Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories.
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. STUDYDESIGN:
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
United States Department of Veterans Affairs
/
Machine Learning
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Hospitalization
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Hospitals, Veterans
Type of study:
Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limits:
Aged
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Female
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Humans
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Male
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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