Siregar Sabrina, Roes Kit C B, van Straten Albert H M, Bots Michiel L, van der Graaf Yolanda, van Herwerden Lex A, Groenwold Rolf H H
Department of Cardio-Thoracic Surgery, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, the Netherlands.
Circ Cardiovasc Qual Outcomes. 2013 Jan 1;6(1):110-8. doi: 10.1161/CIRCOUTCOMES.112.968800.
Comparison of outcomes requires adequate risk adjustment for differences in patient risk and the type of intervention performed. Both unintentional and intentional misclassification (also called gaming) of risk factors might lead to incorrect benchmark results. Therefore, misclassification of risk factors should be detected. We investigated the use of statistical process control techniques to monitor the frequency of risk factors in a clinical database.
A national population-based study was performed using simulation and statistical process control. All patients who underwent cardiac surgery between January 1, 2007, and December 31, 2009, in all 16 cardiothoracic surgery centers in the Netherlands were included. Data on 46 883 consecutive cardiac surgery interventions were extracted. The expected risk factor frequencies were based on 2007 and 2008 data. Monthly frequency rates of 18 risk factors in 2009 were monitored using a Shewhart control chart, exponentially weighted moving average chart, and cumulative sum chart. Upcoding (ie, gaming) in random patients was simulated and detected in 100% of the simulations. Subtle forms of gaming, involving specifically high-risk patients, were more difficult to identify (detection rate of 44%). However, the accompanying rise in mean logistic European system for cardiac operative risk evaluation (EuroSCORE) was detected in all simulations.
Statistical process control in the form of a Shewhart control chart, exponentially weighted moving average, and cumulative sum charts provide a means to monitor changes in risk factor frequencies in a clinical database. Surveillance of the overall expected risk in addition to the separate risk factors ensures a high sensitivity to detect gaming. The use of statistical process control for risk factor surveillance is recommended.
比较治疗结果需要对患者风险差异和所实施的干预类型进行充分的风险调整。风险因素的无意和有意错误分类（也称为博弈行为）都可能导致基准结果错误。因此，应检测风险因素的错误分类情况。我们研究了使用统计过程控制技术来监测临床数据库中风险因素的出现频率。
采用模拟和统计过程控制方法进行了一项基于全国人群的研究。纳入了2007年1月1日至2009年12月31日期间在荷兰所有16家心胸外科中心接受心脏手术的所有患者。提取了46883例连续心脏手术干预的数据。预期风险因素频率基于2007年和2008年的数据。使用休哈特控制图、指数加权移动平均图和累积和图监测了2009年18个风险因素的月度出现频率。在100%的模拟中检测到了随机患者中的编码上调（即博弈行为）。涉及特定高危患者的细微博弈形式更难识别（检测率为44%）。然而，在所有模拟中均检测到了伴随的平均逻辑欧洲心脏手术风险评估系统（EuroSCORE）的升高。
休哈特控制图、指数加权移动平均图和累积和图形式的统计过程控制提供了一种监测临床数据库中风险因素频率变化的方法。除了单独的风险因素外，对总体预期风险进行监测可确保对检测博弈行为具有高敏感性。建议使用统计过程控制进行风险因素监测。
