Author + information
- Received July 10, 2019
- Revision received January 2, 2020
- Accepted January 2, 2020
- Published online June 29, 2020.
- Linyuan Jing, PhDa,
- Alvaro E. Ulloa Cerna, PhDa,
- Christopher W. Good, DOb,
- Nathan M. Sauers, PharmDc,
- Gargi Schneider, MDd,
- Dustin N. Hartzel, BSe,
- Joseph B. Leader, BAe,
- H. Lester Kirchner, PhDf,
- Yirui Hu, PhDf,
- David M. Riviello, BSg,
- Joshua V. Stough, PhDa,h,
- Seth Gazes, MSc,
- Allyson Haggerty, MBAa,
- Sushravya Raghunath, PhDa,
- Brendan J. Carry, MDb,
- Christopher M. Haggerty, PhDa,b,∗ and
- Brandon K. Fornwalt, MD, PhDa,b,i,∗∗ ()
- aDepartment of Translational Data Science and Informatics, Geisinger, Danville, Pennsylvania
- bHeart Institute, Geisinger, Danville, Pennsylvania
- cCenter for Pharmacy Innovation and Outcomes, Geisinger, Danville, Pennsylvania
- dDepartment of Medicine, Geisinger, Danville, Pennsylvania
- ePhenomic Analytics and Clinical Data Core, Geisinger, Danville, Pennsylvania
- fDepartment of Population Health Sciences, Geisinger, Danville, Pennsylvania
- gSteele Institute for Health Innovation, Geisinger, Danville, Pennsylvania
- hDepartment of Computer Science, Bucknell University, Lewisburg, Pennsylvania
- iDepartment of Radiology, Geisinger, Danville, Pennsylvania
- ↵∗Address for correspondence:
Dr. Brandon K. Fornwalt, Department of Imaging Science and Innovation, Geisinger, 100 North Academy Avenue, Danville, Pennsylvania 17822-4400.
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, A1c of <8%, cardiac resynchronization therapy, and active medications (active angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker/angiotensin receptor-neprilysin inhibitor, aldosterone receptor antagonist, hydralazine, and evidence-based beta-blocker) were collected. Care gaps represented actionable variables for which associations with all-cause mortality were modeled from retrospective data and then used to predict the benefit of prospective interventions in 13,238 currently living patients.
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.
↵∗ Drs. Haggerty and Fornwalt contributed equally to this work and are joint senior authors.
This work was supported by a Quality Fund Award from Geisinger Health Plan. Geisinger receives funding from Tempus for ongoing development of predictive modeling technology and commercialization. Tempus and Geisinger have jointly applied for a patent related to the work. None of the Geisinger authors has ownership interest in any of the intellectual property resulting from the partnership. Dr. Cleland acknowledges support from the British Heart Foundation for Centre of Research Excellence (grant number RE/18/6/34217). The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the JACC: Heart Failure author instructions page.
- Received July 10, 2019.
- Revision received January 2, 2020.
- Accepted January 2, 2020.
- 2020 American College of Cardiology Foundation
This article requires a subscription or purchase to view the full text. If you are a subscriber or member, click Login or the Subscribe link (top menu above) to access this article.