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
Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Institute of Primary Health Care (BIHAM), University of Bern, Switzerland.
Mayo Clin Proc Innov Qual Outcomes. 2020 Jan 14;4(1):40-49. doi: 10.1016/j.mayocpiqo.2019.09.002. eCollection 2020 Feb.
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 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.
比较多种合并症的不同定义，以识别医疗资源利用率较高的患者。
我们使用了一个跨国回顾性队列，纳入了2010年1月1日至2011年12月31日期间从3个国家（美国、瑞士和以色列）的11家医院出院的147,806名内科住院患者。我们比较了基于健康状况编码、Deyo-Charlson合并症指数、Elixhauser-van Walraven合并症指数、身体系统或临床分类软件类别对30天再入院和/或住院时间延长（大于或等于特定国家的上四分位数）进行预测的8种合并症定义的受试者工作特征曲线下面积（AUC）。我们使用较低（灵敏度≥90%）和较高（特异度≥60%）的截断值来创建风险类别。
在推导队列（AUC，0.61 - 0.65）和验证队列（AUC，0.64 - 0.71）中，各定义的鉴别能力从差到一般。AUC最高的定义是：（1）涉及2个或更多身体系统的健康状况数量、（2）身体系统、（3）临床分类软件类别以及（4）健康状况数量。在较高截断值时，验证队列中的灵敏度和特异度分别为65%至79%和50%至53%；在147,806名患者中，5%至12%（7474至18,008）被分类为低风险，38%至55%（54,484至81,540）为中风险，32%至50%（47,331至72,435）为高风险。
在8种合并症定义中，4种在识别医疗资源利用率较高的患者方面具有相当的鉴别能力。在这4种定义中，健康状况数量可能是临床常规应用中最容易的定义。应根据定义的目的来确定选择倾向于灵敏度还是特异度的截断值。
