Can We Do More With Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions

Hospital readmission due to heart failure is a topic of concern for patients and hospitals alike: it is both the most frequent and expensive diagnosis for hospitalization. Therefore, accurate prediction of readmission risk while patients are still in the hospital helps to guide appropriate postdischarge interventions. As our understanding of the disease and the volume of electronic health record data both increase, the number of predictors and model-building time for predicting risk grow rapidly. This suggests a need to use methods for reducing the number of predictors without losing predictive performance. We explored and described three such methods and demonstrated their use by applying them to a real-world dataset consisting of 57 variables from health data of 1210 patients from one hospital system. We compared all models generated from predictor reduction methods against the full, 57-predictor model for predicting risk of 30-day readmissions for patients with heart failure. Our predictive performance, measured by the C-statistic, ranged from 0.630 to 0.840, while model-building time ranged from 10 minutes to 10 hours. Our final model achieved a C-statistic (0.832) comparable to the full model (0.840) in the validation cohort while using only 16 predictors and providing a 66-fold improvement in model-building time. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. This project was carried out under the joint institutional review board approval by VA Palo Alto Health Care System and Stanford University (eProtocol# 12015). Views expressed are those of the authors and not necessarily those of the Department of Veterans Affairs or other affiliated organizations. Corresponding author: Satish M. Mahajan, PhD, MStat, MEng, RN, VA Palo Alto Health Care System, Mailstop: Nursing Service, 3801 Miranda Ave, Palo Alto, CA 94304 (satish.mahajan@va.gov). Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.

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