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

Carole E Aubert^{1

2}, Jeffrey L Schnipper^{3

4}, Marie Roumet⁵, Pedro Marques-Vidal⁶, Jérôme Stirnemann⁷, Andrew D Auerbach⁸, Eyal Zimlichman⁹, Sunil Kripalani¹⁰, Eduard E Vasilevskis^{11

12}, Edmondo Robinson¹³, Grant S Fletcher¹⁴, Drahomir Aujesky¹, Andreas Limacher⁵, Jacques Donzé^{1

15

4

16}

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

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

Carole E Aubert et al. Mayo Clin Proc Innov Qual Outcomes. 2020.

. 2020 Jan 14;4(1):40-49.

doi: 10.1016/j.mayocpiqo.2019.09.002. eCollection 2020 Feb.

Authors

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 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.

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 Disclaimer

Figures

**Figure**
Area area under the receiver operating characteristic curve (AUC) of the different definitions of multimorbidity to predict any 30-day hospital readmission and/or a prolonged length of stay (defined as a stay longer than or equal to the country-specific upper quartile [75%]) in the derivation cohort. A, Two or more distinct body system categories and number of health conditions. B, Two or more distinct body system categories and number of chronic health conditions. C, Number of distinct body system categories. D, Number of Clinical Classification Software categories. E, Number of health conditions. F, Number of chronic health conditions. G, Deyo-Charlson Comorbidity Index. H, Elixhauser-van-Walraven Comorbidity Index.

See this image and copyright information in PMC

Cited by

Association of systemic anticholinergic medication use and accelerated decrease in lung function in older adults.
Svensson M, Elmståhl S, Sanmartin Berglund J, Rosso A. Svensson M, et al. Sci Rep. 2024 Feb 22;14(1):4362. doi: 10.1038/s41598-024-54879-z. Sci Rep. 2024. PMID: 38388652 Free PMC article.
The management of multi-morbidity in elderly patients: Ready yet for precision medicine in intensive care?
Beil M, Flaatten H, Guidet B, Sviri S, Jung C, de Lange D, Leaver S, Fjølner J, Szczeklik W, van Heerden PV. Beil M, et al. Crit Care. 2021 Sep 10;25(1):330. doi: 10.1186/s13054-021-03750-y. Crit Care. 2021. PMID: 34507597 Free PMC article.
Profile of Chronic Comorbid Conditions and Obstetrical Complications Among Pregnant Women With Human Immunodeficiency Virus and Receiving Antiretroviral Therapy in the United States.
Venkatesh KK, Morrison L, Tuomala RE, Stek A, Read JS, Shapiro DE, Livingston EG. Venkatesh KK, et al. Clin Infect Dis. 2021 Sep 15;73(6):969-978. doi: 10.1093/cid/ciab203. Clin Infect Dis. 2021. PMID: 33768226 Free PMC article.
Network Analyses to Explore Comorbidities Among Older Adults Living With Dementia.
Quan S, Monchka BA, St John PD, Doupe MB, Turgeon M, Lix LM. Quan S, et al. J Am Geriatr Soc. 2025 Apr;73(4):1168-1178. doi: 10.1111/jgs.19336. Epub 2025 Jan 22. J Am Geriatr Soc. 2025. PMID: 39843960 Free PMC article.
Long COVID after breakthrough SARS-CoV-2 infection.
Al-Aly Z, Bowe B, Xie Y. Al-Aly Z, et al. Nat Med. 2022 Jul;28(7):1461-1467. doi: 10.1038/s41591-022-01840-0. Epub 2022 May 25. Nat Med. 2022. PMID: 35614233 Free PMC article.

See all "Cited by" articles

References

1. Clerencia-Sierra M., Calderón-Larrañaga A., Martínez-Velilla N., et al. Multimorbidity patterns in hospitalized older patients: associations among chronic diseases and geriatric syndromes. PLoS One. 2015;10(7):e0132909. - PMC - PubMed
1. Wong A., Boshuizen H.C., Schellevis F.G., Kommer G.J., Polder J.J. Longitudinal administrative data can be used to examine multimorbidity, provided false discoveries are controlled for. J Clin Epidemiol. 2011;64(10):1109–1117. - PubMed
1. Friedman B., Jiang H.J., Elixhauser A., Segal A. Hospital inpatient costs for adults with multiple chronic conditions. Med Care Res Rev. 2006;63(3):327–346. - PubMed
1. Schneider F., Kaplan V., Rodak R., Battegay E., Holzer B. Prevalence of multimorbidity in medical inpatients. Swiss Med Wkly. 2012;142:w13533. - PubMed
1. Fortin M., Stewart M., Poitras M.E., Almirall J., Maddocks H. A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med. 2012;10(2):142–151. - PMC - PubMed

LinkOut - more resources

Full Text Sources

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

Figures

Similar articles

Cited by

References

LinkOut - more resources

Full Text Sources