chronic disease (32% of thepopulation). Two-thirds of these are aged 75 or above. 4
    5. 5. Chronic disease in the UK 5
    6. 6. Wireless health architecture for chronic disease management Readings automatically Cell phone transmitted by the phoneBlood Glucose meter Feedback to patient internet Incoming data stored Data review by on secure server SERVER healthcare professional • 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 6
    7. 7. Evidence-based medicine 18 clinical studies and trials of wireless health Asthma  3 published clinical studies, 1 Randomized Controlled Trial (Asthma UK) COPD  1 trial at Bristol Royal Infirmary published in Respiratory Medicine  1 cohort study + Randomized Controlled Trial in Oxfordshire Heart Failure  1 cohort study Type 1 diabetes  1 Randomized Controlled Trial published in Diabetes Care  1 Gestational Diabetes study on-going in Oxford Type 2 diabetes  2 published clinical studies (Informatics in Primary Care)  1 study completed in Oxfordshire GP Practices Hypertension  1 trial presented at European Stroke Conferences  1 study on-going in Oxfordshire GP Practices Cystic fibrosis  1 published clinical trial Cancer  1 study published in Annals of Oncology  1 study completed at Churchill Hospital Health Economics  Whole-System Demonstrator with Department of Health 7
    8. 8. Evidence-based medicine Key results from clinical studies and trialsTargeted interventions using wireless health for short periodsof time (up to six months) deliver maximum benefit in terms ofimproved 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 8
    9. 9. Monitoring blood pressure in stroke patients 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.Patients are therefore given a BP monitorlinked via Bluetooth to a cell phone to takehome to measure their blood pressure. 9
    10. 10. Monitoring blood pressure in stroke patientsFor the last four years, all patients with acute TIAs and minor stroke in theOxford 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 witha Bluetooth BP Monitor for one to three months, depending on control.Measurements transmitted automatically in real time by the cell phone arechecked daily on a secure web page in the Stroke Unit. 10
    11. 11. Monitoring blood pressure following a TIA 79 year old female InterventionBP in outpatients: 130/70 mmHg mmHg SysBP DiasBP 1 month 11
    12. 12. Monitoring blood pressure in stroke patients Data analysis after three years 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.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 12
    13. 13. Insulin titration in Type 2 diabetesThe conversion from oral medication to insulin injections is animportant stage in the progression of the management of type 2diabetes for many patients.Insulin initiation can restore good glycemic control, reducing therisks of complications such as retinopathy or amputation.Patients are often reluctant to commence insulin treatment becauseof 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 wirelesssolution (Bluetooth BG meter + cell phone + titration protocol) wasoffered to patients wanting extra support. 13
    14. 14. Insulin titration in Type 2 diabetesPatients with type 2 diabetes recruited from 15 GeneralPractices in OxfordshireMean age = 58 years, with an average diabetes duration of6 yearsMean 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 14
    15. 15. Understanding patient psychology Insulin initiation after 3 weeks of BG monitoring 15
    16. 16. Insulin titration in Type 2 diabetes Impact on HbA1c and insulin doseA telehealth nurse working remotely 10.0 70 HbA1c (%) Insulin dose (U)reviewed results and provided phone 9.8 65advice according to a set protocol. 9.6 9.4 60Practice Nurses saw a reduction in thetime required to support insulin titration. 9.2 9.0 55Blood glucose control improved, as 8.8 50reflected by a mean decrease in 8.6HbA1c of 0.66% (P = 0.05), with the n = 23 8.4 45mean insulin dose increasing by 17 Baseline 3m 6munits (P = 0.006). • Turner J, Larsen M, Tarassenko L, Neil A and Farmer A.(A 1% drop in HbA1c leads to a 33-37% • Informatics in Primary Care (May 2009)reduction in the risk of microvascular • Larsen M, Turner J, Neil A , Farmer A. and Tarassenko L.complications.) • Journal of Telemedicine & Telecare (December 2010) 16
    17. 17. Gestational Diabetes Mellitus (GDM)In 2011, the threshold for an abnormal Fasting Blood Glucose test resultwas reduced from 7.0 mmol/l (126 mg/dl) to 5.1 mmol/l (92 mg/dl) in linewith the International Association of Diabetes and Pregnancy StudyGroups (IADPSG) recommendations.It is estimated that the number of women diagnosed with GDM, accordingto the new criteria, will increase four-fold (500 women a year in Oxford).Wireless health technology has the benefit of allowing the diabetic team toview blood glucose results and to institute an intervention between clinicvisits, improving glycemic control and pregnancy outcome (lower risks oftype 2 diabetes and obesity).This technology also has the potential to reduce the number of clinic visitsby 50%, producing a significant cost saving. 17
    18. 18. Wireless health for Gestational Diabetes Android Tablet with SIM cardSmartphone 18
    19. 19. Wireless health for Gestational Diabetes Feedback screenEmphasis on relationship between pre- and post-prandial readings 19
    20. 20. Wireless health – the first decade What have we learnt?Technology (if properly designed) is acceptable across theage 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 clinicaldeterioration is part of the protocol).Improved health outcomes can be achieved in time-limitedtargeted interventions (e.g. lower HbA1c in diabetes anddecreased systolic blood pressure after a stroke).The evidence for wireless health in long-term monitoring ismuch less clear. 20
    21. 21. Long-term monitoring using wireless healthThe goals of long-term monitoring of patients with heart failure(HF) or Chronic Obstructive Pulmonary Disease (COPD) areto optimize disease management, enhance quality of life andreduce unplanned hospital admissions.Two recent large-scale trials of wireless health have foundlittle 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. 21
    22. 22. 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 22
    23. 23. Android tablet for COPD patients 23
    24. 24. Symptom diary for COPD patients 24
    25. 25. Bluetooth finger probe for pulse oximetry 25
    26. 26. Understanding patient psychologyMulti-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) 26
    27. 27. Breathing rate from PPG waveform Respiration modulates the PPG waveform amplitude and frequency (heart rate variability) Amplitude FrequencyRespPPG Respiratory-induced intensity Respiratory sinus arrhythmia (RSA) variation (RIIV) is the amplitude is the cyclic variation of heart rate modulation of the pulsatile PPG associated with respiration (heart rate waveform by respiration. During variability). The vagus nerve is inspiration, there is a reduction stimulated during expiration, which in tissue blood volume. slows down the heart rate. 27
    28. 28. Algorithms must be tested in target population Heart Rate Variability in different groups The magnitude of Heart Rate Variability varies with age 28
    29. 29. Algorithms must be tested in target population Heart Rate Variability in different groups The magnitude of Heart Rate Variability varies with age, and to a lesser extent with disease 29
    30. 30. Data fusion for estimating breathing rate from PPG waveformAmplitude modulation of light Heart rate is modulated by breathingtransmitted through the finger (slows down during expiration) Reference Inter-beat Interval 30
    31. 31. Data fusion for estimating breathing rate from PPG waveform Bank of AM/RSA Estimation Band-pass Multiple AR Waveform of most likely PPG Filters models Extraction respiratory (0.1-0.6 Hz) rateAn auto-regressive (AR) model is used to model the timeseries extracted from the PPG waveform.The poles of this model correspond to the spectral peaks inthe windowed time series.Select pole with highest magnitude from all candidate models(both AM and RSA). Angle of this pole gives breathing rate. 31
    32. 32. Maximum information at minimal cost to the patient Next-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
    33. 33. Non-contact vital sign monitoring • 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. • Solution: use auto-regressive• The 50 Hz frequency component (AR) models to model both the is aliased down to (varying) region of interest (forehead) and frequencies close to the heart the background and cancel the rate (sampling rate of the camera poles corresponding to the light is between 12 and 25 Hz). interference. 33
    34. 34. Pole cancellation for removal of ambient light interference Background Forehead After pole cancellationFrequency-domain representation z-domain representation 34
    35. 35. Non-contact vital sign monitoringGreen channel time series and AR model 35
    36. 36. Non-contact vital sign monitoringValidation study in Oxford Kidney Unit 36
    37. 37. Heart rate, breathing rate and SpO2 estimation Patient 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 37
    38. 38. Understanding patient psychologyUse state-of-the-art, multi-purpose tablet technology (withBluetooth sensors or webcam)Maximum information at minimal cost to the patient“Episodic monitoring” (a combination of low-levelbackground monitoring with occasional, more intensivemonitoring) 38
    39. 39. Understanding patient psychology Episodic monitoringAdaptive symptom/quality of life diariesDaily measurements (e.g. weight in heart failure) as wellas weekly measurements (e.g. blood pressure) Courtesy of Proteus Digital HealthOccasional sleepstudies usingwearable patch 39
    40. 40. Understanding patient psychology Long-term monitoringAdaptive symptom/quality of life diariesDaily measurements (e.g. weight in heart failure) as wellas weekly measurements (e.g. blood pressure)Regular feedback to encourage self-monitoring (use ofpersonalized text messages or videos)
    41. 41. Understanding patient physiologyDisease management also requires alerting to detect patientdeterioration.Global thresholds (e.g. 92% for SpO2 for COPD patients)generate many false alerts. 41
    42. 42. 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. Alerting algorithms need to learn individual patient variability.Learn patient physiology in an open-loop phase (typicallyfour weeks) before switching patient-specific alerting on. 42
    43. 43. Delivering improved outcomes for chronic disease patients Conclusions 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 43
    44. 44. Extra slides 44
    45. 45. Gestational Diabetes Mellitus (GDM)Definition:Carbohydrate intolerance resulting in hyperglycemia of variableseverity with onset or first recognition during pregnancy1Instantaneous effect2: Long-term effect: • Raised HbA1c • Fetal and maternal complications • Long-term risks of type 2 diabetes and obesity 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
    46. 46. Amplitude modulation of photoplethysmographic (PPG) waveform by respirationDuring inspiration, there is a reduction in tissue blood volumeas 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. 46
    47. 47. Frequency modulation of PPG waveform by respiration Respiratory Sinus Arrhythmia 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. 47
    48. 48. Data fusion for estimating respiratory rate from PPG waveform PPG peak detectionRSA waveformInterpolatedand filteredRSA waveformInterpolatedand filteredPPA waveformReferencesignal 48
    49. 49. Pole-zero plot for AR models (one for AM and one for RSA) θThe pole with the highest magnitude is selected. Its angle θ gives the respiratory frequency. 49
    50. 50. Pole cancellation for removal of ambient light interferenceFit AR models to consecutive 20-second windows in the time series,for both ROIr and ROIs . p x ( n) a k x ( n k ) e( n ) k 1Identify the poles (peaks in frequency spectrum) in ROIr which are thepoles corresponding to the ambient light interference and find theidentical poles in ROIs so that they can be cancelled in ROIs. 50
    51. 51. Data fusion for early warning of patient deterioration Heart rate Respiratory rate Oxygen saturation Fusion Patient status index Blood Pressure TemperatureData fusion can identify trends that signify clinical deterioration beforeindividual parameters would generate an alert. Data fusion technology previously developed for monitoring jet engines 51
    52. 52. Data fusion to amplify signal of interest 52
    53. 53. Physiological variables over time
    54. 54. Physiological variability over time