ArticlePloS one2018
Using item response theory with health system data to identify latent groups of patients with multiple health conditions.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social determinants of multimorbidity patterns: A systematic review.Frontiers in public health · 2023Pooled it
- Impact of Referring High-Risk Patients to Intensive Outpatient Primary Care Services: A Propensity Score-Matched Analysis.Journal of general internal medicine · 2025Trial
- A next-generation prediction risk model for acute myocardial infarction: Derivation and validation in a multi-centre cohort.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Perceived Connections Between Personal Values and Health in High-Risk Patients with Multimorbidity: A Qualitative Study.Journal of general internal medicine · 2026Article
- A Novel Primary Care Planning Informatics Tool Informed by Data-Driven Multimorbidity Grouping: User-Centered Design and Feasibility Testing.JMIR formative research · 2025Article
- Patient-Centered Prioritization of Health Care Processes for Multimorbidity.JAMA network open · 2025Article
- Symptom and functional networks of patients with cancer in different latent risk subgroups based on patient-reported outcomes.BMC cancer · 2025Article
- Psychometric Evaluation of GHQ-12 as a Screening Tool for Psychological Impairment of Healthcare Workers Facing COVID-19 Pandemic.La Medicina del lavoro · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Multimorbidity Patterns and Their Association with Social Determinants, Mental and Physical Health during the COVID-19 Pandemic.International journal of environmental research and public health · 2022Observational
- Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report.BMC health services research · 2022Article
- Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention.Journal of general internal medicine · 2022Article
- Empiric segmentation of high-risk patients: a structured literature review.The American journal of managed care · 2022Review
- Outcomes of a randomized quality improvement trial for high-risk Veterans in year two.Health services research · 2021Article
- Optimal Examination Sites for Periodontal Disease Evaluation: Applying the Item Response Theory Graded Response Model.Journal of clinical medicine · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 cardiovascular disease, and cancer with mental health. Multinomial analyses showed that subgroups significantly differed on demographic and utilization variables which underscored the uniqueness of the groups. Using IRT models with clinical diagnoses from electronic health records permitted identification of diagnostic constellations among otherwise undifferentiated high-risk patients. Recognizing distinct patient profiles provides a framework from which insights into medical complexity of high-risk patients can be explored and effective interventions can be tailored.
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