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Methods Inf Med 2020; 59(02/03): 061-074
DOI: 10.1055/s-0040-1713905
Original Article

Predicting Lipid-Lowering Medication Persistence after the First Cardiovascular Disease Hospitalization

Feiyu Hu
1   School of Computer Science, University of Auckland, Auckland, New Zealand
,
Jim Warren
1   School of Computer Science, University of Auckland, Auckland, New Zealand
,
Daniel J. Exeter
2   School of Population Health, University of Auckland, Auckland, New Zealand
› Author Affiliations Funding This work was supported in part by New Zealand Health Research Council programme grant HRC16-609, “Vascular Informatics and Epidemiology using the Web 2020 (VIEW 2020)” and by scholarship funding from the Precision Driven Health research partnership. We thank the entire VIEW 2020 team for their support and feedback.
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Abstract

Objectives This study analyzed patient factors in medication persistence after discharge from the first hospitalization for cardiovascular disease (CVD) with the aim of predicting persistence to lipid-lowering therapy for 1 to 2 years.

Methods A subcohort having a first CVD hospitalization was selected from 313,207 patients for proportional hazard model analysis. Logistic regression, support vector machine, artificial neural networks, and boosted regression tree (BRT) models were used to predict 1- and 2-year medication persistence.

Results Proportional hazard modeling found significant association of persistence with age, diabetes history, complication and comorbidity level, days stayed in hospital, CVD diagnosis type, in-patient procedures, and being new to therapy. BRT had the best predictive performance with c-statistic of 0.811 (0.799–0.824) for 1-year and 0.793 (0.772–0.814) for 2-year prediction using variables potentially available shortly after discharge.

Conclusion The results suggest that development of a machine learning-based clinical decision support tool to focus improvements in secondary prevention of CVD is feasible.

Keywords

medication persistence - cardiovascular diseases - machine learning - prediction models


Publication History

Received: 05 May 2019

Accepted: 20 May 2020

Article published online:
29 July 2020

Georg Thieme Verlag KG
Stuttgart · New York

 

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