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

Overview of attention for article published in Journal of General Internal Medicine, September 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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1 news outlet
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13 X users

Citations

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22 Dimensions

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49 Mendeley
Title
Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories
Published in
Journal of General Internal Medicine, September 2018
DOI 10.1007/s11606-018-4653-x
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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. 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 12%
Researcher 5 10%
Student > Ph. D. Student 5 10%
Student > Doctoral Student 4 8%
Student > Bachelor 3 6%
Other 8 16%
Unknown 18 37%
Readers by discipline Count As %
Medicine and Dentistry 9 18%
Social Sciences 5 10%
Nursing and Health Professions 4 8%
Mathematics 3 6%
Business, Management and Accounting 2 4%
Other 6 12%
Unknown 20 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 September 2021.
All research outputs
#2,098,922
of 24,088,270 outputs
Outputs from Journal of General Internal Medicine
#1,609
of 7,852 outputs
Outputs of similar age
#44,530
of 344,947 outputs
Outputs of similar age from Journal of General Internal Medicine
#32
of 118 outputs
Altmetric has tracked 24,088,270 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,852 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.9. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 344,947 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.