Your browser doesn't support javascript.
loading
Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories.
Wong, Edwin S; Yoon, Jean; Piegari, Rebecca I; Rosland, Ann-Marie M; Fihn, Stephan D; Chang, Evelyn T.
Affiliation
  • Wong ES; Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, 1660 S. Columbian Way, HSR&D MS S-152, Seattle, WA, 98108, USA. eswong@uw.edu.
  • Yoon J; Department of Health Services, University of Washington, Seattle, WA, USA. eswong@uw.edu.
  • Piegari RI; Health Economics Resource Center, VA Palo Alto Healthcare System, Livermore, CA, USA.
  • Rosland AM; Department of General Internal Medicine, UCSF School of Medicine, San Francisco, CA, USA.
  • Fihn SD; Office of Clinical Systems Development and Evaluation, Veterans Health Administration, Seattle, WA, USA.
  • Chang ET; Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA.
J Gen Intern Med ; 33(12): 2120-2126, 2018 12.
Article in En | MEDLINE | ID: mdl-30225769
ABSTRACT

OBJECTIVE:

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. STUDY

DESIGN:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: United States Department of Veterans Affairs / Machine Learning / Hospitalization / Hospitals, Veterans Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: America do norte Language: En Journal: J Gen Intern Med Journal subject: MEDICINA INTERNA Year: 2018 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: United States Department of Veterans Affairs / Machine Learning / Hospitalization / Hospitals, Veterans Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: America do norte Language: En Journal: J Gen Intern Med Journal subject: MEDICINA INTERNA Year: 2018 Type: Article Affiliation country: United States