Evidence map›Paper›PMID 30225769›Full record

Observational studyJournal of general internal medicine2018

Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories.

Edwin S Wong, Jean Yoon, Rebecca I Piegari, Ann-Marie M Rosland, Stephan D Fihn, Evelyn T Chang

Abstract readObservational Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Edwin S WongCenter 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.
Jean YoonHealth Economics Resource Center, VA Palo Alto Healthcare System, Livermore, CA, USA.
Rebecca I PiegariOffice of Clinical Systems Development and Evaluation, Veterans Health Administration, Seattle, WA, USA.
Ann-Marie M RoslandCenter for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA.
Stephan D FihnOffice of Clinical Systems Development and Evaluation, Veterans Health Administration, Seattle, WA, USA.
Evelyn T ChangCenter for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Health Care System, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AgedFemaleFollow-Up StudiesHospitalizationHospitals, VeteransHumansMachine LearningMaleMiddle AgedProspective StudiesRisk FactorsUnited StatesUnited States Department of Veterans Affairshigh risklatent subgroupsmachine learningpatient-centered medical homerisk stratificationtrajectory

Identifiers

PMID30225769
PMCPMC6258600

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.