Glance Laurent G, Dick Andrew, Osler Turner M, Li Yue, Mukamel Dana B
Department of Anesthesiology, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA.
Med Care. 2006 Apr;44(4):311-9. doi: 10.1097/01.mlr.0000204106.64619.2a.
Risk adjustment is central to the generation of health outcome report cards. It is unclear, however, whether risk adjustment should be based on standard logistic regression, fixed-effects or random-effects modeling.
The objective of this study was to determine how robust the New York State (NYS) Coronary Artery Bypass Graft (CABG) Surgery Report Card is to changes in the underlying statistical methodology.
Retrospective cohort study based on data from the NYS Cardiac Surgery Reporting System on all patient undergoing isolated CABG surgery in NYS and who were discharged between 1997 and 1999 (51,750 patients). Using the same risk factors as in the NYS models, fixed-effects and random-effects models were fitted to the NYS data. Quality outliers were identified using 1) the ratio of observed-to-expected mortality rates (O/E ratio) and confidence intervals (CIs) calculated using both parametric (Poisson distribution) and nonparametric (bootstrapping) techniques; and 2) shrinkage estimators.
At the surgeon level, the standard logistic regression model, the fixed-effects model, and the fixed-effects component of the random-effects model demonstrated near-perfect agreement on the identity of quality outliers using a quality indicator based on the O/E ratio and the Poisson distribution. Shrinkage estimators identified the fewest outliers, whereas the O/E ratios with bootstrap CI identified the greatest number of outliers. The results were similar for hospitals, except that the fixed-effects model identified more outliers than either the NYS model or the fixed-effects component of the random-effects model.
Shrinkage estimators based on random-effects models are slightly more conservative in identifying quality outliers compared with the traditional approach based on fixed-effects modeling and standard regression. Explicitly modeling surgeon provider effect (fixed-effects and random-effects models) did not significantly alter the distribution of quality outliers when compared with standard logistic regression (which does not model provider effect). Compared with the standard parametric approach, the use of a bootstrap approach to construct 95% confidence interval around the O/E ratio resulted in more providers being identified as quality outliers.
风险调整是生成健康结果报告卡的核心。然而，尚不清楚风险调整应基于标准逻辑回归、固定效应模型还是随机效应模型。
本研究的目的是确定纽约州（NYS）冠状动脉搭桥术（CABG）手术报告卡对基础统计方法变化的稳健程度。
基于纽约州心脏手术报告系统的数据进行回顾性队列研究，研究对象为1997年至1999年间在纽约州接受单纯CABG手术并出院的所有患者（51750例患者）。使用与纽约州模型相同的风险因素，对纽约州的数据拟合固定效应模型和随机效应模型。使用以下两种方法识别质量异常值：1）观察到的与预期死亡率之比（O/E比）以及使用参数（泊松分布）和非参数（自举法）技术计算的置信区间（CI）；2）收缩估计量。
在外科医生层面，标准逻辑回归模型、固定效应模型以及随机效应模型的固定效应成分，使用基于O/E比和泊松分布的质量指标，在质量异常值的识别上表现出近乎完美的一致性。收缩估计量识别出的异常值最少，而带有自举法CI的O/E比识别出的异常值最多。医院层面的结果相似，只是固定效应模型识别出的异常值比纽约州模型或随机效应模型的固定效应成分更多。
与基于固定效应建模和标准回归的传统方法相比，基于随机效应模型的收缩估计量在识别质量异常值时略显保守。与不对外科医生提供者效应进行建模的标准逻辑回归相比，明确对外科医生提供者效应进行建模（固定效应和随机效应模型）并没有显著改变质量异常值的分布。与标准参数方法相比，使用自举法构建O/E比周围的95%置信区间会使更多提供者被识别为质量异常值。
