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Aubert CE, Schnipper JL, Roumet M, et al. Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization. Mayo Clin Proc Innov Qual Outcomes. 2020;4(1):40-49doi: 10.1016/j.mayocpiqo.2019.09.002.
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. (2020). Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization. Mayo Clinic proceedings. Innovations, quality & outcomes, 4(1), 40-49. https://doi.org/10.1016/j.mayocpiqo.2019.09.002
Aubert, Carole E, et al. "Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization." Mayo Clinic proceedings. Innovations, quality & outcomes vol. 4,1 (2020): 40-49. doi: https://doi.org/10.1016/j.mayocpiqo.2019.09.002
Aubert CE, Schnipper JL, Roumet M, Marques-Vidal P, Stirnemann J, Auerbach AD, Zimlichman E, Kripalani S, Vasilevskis EE, Robinson E, Fletcher GS, Aujesky D, Limacher A, Donzé J. Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization. Mayo Clin Proc Innov Qual Outcomes. 2020 Jan 14;4(1):40-49. doi: 10.1016/j.mayocpiqo.2019.09.002. eCollection 2020 Feb. PMID: 32055770; PMCID: PMC7011007.
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Mayo Clin Proc Innov Qual Outcomes. 2020 Jan 14;4(1):40-49. doi: 10.1016/j.mayocpiqo.2019.09.002. eCollection 2020 Feb.

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

Mayo Clinic proceedings. Innovations, quality & outcomes

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

Affiliations

  1. Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland.
  2. Institute of Primary Health Care (BIHAM), University of Bern, Switzerland.
  3. BWH Hospitalist Service, Division of General Medicine, Brigham and Women's Hospital, Boston, MA.
  4. Harvard Medical School, Boston, MA.
  5. CTU Bern and Institute of Social and Preventive Medicine, University of Bern, Switzerland.
  6. Department of Internal Medicine, Lausanne University Hospital, Switzerland.
  7. Department of Internal Medicine, Geneva University Hospital, Switzerland.
  8. Division of Hospital Medicine, University of California, San Francisco, Sheba Medical Center, Tel HaShomer, Israel.
  9. Sheba Medical Center, Tel HaShomer, Israel.
  10. Section of Hospital Medicine, Division of General Internal Medicine and Public Health and Center for Clinical Quality and Implementation Research, Vanderbilt University, Nashville, TN.
  11. Section of Hospital Medicine, Vanderbilt University Medical Center, Nashville, TN.
  12. Geriatric Research Education and Clinical Center, VA Tennessee Valley, Nashville.
  13. Christiana Care Health System, Wilmington, DE.
  14. Department of Medicine, Harborview Medical Center, University of Washington, Seattle.
  15. Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA.
  16. Department of Internal Medicine, Hôpital neuchâtelois, Neuchâtel, Switzerland.

PMID: 32055770 PMCID: PMC7011007 DOI: 10.1016/j.mayocpiqo.2019.09.002

Abstract

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

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.

© 2019 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc.

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

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