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An integrated epidemiological and neural net model of the warfarin effect in managed care patients

Authors Jacobs DM, Stefanovic F, Wilton G, Gomez-Caminero A, Schentag JJ

Received 6 March 2017

Accepted for publication 13 April 2017

Published 18 May 2017 Volume 2017:9 Pages 55—64


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Professor Arthur Frankel

David M Jacobs,1,2,* Filip Stefanovic,3,* Greg Wilton,2 Andres Gomez-Caminero,4 Jerome J Schentag1,2

1Department of Pharmacy Practice, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, 2CPL Associates LLC, 3Department of Biomedical Engineering, University at Buffalo School of Engineering and Applied Sciences, Buffalo, NY, 4Global Pharmacovigilance and Epidemiology, Bristol Myers Squibb, Princeton, NJ, USA

*These authors contributed equally to this work.

Introduction: Risk assessment tools are utilized to estimate the risk for stroke and need of anticoagulation therapy for patients with atrial fibrillation (AF). These risk stratification scores are limited by the information inputted into them and a reliance on time-independent variables. The objective of this study was to develop a time-dependent neural net model to identify AF populations at high risk of poor clinical outcomes and evaluate the discriminatory ability of the model in a managed care population.
Methods: We performed a longitudinal, cohort study within a health-maintenance organization from 1997 to 2008. Participants were identified with incident AF irrespective of warfarin status and followed through their duration within the database. Three clinical outcome measures were evaluated including stroke, myocardial infarction, and hemorrhage. A neural net model was developed to identify patients at high risk of clinical events and defined to be an “enriched” patient. The model defines the enrichment based on the top 10 minimum mean square error output parameters that describe the three clinical outcomes. Cox proportional hazard models were utilized to evaluate the outcome measures.
Results: Among 285 patients, the mean age was 74±12 years with a mean follow-up of 4.3±2.6 years, and 154 (54%) were treated with warfarin. After propensity score adjustment, warfarin use was associated with a slightly increased risk of adverse outcomes (including stroke, myocardial infarction, and hemorrhage), though it did not attain statistical significance (adjusted hazard ratio [aHR] =1.22; 95% confidence interval [CI] 0.75–1.97; p=0.42). Within the neural net model, subjects at high risk of adverse outcomes were identified and labeled as “enriched.” Following propensity score adjustment, enriched subjects were associated with an 81% higher risk of adverse outcomes as compared to nonenriched subjects (aHR=1.81; 95% CI, 1.15–2.88; p=0.01).
Conclusion: Enrichment methodology improves the statistical discrimination of meaningful endpoints when used in a health records-based analysis.

Keywords: atrial fibrillation, neural net model, warfarin, epidemiology

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