NIHI Big Data in Healthcare Research Case Study

Transcription

1 NIHI Big Data in Healthcare Research Case Study Professor Rob Doughty Heart Foundation Chair of Heart Health National Institute for Health Innovation and the Dept of Medicine, University of Auckland & Green Lane Cardiovascular Service, Auckland City Hospital

2 Brian D. Ata Research Case Study

3 Brian s Father No CVD risk assessment Told he had hypertension Didn t like the beta blocker he was prescribed Had a heart attack: hospitalised, treated with aspirin, given hand written discharge summary Developed heart failure Died suddenly age 56

4 The Life of Brian Brian D. Ata Big D Formative years in a small town in NZ Moved to Auckland for work in his 20s Dabbled in smoking, binge drinking in Queen Street on Friday night and drinking sugary drinks Contributed to big data with membership of 8 different consumer rewards/loyalty programmes

5 Brian, Age 45 CVD Risk assessment Primary care PREDICT assessment Age, gender, smoking, diabetes, BP, chol/hdl, FH/ethnicity 5 year CVD risk 11% Risk communication by practice nurse (Heart Foundation training), using Heart Forecast tool

6 Patient population Electronic medical record NZ adjusted Framingham HS risk algorithm MoH patient specific outcomes: hospital admissions, deaths, drugs, labs PREDICT: electronic decision support software patient specific CVD risk factor profiles PREDICT Slide courtesy of Prof Rod Jackson, VIEW/PREDICT UoA

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8 Observed 5 yr CVD events (%) yrs yrs yrs yrs NZ adjusted Framingham score (5-yr risk, %) Slide courtesy of Prof Rod Jackson, VIEW/PREDICT UoA

9 Who to treat with what: NZGG CVD guidelines Select people for risk assessment 5 y CVD risk < 10% CVD general advice only 10-15% Specific lifestyle assessment and advice >15% or prior CVD event Intensive CVD risk managementpatient specific lifestyle advice + drug therapy

10 Daily Mail 2012

11 Daily Mail 2012

12 Brian has a heart attack 2am, 30 minutes away from Auckland Hospital Transferred straight to the catheter lab Angiogram Severe left anterior descending artery stenosis, with thrombus Interventional cardiologist primary stenting

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14 Relevance of Big Data to Brian Should thrombus in a coronary artery be aspirated prior to stenting? TASTE Trial 7244 heart attack patients in Sweden randomised to stenting or thrombus aspiration then stenting Equivalent mortality at 30 days (3% vs 2.8%) Conclusion: don t aspirate the clot

15 New Era in Trials SCAAR Swedish Coronary Angiography and Angioplasty Register Registry based RCT Online randomisation module Broad inclusion criteria, wide generalisability Rapidly conducted, at low cost

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18 2200km, 13 days, 1.5x Everest ascent 1000 Power Bars 1500 litres of Peak Fuel 2 boxes of Keywin A very large number of bananas.

19 Brian moves to the USA Brian is breathless Primary care in Sutter Health Big data analytics to detect heart failure 3.3million clinical case notes Iterative annotation refinement Detection of clinical criteria for heart failure diagnosis in EMR Byrd et al. Int J Med Inform 2013

20 Brian moves back to NZ Diagnosed with heart failure Normal heart pump function? What is Brian s prognosis?

21 Meta-Analysis Global Group in Chronic Heart Failure. EHJ 2012 Outcome for patients with heart failure with preserved or reduced pump function 31 studies, 50,991 patients Individual patient data collated Outcome: all-cause mortality Length of follow-up: 3 years

22 Meta-Analysis Global Group in Chronic Heart Failure. EHJ 2012 N (31studies) Age Women, % Medical history Hypertension Prior MI AF Diabetes Ischaemic aetiology Whole Group 50, (12) HF-PEF HF-REF Missing LVEF 10, (12) , (12) (13) Deaths 11,987 2,422 8,332 1,233

23 Meta-Analysis Global Group in Chronic Heart Failure. EHJ 2012 Death from any cause Death by LVEF Group HF-PEF HR 0.68 (95% CI 0.64, 0.71) 3 40 Mortality (%) HF-REF HF-PEF Adjusted Hazard Ratio (Mortality) 2 1 er at risk: Years 0 < Ejection Fraction (%)

24 What is Brian s Prognosis? heartfailurerisk.org

25 Big Data Big data concepts integral part of cardiovascular medicine Big data is the wrong terminology Tangible clinical benefits for patients and populations Major potential for improvements in healthcare