Osnabrugge Ruben L, Speir Alan M, Head Stuart J, Jones Philip G, Ailawadi Gorav, Fonner Clifford E, Fonner Edwin, Kappetein A Pieter, Rich Jeffrey B
Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands.
Inova Heart and Vascular Institute, Fairfax, Virginia.
Ann Thorac Surg. 2014 Oct;98(4):1286-93. doi: 10.1016/j.athoracsur.2014.05.073. Epub 2014 Aug 7.
Although more than 200,000 bypass operations are performed in the United States annually, few data exist on the predictors of costs and resource use for this procedure. Questions related to clinical outcomes, costs, and resource use in coronary artery bypass grafting (CABG) were addressed.
In a multiinstitutional statewide database, patient level data from 42,839 patients undergoing isolated CABG were combined with cost data. After adjustment for cost-to-charge ratios and inflation, the association of length of stay and costs with the Society of Thoracic Surgeons-Predicted Risk of Mortality (STS-PROM) was analyzed. Patients were randomly divided into development (60%) and validation (40%) cohorts. Regression models were developed to analyze the impact of patient characteristics, comorbidities, and adverse events on postoperative length of stay and total costs.
Postoperative length of stay and total direct costs for CABG averaged 6.9 days and $38,847. Length of stay and costs increased from 5.4 days and $33,275 in the lowest-risk decile (mean STS-PROM of 0.6%) to 13.8 days and $69,122 in the highest-risk decile (mean STS-PROM 19%). Compared with adverse events, patient characteristics had little impact on length of stay and costs. on validation, the models that combined preoperative and postoperative variables explained variance better (R(2) = 0.51 for length of stay; R(2) = 0.47 for costs) and were better calibrated than the preoperative models (R(2) = 0.10 for length of stay; R(2) = 0.14 for costs).
The STS-PROM and preoperative regression models are useful for preoperative prediction of costs and length of stay for groups of patients, case-mix adjustment in hospital benchmarking, and pay for performance measures. The combined preoperative and postoperative models identify incremental costs and length of stay associated with adverse events and are more suitable for prioritizing quality improvement efforts.
尽管美国每年进行超过20万例搭桥手术，但关于该手术成本及资源使用预测因素的数据却很少。本研究探讨了冠状动脉搭桥术（CABG）中与临床结局、成本和资源使用相关的问题。
在一个多机构的全州数据库中，将42,839例接受单纯CABG患者的个体水平数据与成本数据相结合。在对成本收费比和通货膨胀进行调整后，分析了住院时间和成本与胸外科医师协会预测死亡率（STS - PROM）之间的关联。患者被随机分为开发队列（60%）和验证队列（40%）。建立回归模型以分析患者特征、合并症和不良事件对术后住院时间和总成本的影响。
CABG术后平均住院时间和总直接成本分别为6.9天和38,847美元。住院时间和成本从风险最低十分位数组的5.4天和33,275美元（平均STS - PROM为0.6%）增加到风险最高十分位数组的13.8天和69,122美元（平均STS - PROM为19%）。与不良事件相比，患者特征对住院时间和成本的影响较小。在验证时，结合术前和术后变量的模型对方差的解释更好（住院时间的R² = 0.51；成本的R² = 0.47），并且比术前模型校准得更好（住院时间的R² = 0.10；成本的R² = 0.14）。
STS - PROM和术前回归模型可用于术前预测患者群体的成本和住院时间、医院基准中的病例组合调整以及绩效付费指标。术前和术后相结合的模型可识别与不良事件相关的增量成本和住院时间，更适合于确定质量改进工作的优先级。
