Dattalo Melissa, DuGoff Eva, Ronk Katie, Kennelty Korey, Gilmore-Bykovskyi Andrea, Kind Amy J
Geriatric Research, Education, and Clinical Center, Department of Veterans Affairs, William S. Middleton Memorial Veterans Affairs Hospital, Madison, Wisconsin.
Division of Geriatrics, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin.
J Am Geriatr Soc. 2017 Apr;65(4):712-720. doi: 10.1111/jgs.14539. Epub 2017 Feb 15.
To determine the extent of agreement between four commonly used definitions of multiple chronic conditions (MCCs) and compare each definition's ability to predict 30-day hospital readmissions.
Retrospective cohort study.
National Medicare claims data.
Random sample of Medicare beneficiaries discharged from the hospital from 2005 to 2009 (n = 710,609).
Baseline chronic conditions were determined for each participant using four definitions of MCC. The primary outcome was all-cause 30-day hospital readmission. Agreement between MCC definitions was measured, and sensitivities and specificities for each definition's ability to identify patients experiencing a future readmission were calculated. Logistic regression was used to assess the ability of each MCC definition to predict 30-day hospital readmission.
The sample prevalence of hospitalized Medicare beneficiaries with two or more chronic conditions ranged from 18.6% (Johns Hopkins Adjusted Clinical Groups (ACG) Case-Mix System software) to 92.9% (Medicare Chronic Condition Warehouse (CCW)). There was slight to moderate agreement (kappa = 0.03-0.44) between pair-wise combinations of MCC definitions. CCW-defined MCC was the most sensitive (sensitivity 95.4%, specificity 7.4%), and ACG-defined MCC was the most specific (sensitivity 32.7%, specificity 83.2%) predictor of being readmitted. In the fully adjusted model, the risk of readmission was higher for those with chronic condition Special Needs Plan (c-SNP)-defined MCCs (odds ratio (OR) = 1.50, 95% confidence interval (CI) = 1.47-1.52), Charlson Comorbidity Index-defined MCCs (OR = 1.45, 95% CI = 1.42-1.47), ACG-defined MCCs (OR = 1.22, 95% CI = 1.19-1.25), and CCW-defined MCCs (OR = 1.15, 95% CI = 1.11-1.19) than for those without MCCs.
MCC definitions demonstrate poor agreement and should not be used interchangeably. The two definitions with the greatest agreement (CCI, c-SNP) were also the best predictors of 30-day hospital readmissions.
确定四种常用的多重慢性病（MCC）定义之间的一致程度，并比较每种定义预测30天内再次入院的能力。
回顾性队列研究。
国家医疗保险索赔数据。
2005年至2009年从医院出院的医疗保险受益人的随机样本（n = 710,609）。
使用四种MCC定义为每个参与者确定基线慢性病。主要结局是全因30天内再次入院。测量了MCC定义之间的一致性，并计算了每种定义识别未来再次入院患者的敏感性和特异性。使用逻辑回归评估每种MCC定义预测30天内再次入院的能力。
患有两种或更多种慢性病的住院医疗保险受益人的样本患病率从18.6%（约翰霍普金斯调整临床分组（ACG）病例组合系统软件）到92.9%（医疗保险慢性病仓库（CCW））不等。MCC定义的两两组合之间存在轻度至中度一致性（kappa = 0.03 - 0.44）。CCW定义的MCC是再次入院最敏感的预测指标（敏感性95.4%，特异性7.4%），而ACG定义的MCC是最具特异性的预测指标（敏感性32.7%，特异性83.2%）。在完全调整模型中，患有慢性病特殊需求计划（c - SNP）定义的MCC、查尔森合并症指数定义的MCC、ACG定义的MCC和CCW定义的MCC的患者再次入院风险高于无MCC的患者（比值比（OR）分别为1.50，95%置信区间（CI）= 1.47 - 1.52；OR = 1.45，95% CI = 1.42 - 1.47；OR = 1.22，95% CI = 1.19 - 1.25；OR = 1.15，95% CI = 1.11 - 1.19）。
MCC定义显示出较差的一致性，不应互换使用。一致性最高的两种定义（CCI，c - SNP）也是30天内再次入院的最佳预测指标。
