Centre for Healthcare Related Infection Surveillance and Prevention, Queensland Health, Herston, QLD 4006, Australia.
Int J Qual Health Care. 2012 Apr;24(2):176-81. doi: 10.1093/intqhc/mzr082. Epub 2011 Dec 21.
Risk-adjusted control charts have become popular for monitoring processes that involve the management and treatment of patients in hospitals or other healthcare institutions. However, to date, the effect of estimation error on risk-adjusted control charts has not been studied.
We studied the effect of estimation error on risk-adjusted binary cumulative sum (CUSUM) performance using actual and simulated data on patients undergoing coronary artery bypass surgery and assessed for mortality up to 30 days post-surgery. The effect of estimation error was indicated by the variability of the 'true' average run lengths (ARLs) obtained using repeated sampling of the observed data under various realistic scenarios.
Results showed that estimation error can have a substantial effect on risk-adjusted CUSUM chart performance in terms of variation of true ARLs. Moreover, the performance was highly dependent on the number of events used to derive the control chart parameters and the specified ARL for an in-control process (ARL(0)). However, the results suggest that it is the uncertainty in the overall adverse event rate that is the main component of estimation error.
When designing a control chart, the effect of estimation error could be taken into account by generating a number of bootstrap samples of the available Phase I data and then determining the control limit needed to obtain an ARL(0) of a pre-specified level 95% of the time. If limited Phase I data are available, it may be advisable to continue to update model parameters even after prospective patient monitoring is implemented.
风险调整控制图已成为监测医院或其他医疗机构中患者管理和治疗过程的热门工具。然而，迄今为止，估计误差对风险调整控制图的影响尚未得到研究。
我们使用接受冠状动脉旁路移植术患者的实际和模拟数据研究了估计误差对风险调整二项累积和（CUSUM）性能的影响，并评估了术后 30 天内的死亡率。通过在各种现实情况下重复抽样观察数据来获得“真实”平均运行长度（ARL）的变异性来指示估计误差的影响。
结果表明，估计误差会对风险调整 CUSUM 图表性能产生重大影响，表现在真实 ARL 的变化上。此外，性能高度依赖于用于推导控制图表参数的事件数量以及规定的控制过程 ARL（ARL(0)）。但是，结果表明，估计误差的主要组成部分是总体不良事件率的不确定性。
在设计控制图时，可以通过生成可用 Phase I 数据的多个引导样本，并确定获得预定义水平 95%的 ARL(0)所需的控制限来考虑估计误差的影响。如果 Phase I 数据有限，在实施前瞻性患者监测后继续更新模型参数可能是明智的。
