Wittenberg Philipp
Department of Mathematics and Statistics, Helmut Schmidt University, Germany.
Stat Methods Med Res. 2022 May;31(5):779-800. doi: 10.1177/09622802211053205. Epub 2022 Mar 10.
The improvement of surgical quality and the corresponding early detection of its changes is of increasing importance. To this end, sequential monitoring procedures such as the risk-adjusted CUmulative SUM chart are frequently applied. The patient risk score population (patient mix), which considers the patients' perioperative risk, is a core component for this type of quality control chart. Consequently, it is important to be able to adapt different shapes of patient mixes and determine their impact on the monitoring scheme. This article proposes a framework for modeling the patient mix by a discrete beta-binomial and a continuous beta distribution for risk-adjusted CUSUM charts. Since the model-based approach is not limited by data availability, patient mix can be analyzed. We examine the effects on the control chart's false alarm behavior for more than 100,000 different scenarios for a cardiac surgery data set. Our study finds a negative relationship between the average risk score and the number of false alarms. The results indicate that a changing patient mix has a considerable impact and, in some cases, almost doubles the number of expected false alarms.
手术质量的提高以及对其变化的相应早期检测变得越来越重要。为此，诸如风险调整累积和图等序贯监测程序经常被应用。考虑患者围手术期风险的患者风险评分群体（患者组合）是这类质量控制图的核心组成部分。因此，能够适应不同形状的患者组合并确定它们对监测方案的影响很重要。本文提出了一个框架，用于通过离散贝塔 - 二项分布和连续贝塔分布对风险调整累积和图的患者组合进行建模。由于基于模型的方法不受数据可用性的限制，因此可以分析患者组合。我们针对一个心脏手术数据集，在超过100,000种不同情况下检查了对控制图误报行为的影响。我们的研究发现平均风险评分与误报数量之间存在负相关关系。结果表明，变化的患者组合具有相当大的影响，在某些情况下，预期误报数量几乎翻倍。
