Use of a matching algorithm to evaluate hospital coronary artery bypass grafting performance as an alternative to conventional risk adjustment.
Use of a matching algorithm to evaluate hospital coronary artery bypass grafting performance as an alternative to conventional risk adjustment.
复制标题
使用匹配算法评估医院冠状动脉搭桥术的表现，作为传统风险调整的替代方案。
DOI：
10.1097/01.mlr.0000252225.26917.91
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发表时间：
2007
期刊：
影响因子：
3
通讯作者：
Dick,AndrewW
中科院分区：
文献类型：
--
作者：
Glance,LaurentG;Osler,TurnerM;Mukamel,DanaB;Dick,AndrewW
背景：虽然公开报告的医院和医生的表现是努力提高医疗质量的基石，风险调整的最佳方法是unknown.Objective：我们试图评估医院质量使用匹配算法的基础上广义距离metric和比较这种方法更传统的回归为基础的approach.Design/Data Source：这是一项回顾性研究，使用纽约州（NYS）冠状动脉旁路手术报告系统（CSRS），集中在所有接受孤立的CABG手术的患者在纽约州谁在1999年出院（18,116例）。来自特定医院的患者与使用Mahalanobis距离的对照组相匹配。医院的预期死亡率以两种方式计算：（1）对照组的死亡率或（2）纽约州冠状动脉旁路移植术模型预测的死亡率。医院的观察死亡率是显着不同的预期死亡率（OE差异）被定义为质量outliers.Results：2风险调整方法不同意的离群值状态的33家医院中的4。Kappa分析证明这2种方法在识别质量离群值方面基本一致：κ= 0.61。使用这2个风险调整methods.Conclusion：匹配或回归模型的基础上的结果评估医院排名，但只有中等水平的协议医院质量的OE差异的点估计值之间有很好的协议。匹配的使用可以提高医院和医生对结果报告卡的透明度和接受度。
Background:Although public reporting of hospital and physician performance is a cornerstone of the effort to improve health care quality, the optimal approach to risk adjustment is unknown.Objective:We sought to assess hospital quality using a matching algorithm based on a generalized distance metric and to compare this approach to the more traditional regression-based approach.Design/Data Source:This was a retrospective study using the New York State (NYS) Coronary Artery Bypass Surgery Reporting System (CSRS), focusing on all patients undergoing isolated CABG surgery in NYS who were discharged in 1999 (18,116 patients). Patients from specific hospital were matched to a control group using the Mahalanobis distance. The hospitals’ expected mortality rate was calculated in 2 ways:(1) as the mortality rate of the control group or (2) as the mortality rate predicted by the NYS CABG model. Hospitals whose observed mortality rate was significantly different from their expected mortality rate (OE difference) were defined as quality outliers.Results:The 2 risk-adjustment methodologies disagreed on the outlier status of 4 of the 33 hospitals. Kappa analysis demonstrated substantial agreement between these 2 methods for identifying quality outliers: κ= 0.61. There was excellent agreement between the point estimates of the OE difference obtained using these 2 risk adjustment methodologies.Conclusion:Basing outcome assessment on either matching or regression modeling yielded similar findings on hospital ranking but only moderate level of agreement on hospital quality. The use of matching may enhance the transparency and acceptance of outcome report cards by hospitals and physicians.