Personalising the decision for prolonged dual antiplatelet therapy: development, validation and potential impact of prognostic models for cardiovascular events and bleeding in myocardial infarction survivors.
The aim of this study is to develop models to aid the decision to prolong dual antiplatelet therapy (DAPT) that requires balancing an individual patient's potential benefits and harms.



Using population-based electronic health records (EHRs) (CALIBER, England, 2000-10), of patients evaluated 1 year after acute myocardial infarction (MI), we developed (n = 12 694 patients) and validated (n = 5613) prognostic models for cardiovascular (cardiovascular death, MI or stroke) events and three different bleeding endpoints. We applied trial effect estimates to determine potential benefits and harms of DAPT and the net clinical benefit of individuals. Prognostic models for cardiovascular events (c-index: 0.75 (95% CI: 0.74, 0.77)) and bleeding (c index 0.72 (95% CI: 0.67, 0.77)) were well calibrated: 3-year risk of cardiovascular events was 16.5% overall (5.2% in the lowest- and 46.7% in the highest-risk individuals), while for major bleeding, it was 1.7% (0.3% in the lowest- and 5.4% in the highest-risk patients). For every 10 000 patients treated per year, we estimated 249 (95% CI: 228, 269) cardiovascular events prevented and 134 (95% CI: 87, 181) major bleeding events caused in the highest-risk patients, and 28 (95% CI: 19, 37) cardiovascular events prevented and 9 (95% CI: 0, 20) major bleeding events caused in the lowest-risk patients. There was a net clinical benefit of prolonged DAPT in 63-99% patients depending on how benefits and harms were weighted.



Prognostic models for cardiovascular events and bleeding using population-based EHRs may help to personalise decisions for prolonged DAPT 1-year following acute MI.
The Farr Institute of Health Informatics Research, University College London, London, UK.
Laura
L
Pasea
The Farr Institute of Health Informatics Research, University College London, London, UK.
Sheng-Chia
SC
Chung
The Farr Institute of Health Informatics Research, University College London, London, UK.|MRC Medical Bioinformatics Centre, Leeds Institute of Biomedical and Clinical Sciences, University of Leeds, UK.
Mar
M
Pujades-Rodriguez
The Farr Institute of Health Informatics Research, University College London, London, UK.
Alireza
A
Moayyeri
The Farr Institute of Health Informatics Research, University College London, London, UK.
Spiros
S
Denaxas
Centre for Cardiovascular Science, University of Edinburgh and Royal Infirmary of Edinburgh, Edinburgh, UK.
Keith A A
KAA
Fox
Department of Medical Sciences Cardiology, Uppsala Clinical Research Centre, Uppsala University, Uppsala, Sweden.
Lars
L
Wallentin
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
Stuart J
SJ
Pocock
Bart's Heart Centre, Barts and the London National Institute for Health Research Cardiovascular Biomedical Research Unit, London, UK.
Adam
A
Timmis
The Farr Institute of Health Informatics Research, University College London, London, UK.
Amitava
A
Banerjee
The Farr Institute of Health Informatics Research, University College London, London, UK.
Riyaz
R
Patel
The Farr Institute of Health Informatics Research, University College London, London, UK.
Harry
H
Hemingway
D000368:Aged; D000066491:Clinical Decision-Making; D016757:Death, Sudden, Cardiac / Q000453:epidemiology; D004359:Drug Therapy, Combination; D005260:Female; D006470:Hemorrhage / Q000139:chemically induced*; D006801:Humans; D008297:Male; D008954:Models, Biological; D009203:Myocardial Infarction / Q000188:drug therapy* / Q000401:mortality; D010975:Platelet Aggregation Inhibitors / Q000008:administration & dosage* / Q000009:adverse effects; D057285:Precision Medicine / Q000379:methods; D020521:Stroke / Q000401:mortality; D017741:Survivors; D016896:Treatment Outcome
D010975:Platelet Aggregation Inhibitors
Bleeding; Myocardial infarction; Prognosis