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Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization
OBJECTIVE: To compare different definitions of multimorbidity to identify patients with higher health care resource utilization. PATIENTS AND METHODS: We used a multinational retrospective cohort including 147,806 medical inpatients discharged from 11 hospitals in 3 countries (United States, Switzer...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011007/ https://www.ncbi.nlm.nih.gov/pubmed/32055770 http://dx.doi.org/10.1016/j.mayocpiqo.2019.09.002 |
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author | Aubert, Carole E. Schnipper, Jeffrey L. Roumet, Marie Marques-Vidal, Pedro Stirnemann, Jérôme Auerbach, Andrew D. Zimlichman, Eyal Kripalani, Sunil Vasilevskis, Eduard E. Robinson, Edmondo Fletcher, Grant S. Aujesky, Drahomir Limacher, Andreas Donzé, Jacques |
author_facet | Aubert, Carole E. Schnipper, Jeffrey L. Roumet, Marie Marques-Vidal, Pedro Stirnemann, Jérôme Auerbach, Andrew D. Zimlichman, Eyal Kripalani, Sunil Vasilevskis, Eduard E. Robinson, Edmondo Fletcher, Grant S. Aujesky, Drahomir Limacher, Andreas Donzé, Jacques |
author_sort | Aubert, Carole E. |
collection | PubMed |
description | OBJECTIVE: To compare different definitions of multimorbidity to identify patients with higher health care resource utilization. PATIENTS AND METHODS: We used a multinational retrospective cohort including 147,806 medical inpatients discharged from 11 hospitals in 3 countries (United States, Switzerland, and Israel) between January 1, 2010, and December 31, 2011. We compared the area under the receiver operating characteristic curve (AUC) of 8 definitions of multimorbidity, based on International Classification of Diseases codes defining health conditions, the Deyo-Charlson Comorbidity Index, the Elixhauser-van Walraven Comorbidity Index, body systems, or Clinical Classification Software categories to predict 30-day hospital readmission and/or prolonged length of stay (longer than or equal to the country-specific upper quartile). We used a lower (yielding sensitivity ≥90%) and an upper (yielding specificity ≥60%) cutoff to create risk categories. RESULTS: Definitions had poor to fair discriminatory power in the derivation (AUC, 0.61-0.65) and validation cohorts (AUC, 0.64-0.71). The definitions with the highest AUC were number of (1) health conditions with involvement of 2 or more body systems, (2) body systems, (3) Clinical Classification Software categories, and (4) health conditions. At the upper cutoff, sensitivity and specificity were 65% to 79% and 50% to 53%, respectively, in the validation cohort; of the 147,806 patients, 5% to 12% (7474 to 18,008) were classified at low risk, 38% to 55% (54,484 to 81,540) at intermediate risk, and 32% to 50% (47,331 to 72,435) at high risk. CONCLUSION: Of the 8 definitions of multimorbidity, 4 had comparable discriminatory power to identify patients with higher health care resource utilization. Of these 4, the number of health conditions may represent the easiest definition to apply in clinical routine. The cutoff chosen, favoring sensitivity or specificity, should be determined depending on the aim of the definition. |
format | Online Article Text |
id | pubmed-7011007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-70110072020-02-13 Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization Aubert, Carole E. Schnipper, Jeffrey L. Roumet, Marie Marques-Vidal, Pedro Stirnemann, Jérôme Auerbach, Andrew D. Zimlichman, Eyal Kripalani, Sunil Vasilevskis, Eduard E. Robinson, Edmondo Fletcher, Grant S. Aujesky, Drahomir Limacher, Andreas Donzé, Jacques Mayo Clin Proc Innov Qual Outcomes Original Article OBJECTIVE: To compare different definitions of multimorbidity to identify patients with higher health care resource utilization. PATIENTS AND METHODS: We used a multinational retrospective cohort including 147,806 medical inpatients discharged from 11 hospitals in 3 countries (United States, Switzerland, and Israel) between January 1, 2010, and December 31, 2011. We compared the area under the receiver operating characteristic curve (AUC) of 8 definitions of multimorbidity, based on International Classification of Diseases codes defining health conditions, the Deyo-Charlson Comorbidity Index, the Elixhauser-van Walraven Comorbidity Index, body systems, or Clinical Classification Software categories to predict 30-day hospital readmission and/or prolonged length of stay (longer than or equal to the country-specific upper quartile). We used a lower (yielding sensitivity ≥90%) and an upper (yielding specificity ≥60%) cutoff to create risk categories. RESULTS: Definitions had poor to fair discriminatory power in the derivation (AUC, 0.61-0.65) and validation cohorts (AUC, 0.64-0.71). The definitions with the highest AUC were number of (1) health conditions with involvement of 2 or more body systems, (2) body systems, (3) Clinical Classification Software categories, and (4) health conditions. At the upper cutoff, sensitivity and specificity were 65% to 79% and 50% to 53%, respectively, in the validation cohort; of the 147,806 patients, 5% to 12% (7474 to 18,008) were classified at low risk, 38% to 55% (54,484 to 81,540) at intermediate risk, and 32% to 50% (47,331 to 72,435) at high risk. CONCLUSION: Of the 8 definitions of multimorbidity, 4 had comparable discriminatory power to identify patients with higher health care resource utilization. Of these 4, the number of health conditions may represent the easiest definition to apply in clinical routine. The cutoff chosen, favoring sensitivity or specificity, should be determined depending on the aim of the definition. Elsevier 2020-01-14 /pmc/articles/PMC7011007/ /pubmed/32055770 http://dx.doi.org/10.1016/j.mayocpiqo.2019.09.002 Text en © 2020 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Article Aubert, Carole E. Schnipper, Jeffrey L. Roumet, Marie Marques-Vidal, Pedro Stirnemann, Jérôme Auerbach, Andrew D. Zimlichman, Eyal Kripalani, Sunil Vasilevskis, Eduard E. Robinson, Edmondo Fletcher, Grant S. Aujesky, Drahomir Limacher, Andreas Donzé, Jacques Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization |
title | Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization |
title_full | Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization |
title_fullStr | Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization |
title_full_unstemmed | Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization |
title_short | Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization |
title_sort | best definitions of multimorbidity to identify patients with high health care resource utilization |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011007/ https://www.ncbi.nlm.nih.gov/pubmed/32055770 http://dx.doi.org/10.1016/j.mayocpiqo.2019.09.002 |
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