Abstract
Objectives
To use electronic health records (EHR) to predict lifetime costs and health outcomes of patients with stable coronary artery disease (stable-CAD) stratified by their risk of future cardiovascular events, and to evaluate the cost-effectiveness of treatments targeted at these populations.
Methods
The analysis was based on 94 966 patients with stable-CAD in England between 2001 and 2010, identified in four prospectively collected, linked EHR sources. Markov modelling was used to estimate lifetime costs and quality-adjusted life years (QALYs) stratified by baseline cardiovascular risk.
Results
For the lowest risk tenth of patients with stable-CAD, predicted discounted remaining lifetime healthcare costs and QALYs were £62 210 (95% CI £33 724 to £90 043) and 12.0 (95% CI 11.5 to 12.5) years, respectively. For the highest risk tenth of the population, the equivalent costs and QALYs were £35 549 (95% CI £31 679 to £39 615) and 2.9 (95% CI 2.6 to 3.1) years, respectively. A new treatment with a hazard reduction of 20% for myocardial infarction, stroke and cardiovascular disease death and no side-effects would be cost-effective if priced below £72 per year for the lowest risk patients and £646 per year for the highest risk patients.
Conclusions
Existing EHRs may be used to estimate lifetime healthcare costs and outcomes of patients with stable-CAD. The stable-CAD model developed in this study lends itself to informing decisions about commissioning, pricing and reimbursement. At current prices, to be cost-effective some established as well as future stable-CAD treatments may require stratification by patient risk.
Citation: Miqdad Asaria, Simon Walker, Stephen Palmer Using electronic health records to predict costs and outcomes in stable coronary artery disease Heart 2016;102:755-762 doi:10.1136/heartjnl-2015-308850
Received: 15 October 2015 Revised: 11 January 2016 Accepted: 14 January 2016 Published Online First: 10 February 2016
Copyright: This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/
Acknowledgments
The authors would like to acknowledge the following people for help in understanding the CALIBER dataset and prognostic models: Eleni Rapsomaniki, Pablo Perel, Spiros Denaxas, Katja Grasic, Julie George, Owen Nicholas, Ruzan Udumyan, Gene Feder, Aroon Hingorani, Spiros Denaxas, Julie George, Emily Herrett, Dipak Kalra, Aroon Hingorani, Mike Kivimaki and Liam Smeeth. This work made use of the facilities of N8 HPC provided and funded by the N8 consortium and EPSRC (Grant No. EP/K000225/1). The Centre is coordinated by the Universities of Leeds and Manchester.
Competing interests
All authors have completed the Unified Competing Interests form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare that MA, SW, SP, CPG, ADS, MC and HH have nothing to disclose. AM reports and currently sits on one of the NICE Technology Appraisal Committees. MS received grant funding for the work reported in this paper from the National Institute for Health Research. Outside of the published work, he has received personal fees from various pharmaceutical and medical device companies some of which have products used in cardiovascular disease. AT reports personal fees from Menarini Pharmaceuticals, other from Servier, outside the submitted work. KRA reports personal fees from ABPI, Roche, Novo Nordisk, AstraZeneca, Janssen, Allergan, outside the submitted work.
