A Comprehensive Index for Predicting Risk of Anemia from Patients' Diagnoses
| Autor: | Farrokh Alemi, Matthew Tuck, John F. Shortle, Sanja Avramovic, Charles Hesdorffer |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male medicine.medical_specialty Information Systems and Management Databases Factual Anemia Risk Assessment 01 natural sciences Predictive medicine 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Risk Factors medicine Electronic Health Records Humans Medical history 0101 mathematics Veterans Affairs Retrospective Studies Aged 80 and over Models Statistical business.industry Retrospective cohort study Middle Aged medicine.disease Computer Science Applications Informatics Emergency medicine Female 030211 gastroenterology & hepatology Medical emergency Major Diagnostic Category Risk assessment business Information Systems |
| Zdroj: | Big Data. 5:42-52 |
| ISSN: | 2167-647X 2167-6461 |
| DOI: | 10.1089/big.2016.0073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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