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A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care
BACKGROUND: Methods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly. OBJECTIVES: We compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205206/ https://www.ncbi.nlm.nih.gov/pubmed/32379765 http://dx.doi.org/10.1371/journal.pone.0232017 |
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author | Li, Chih-Ying Karmarkar, Amol Kuo, Yong-Fang Mehta, Hemalkumar B. Mallinson, Trudy Haas, Allen Kumar, Amit Ottenbacher, Kenneth J. |
author_facet | Li, Chih-Ying Karmarkar, Amol Kuo, Yong-Fang Mehta, Hemalkumar B. Mallinson, Trudy Haas, Allen Kumar, Amit Ottenbacher, Kenneth J. |
author_sort | Li, Chih-Ying |
collection | PubMed |
description | BACKGROUND: Methods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly. OBJECTIVES: We compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA). RESEARCH DESIGN: Retrospective analysis of 2013–2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories. SETTING: IRF, SNF and HHA. SUBJECTS: We included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture. MEASURES: Unplanned 30-day and 90-day hospital readmission. RESULTS: For all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, p<.001), using the testing sample. CONCLUSIONS: Overall, functional staging was the optimal method to generate functional status categories to predict 30-day and 90-day hospital readmission. To facilitate clinical and scientific use, we suggest the most appropriate method to categorize functional status should be based on the strengths and weaknesses of each method. |
format | Online Article Text |
id | pubmed-7205206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72052062020-05-12 A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care Li, Chih-Ying Karmarkar, Amol Kuo, Yong-Fang Mehta, Hemalkumar B. Mallinson, Trudy Haas, Allen Kumar, Amit Ottenbacher, Kenneth J. PLoS One Research Article BACKGROUND: Methods used to categorize functional status to predict health outcomes across post-acute care settings vary significantly. OBJECTIVES: We compared three methods that categorize functional status to predict 30-day and 90-day hospital readmission across inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF) and home health agencies (HHA). RESEARCH DESIGN: Retrospective analysis of 2013–2014 Medicare claims data (N = 740,530). Data were randomly split into two subsets using a 1:1 ratio. We used half of the cohort (development subset) to develop functional status categories for three methods, and then used the rest (testing subset) to compare outcome prediction. Three methods to generate functional categories were labeled as: Method I, percentile based on proportional distribution; Method II, percentile based on change score distribution; and Method III, functional staging categories based on Rasch person strata. We used six differentiation and classification statistics to determine the optimal method of generating functional categories. SETTING: IRF, SNF and HHA. SUBJECTS: We included 130,670 (17.7%) Medicare beneficiaries with stroke, 498,576 (67.3%) with lower extremity joint replacement and 111,284 (15.0%) with hip and femur fracture. MEASURES: Unplanned 30-day and 90-day hospital readmission. RESULTS: For all impairment conditions, Method III best predicted 30-day and 90-day hospital readmission. However, we observed overlapping confidence intervals among some comparisons of three methods. The bootstrapping of 30-day and 90-day hospital readmission predictive models showed the area under curve for Method III was statistically significantly higher than both Method I and Method II (all paired-comparisons, p<.001), using the testing sample. CONCLUSIONS: Overall, functional staging was the optimal method to generate functional status categories to predict 30-day and 90-day hospital readmission. To facilitate clinical and scientific use, we suggest the most appropriate method to categorize functional status should be based on the strengths and weaknesses of each method. Public Library of Science 2020-05-07 /pmc/articles/PMC7205206/ /pubmed/32379765 http://dx.doi.org/10.1371/journal.pone.0232017 Text en © 2020 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Chih-Ying Karmarkar, Amol Kuo, Yong-Fang Mehta, Hemalkumar B. Mallinson, Trudy Haas, Allen Kumar, Amit Ottenbacher, Kenneth J. A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
title | A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
title_full | A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
title_fullStr | A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
title_full_unstemmed | A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
title_short | A comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
title_sort | comparison of three methods in categorizing functional status to predict hospital readmission across post-acute care |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205206/ https://www.ncbi.nlm.nih.gov/pubmed/32379765 http://dx.doi.org/10.1371/journal.pone.0232017 |
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