Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model

• Published in: PloS one Vol. 16; no. 12; p. e0260885
• Main Authors: Duong, Son Q., Zheng, Le, Xia, Minjie, Jin, Bo, Liu, Modi, Li, Zhen, Hao, Shiying, Alfreds, Shaun T., Sylvester, Karl G., Widen, Eric, Teuteberg, Jeffery J., McElhinney, Doff B., Ling, Xuefeng B.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.12.2021; Public Library of Science (PLoS)
• Subjects: Aged; Algorithms; Biology and Life Sciences; Care and treatment; Clinical outcomes; Codes; Computer and Information Sciences; Congestive heart failure; Data exchange; Data Mining; Decision support systems; Decision Support Systems, Clinical; Diabetes; Diagnosis; Early Diagnosis; Female; Health care; Health Information Exchange; Health risk assessment; Health risks; Heart failure; Heart Failure - diagnosis; Heart Failure - epidemiology; Heart surgery; Hospitals; Humans; Incidence; Laboratories; Learning algorithms; Machine learning; Maine - epidemiology; Male; Medical care; Medical prognosis; Medicine; Medicine and Health Sciences; Medicine, Preventive; Methods; Middle Aged; Modelling; Models, Statistical; Patients; People and places; Prediction models; Preventive health services; Prognosis; Prospective Studies; Resource allocation; Risk; Risk factors; Statistical analysis; Supervised Machine Learning; Utilization; Maine; United States; Palo Alto California; California; United States > US; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0260885

New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE. We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified. Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.