A Comprehensive Index for Predicting Risk of Anemia from Patients' Diagnoses

This is the authors' abstract. We don't add key points for this paper.

This article demonstrates how time-dependent, interacting, and repeating risk factors can be used to create more accurate predictive medicine. In particular, we show how emergence of anemia can be predicted from medical history within electronic health records. We used the Veterans Affairs Informatics and Computing Infrastructure database to examine a retrospective cohort of 9,738,838 veterans over an 11-year period. Using International Clinical Diagnoses Version 9 codes organized into 25 major diagnostic categories, we measured progression of disease by examining changes in risk over time, interactions in risk of combination of diseases, and elevated risk associated with repeated hospitalization for the same diagnostic category. The maximum risk associated with each diagnostic category was used to predict anemia. The accuracy of the model was assessed using a validation cohort. Age and several diagnostic categories significantly contributed to the prediction of anemia. The largest contributors were health status ([Formula: see text] = -1075, t = -92, p < 0.000), diseases of the endocrine ([Formula: see text] = -1046, t = -87, p < 0.000), hepatobiliary ([Formula: see text] = -1043, t = -72, p < 0.000), kidney ([Formula: see text] = -1125, t = -111, p < 0.000), and respiratory systems ([Formula: see text] = -1151, t = -89, p < 0.000). The AUC for the additive model was 0.751 (confidence interval 74.95%-75.26%). The magnitude of AUC suggests that the model may assist clinicians in determining which patients are likely to develop anemia. The procedures used for examining changes in risk factors over time may also be helpful in other predictive medicine projects.