Observational studyJournal of general internal medicine2018
Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories.
Observational study in Journal of general internal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Quality Improvement Intervention to Increase Sleep Apnea Diagnostic Testing After Stroke and Transient Ischemic Attack: A Cluster Randomized Trial.JAMA network open · 2025Trial
- Evaluation of Hybrid-Virtual Home Visits for Comprehensive Geriatric Management: A Quality Improvement Project.Journal of general internal medicine · 2026Article
- Empirical vulnerability strata in under-five mortality in Cameroon: Evidence from DHS data.SSM - population health · 2026Article
- Risk Factors for Pressure Injuries and Injury Types Among Inpatients in Multi-Centre Military Hospitals: A Factor Analysis Study.International wound journal · 2025Article
- Life-sustaining treatment decisions and family evaluations of end-of-life care for Veteran decedents in Department of Veterans Affairs nursing homes.Journal of the American Geriatrics Society · 2024Article
- Expanding Access to Comprehensive Geriatric Evaluation via Telehealth: Development of Hybrid-Virtual Home Visits.Journal of general internal medicine · 2024Article
- Variation in benefit among patients with serious mental illness who receive integrated psychiatric and primary care.PloS one · 2024Article
- Characterizing hospitalization trajectories in the high-need, high-cost population using electronic health record data.Health affairs scholar · 2023Article
- A scoping review of the clinical application of machine learning in data-driven population segmentation analysis.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report.BMC health services research · 2022Article
- Unpacking complex interventions that manage care for high-need, high-cost patients: a realist review.BMJ open · 2022Review
- Family members' experience improves with care preference documentation in home based primary care.Journal of the American Geriatrics Society · 2021Article
- High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program.BMC health services research · 2021Article
- Outcomes of a randomized quality improvement trial for high-risk Veterans in year two.Health services research · 2021Article
- Preoperative Care Assessment of Need Scores Are Associated With Postoperative Mortality and Length of Stay in Veterans Undergoing Knee Replacement.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2021Article
- Article
- Life-Sustaining Treatment Decisions Initiative: Early Implementation Results of a National Veterans Affairs Program to Honor Veterans' Care Preferences.Journal of general internal medicine · 2020Observational
- High-Need Phenotypes in Medicare Beneficiaries: Drivers of Variation in Utilization and Outcomes.Journal of the American Geriatrics Society · 2020Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMany healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories. STUDY
designObservational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization.
methodsUsing VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups.
resultsThe best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services.
conclusionsOn average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.
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