Using item response theory with health system data to identify latent groups of patients with multiple health conditions.

ABSTRACT: A critical step toward tailoring effective interventions for heterogeneous and medically complex patients is to identify clinically meaningful subgroups on the basis of their comorbid conditions. We applied Item Response Theory (IRT), a potentially useful tool to identify clinically meaningful subgroups, to characterize phenotypes within a cohort of high-risk patients. This was a retrospective cohort study using 68,400 high-risk Veteran's Health Administration (VHA) patients. Thirty-one physical and mental health diagnosis indicators based on ICD-9 codes from patients' inpatient, outpatient VHA and VA-paid community care claims. Results revealed 6 distinct subgroups of high-risk patients were identified: substance use, complex mental health, complex diabetes, liver disease, cancer with car

SUBMITTER: Prenovost KM 

PROVIDER: S-EPMC6261016 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature