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

Carole E. Aubert, MD; Jeffrey L. Schnipper, MD, MPH; Marie Roumet, PhD; Pedro Marques-Vidal, MD, PhD; Jérôme Stirnemann, MD, PhD; Andrew D. Auerbach, MD, MPH; Eyal Zimlichman, MD, MSc; Sunil Kripalani, MD, MSc; Eduard E. Vasilevskis, MD, MPH, FHM; Edmondo Robinson, MD, MBA, MSHP; Grant S. Fletcher, MD, MPH; Drahomir Aujesky, MD, MSc; Andreas Limacher, PhD; Jacques Donzé, MD, MSc

Mayo Clinic Proceedings: Innovations, Quality & Outcomes (Feb 2020)

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

Carole E. Aubert, MD,
Jeffrey L. Schnipper, MD, MPH,
Marie Roumet, PhD,
Pedro Marques-Vidal, MD, PhD,
Jérôme Stirnemann, MD, PhD,
Andrew D. Auerbach, MD, MPH,
Eyal Zimlichman, MD, MSc,
Sunil Kripalani, MD, MSc,
Eduard E. Vasilevskis, MD, MPH, FHM,
Edmondo Robinson, MD, MBA, MSHP,
Grant S. Fletcher, MD, MPH,
Drahomir Aujesky, MD, MSc,
Andreas Limacher, PhD,
Jacques Donzé, MD, MSc

Affiliations

Carole E. Aubert, MD: Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland; Institute of Primary Health Care (BIHAM), University of Bern, Switzerland; Correspondence: Address to Carole E. Aubert, MD, Department of General Internal Medicine, Inselspital, Bern University Hospital, Freiburgstrasse, 3010 Bern, Switzerland.
Jeffrey L. Schnipper, MD, MPH: BWH Hospitalist Service, Division of General Medicine, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA
Marie Roumet, PhD: CTU Bern and Institute of Social and Preventive Medicine, University of Bern, Switzerland
Pedro Marques-Vidal, MD, PhD: Department of Internal Medicine, Lausanne University Hospital, Switzerland
Jérôme Stirnemann, MD, PhD: Department of Internal Medicine, Geneva University Hospital, Switzerland
Andrew D. Auerbach, MD, MPH: Division of Hospital Medicine, University of California, San Francisco, Sheba Medical Center, Tel HaShomer, Israel
Eyal Zimlichman, MD, MSc: Sheba Medical Center, Tel HaShomer, Israel
Sunil Kripalani, MD, MSc: Section of Hospital Medicine, Division of General Internal Medicine and Public Health and Center for Clinical Quality and Implementation Research, Vanderbilt University, Nashville, TN
Eduard E. Vasilevskis, MD, MPH, FHM: Section of Hospital Medicine, Vanderbilt University Medical Center, Nashville, TN; Geriatric Research Education and Clinical Center, VA Tennessee Valley, Nashville
Edmondo Robinson, MD, MBA, MSHP: Christiana Care Health System, Wilmington, DE
Grant S. Fletcher, MD, MPH: Department of Medicine, Harborview Medical Center, University of Washington, Seattle
Drahomir Aujesky, MD, MSc: Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
Andreas Limacher, PhD: CTU Bern and Institute of Social and Preventive Medicine, University of Bern, Switzerland
Jacques Donzé, MD, MSc: Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland; Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA; Department of Internal Medicine, Hôpital neuchâtelois, Neuchâtel, Switzerland

Journal volume & issue: Vol. 4, no. 1
pp. 40 – 49

Abstract

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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.

Published in Mayo Clinic Proceedings: Innovations, Quality & Outcomes

ISSN: 2542-4548 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Medicine (General)
Website: https://www.journals.elsevier.com/mayo-clinic-proceedings-innovations-quality-and-outcomes/

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