Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization.

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Ressource 1Download: Mayo Clinic 2020.pdf (312.90 [Ko])
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_02162AF0CDDB
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization.
Journal
Mayo Clinic proceedings. Innovations, quality & outcomes
Author(s)
Aubert C.E., Schnipper J.L., Roumet M., Marques-Vidal P., Stirnemann J., Auerbach A.D., Zimlichman E., Kripalani S., Vasilevskis E.E., Robinson E., Fletcher G.S., Aujesky D., Limacher A., Donzé J.
ISSN
2542-4548 (Electronic)
ISSN-L
2542-4548
Publication state
Published
Issued date
02/2020
Peer-reviewed
Oui
Volume
4
Number
1
Pages
40-49
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
To compare different definitions of multimorbidity to identify patients with higher health care resource utilization.
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.
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.
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.
Keywords
AUC, area under the receiver operating characteristic curve, CCI, Chronic Condition Indicator, CCS, Clinical Classification Software, ICD, International Classification of Diseases, IQR, interquartile range, LOS, length of stay, NICE, National Institute for Health and Care Excellence, WHO, World Health Organization
Pubmed
Open Access
Yes
Create date
17/02/2020 16:50
Last modification date
31/07/2020 6:08
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