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- Title
Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories.
- Authors
Wong, Edwin S.; Yoon, Jean; Piegari, Rebecca I.; Rosland, Ann-Marie M.; Fihn, Stephan D.; Chang, Evelyn T.
- Abstract
<bold>Objective: </bold>Many 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.<bold>Study Design: </bold>Observational 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.<bold>Methods: </bold>Using 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.<bold>Results: </bold>The 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.<bold>Conclusions: </bold>On 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.
- Subjects
PATIENT-centered medical homes; MACHINE learning; COMORBIDITY; HOSPITAL care; SOCIODEMOGRAPHIC factors
- Publication
JGIM: Journal of General Internal Medicine, 2018, Vol 33, Issue 12, p2120
- ISSN
0884-8734
- Publication type
journal article
- DOI
10.1007/s11606-018-4653-x