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 (2020). Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization. Mayo Clinic Proceedings: Innovations, Quality and Outcomes, 4(1), pp. 40-49. Elsevier 10.1016/j.mayocpiqo.2019.09.002

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM)
04 Faculty of Medicine > Pre-clinic Human Medicine > CTU Bern
04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine
04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine > Centre of Competence for General Internal Medicine

UniBE Contributor:

Aubert, Carole Elodie; Roumet, Marie Camille; Aujesky, Drahomir; Limacher, Andreas and Donzé, Jacques

Subjects:

300 Social sciences, sociology & anthropology > 360 Social problems & social services
600 Technology > 610 Medicine & health

ISSN:

2542-4548

Publisher:

Elsevier

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

25 Feb 2020 12:45

Last Modified:

26 Feb 2020 09:41

Publisher DOI:

10.1016/j.mayocpiqo.2019.09.002

PubMed ID:

32055770

Uncontrolled 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

BORIS DOI:

10.7892/boris.140685

URI:

https://boris.unibe.ch/id/eprint/140685

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