A Machine Learning Approach to Management of Heart Failure Populations.
| Autor: | Jing L; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania., Ulloa Cerna AE; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania., Good CW; Heart Institute, Geisinger, Danville, Pennsylvania., Sauers NM; Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania., Schneider G; Department of Medicine, Geisinger, Danville, Pennsylvania., Hartzel DN; Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania., Leader JB; Phenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania., Kirchner HL; Department of Population Health Sciences, Geisinger, Danville, Pennsylvania., Hu Y; Department of Population Health Sciences, Geisinger, Danville, Pennsylvania., Riviello DM; Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania., Stough JV; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania., Gazes S; Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania., Haggerty A; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania., Raghunath S; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania., Carry BJ; Heart Institute, Geisinger, Danville, Pennsylvania., Haggerty CM; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Heart Institute, Geisinger, Danville, Pennsylvania., Fornwalt BK; Department of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania; Heart Institute, Geisinger, Danville, Pennsylvania; Department of Radiology, Geisinger, Danville, Pennsylvania. Electronic address: bkf@gatech.edu. |
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| Jazyk: | angličtina |
| Zdroj: | JACC. Heart failure [JACC Heart Fail] 2020 Jul; Vol. 8 (7), pp. 578-587. Date of Electronic Publication: 2020 May 06. |
| DOI: | 10.1016/j.jchf.2020.01.012 |
| Abstrakt: | Background: Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. Objectives: This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. Methods: Geisinger electronic health record data were used to train machine learning models to predict 1-year all-cause mortality in 26,971 patients with heart failure who underwent 276,819 clinical episodes. There were 26 clinical variables (demographics, laboratory test results, medications), 90 diagnostic codes, 41 electrocardiogram measurements and patterns, 44 echocardiographic measurements, and 8 evidence-based "care gaps": flu vaccine, blood pressure of <130/80 mm Hg, A Results: Machine learning models achieved areas under the receiver-operating characteristic curve (AUCs) of 0.74 to 0.77 in a split-by-year training/test scheme, with the nonlinear XGBoost model (AUC: 0.77) outperforming linear logistic regression (AUC: 0.74). Out of 13,238 currently living patients, 2,844 were predicted to die within a year, and closing all care gaps was predicted to save 231 of these lives. Prioritizing patients for intervention by using the predicted reduction in 1-year mortality risk outperformed all other priority rankings (e.g., random selection or Seattle Heart Failure risk score). Conclusions: Machine learning can be used to priority-rank patients most likely to benefit from interventions to optimize evidence-based therapies. This approach may prove useful for optimizing heart failure population health management teams within value-based payment models. (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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