Using item response theory with health system data to identify latent groups of patients with multiple health conditions.
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 subgr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6261016?pdf=render |
| _version_ | 1818618892896960512 |
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| author | Katherine M Prenovost Stephan D Fihn Matthew L Maciejewski Karin Nelson Sandeep Vijan Ann-Marie Rosland |
| author_facet | Katherine M Prenovost Stephan D Fihn Matthew L Maciejewski Karin Nelson Sandeep Vijan Ann-Marie Rosland |
| author_sort | Katherine M Prenovost |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T17:28:49Z |
| format | Article |
| id | doaj.art-f6abd5c7b00b4c12845d5787b98e5002 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T17:28:49Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f6abd5c7b00b4c12845d5787b98e50022022-12-21T22:22:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011311e020691510.1371/journal.pone.0206915Using item response theory with health system data to identify latent groups of patients with multiple health conditions.Katherine M PrenovostStephan D FihnMatthew L MaciejewskiKarin NelsonSandeep VijanAnn-Marie RoslandA 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.http://europepmc.org/articles/PMC6261016?pdf=render |
| spellingShingle | Katherine M Prenovost Stephan D Fihn Matthew L Maciejewski Karin Nelson Sandeep Vijan Ann-Marie Rosland Using item response theory with health system data to identify latent groups of patients with multiple health conditions. PLoS ONE |
| title | Using item response theory with health system data to identify latent groups of patients with multiple health conditions. |
| title_full | Using item response theory with health system data to identify latent groups of patients with multiple health conditions. |
| title_fullStr | Using item response theory with health system data to identify latent groups of patients with multiple health conditions. |
| title_full_unstemmed | Using item response theory with health system data to identify latent groups of patients with multiple health conditions. |
| title_short | Using item response theory with health system data to identify latent groups of patients with multiple health conditions. |
| title_sort | using item response theory with health system data to identify latent groups of patients with multiple health conditions |
| url | http://europepmc.org/articles/PMC6261016?pdf=render |
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