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Using item response theory with health system data to identify latent groups of patients with multiple health conditions.

Publication ,  Journal Article
Prenovost, KM; Fihn, SD; Maciejewski, ML; Nelson, K; Vijan, S; Rosland, A-M
Published in: PLoS One
2018

A critical step toward tailoring effective interventions for heterogeneous and medically complex patients is to identify clinically meaningful subgroups on the basis of their comorbid conditions. We applied Item Response Theory (IRT), a potentially useful tool to identify clinically meaningful subgroups, to characterize phenotypes within a cohort of high-risk patients. This was a retrospective cohort study using 68,400 high-risk Veteran's Health Administration (VHA) patients. Thirty-one physical and mental health diagnosis indicators based on ICD-9 codes from patients' inpatient, outpatient VHA and VA-paid community care claims. Results revealed 6 distinct subgroups of high-risk patients were identified: substance use, complex mental health, complex diabetes, liver disease, cancer with cardiovascular disease, and cancer with mental health. Multinomial analyses showed that subgroups significantly differed on demographic and utilization variables which underscored the uniqueness of the groups. Using IRT models with clinical diagnoses from electronic health records permitted identification of diagnostic constellations among otherwise undifferentiated high-risk patients. Recognizing distinct patient profiles provides a framework from which insights into medical complexity of high-risk patients can be explored and effective interventions can be tailored.

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Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2018

Volume

13

Issue

11

Start / End Page

e0206915

Location

United States

Related Subject Headings

  • Veterans
  • Retrospective Studies
  • Patient Selection
  • Multimorbidity
  • Middle Aged
  • Male
  • Humans
  • General Science & Technology
  • Female
  • Feasibility Studies
 

Citation

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ICMJE
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Prenovost, K. M., Fihn, S. D., Maciejewski, M. L., Nelson, K., Vijan, S., & Rosland, A.-M. (2018). Using item response theory with health system data to identify latent groups of patients with multiple health conditions. PLoS One, 13(11), e0206915. https://doi.org/10.1371/journal.pone.0206915
Prenovost, Katherine M., Stephan D. Fihn, Matthew L. Maciejewski, Karin Nelson, Sandeep Vijan, and Ann-Marie Rosland. “Using item response theory with health system data to identify latent groups of patients with multiple health conditions.PLoS One 13, no. 11 (2018): e0206915. https://doi.org/10.1371/journal.pone.0206915.
Prenovost KM, Fihn SD, Maciejewski ML, Nelson K, Vijan S, Rosland A-M. Using item response theory with health system data to identify latent groups of patients with multiple health conditions. PLoS One. 2018;13(11):e0206915.
Prenovost, Katherine M., et al. “Using item response theory with health system data to identify latent groups of patients with multiple health conditions.PLoS One, vol. 13, no. 11, 2018, p. e0206915. Pubmed, doi:10.1371/journal.pone.0206915.
Prenovost KM, Fihn SD, Maciejewski ML, Nelson K, Vijan S, Rosland A-M. Using item response theory with health system data to identify latent groups of patients with multiple health conditions. PLoS One. 2018;13(11):e0206915.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2018

Volume

13

Issue

11

Start / End Page

e0206915

Location

United States

Related Subject Headings

  • Veterans
  • Retrospective Studies
  • Patient Selection
  • Multimorbidity
  • Middle Aged
  • Male
  • Humans
  • General Science & Technology
  • Female
  • Feasibility Studies