Center for Surgical Trials and Outcomes Research, Department of Surgery, The Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
J Trauma Acute Care Surg. 2013 Jul;75(1):166-72. doi: 10.1097/ta.0b013e318298494f.
Currently, trauma center quality benchmarking is based on risk adjusted observed-expected (O/E) mortality ratios. However, failure to account for number of patients has been recently shown to produce unreliable mortality estimates, especially for low-volume centers. This study explores the effect of reliability adjustment (RA), a statistical technique developed to eliminate bias introduced by low volume on risk-adjusted trauma center benchmarking.
Analysis of the National Trauma Data Bank 2010 was performed. Patients 16 years or older with blunt or penetrating trauma and an Injury Severity Score (ISS) of 9 or greater were included. Based on the statistically accepted standards of the Trauma Quality Improvement Program methodology, risk-adjusted mortality rates were generated for each center and used to rank them accordingly. Hierarchical logistic regression modeling was then performed to adjust these rates for reliability using an empiric Bayes approach. The impact of RA was examined by (1) recalculating interfacility variations in adjusted mortality rates and (2) comparing adjusted hospital mortality quintile rankings before and after RA.
A total of 557 facilities (with 278,558 patients) were included. RA significantly reduced the variation in risk-adjusted mortality rates between centers from 14-fold (0.7-9.8%) to only 2-fold (4.4-9.6%) after RA. This reduction in variation was most profound for smaller centers. A total of 68 "best" hospitals and 18 "worst" hospitals based on current risk adjustment methods were reclassified after performing RA.
"Reliability adjustment" dramatically reduces variations in risk-adjusted mortality arising from statistical noise, especially for lower volume centers. Moreover, the absence of RA had a profound impact on hospital performance assessment, suggesting that nearly one of every six hospitals in National Trauma Data Bank would have been inappropriately placed among the very best or very worst quintile of rankings. RA should be considered while benchmarking trauma centers based on mortality.
目前，创伤中心的质量基准测试是基于风险调整的观察到的预期（O/E）死亡率比。然而，最近的研究表明，未考虑患者数量会导致不可靠的死亡率估计，尤其是对于低容量中心。本研究探讨了可靠性调整（RA）的效果，这是一种统计学技术，旨在消除低容量对风险调整的创伤中心基准测试引入的偏差。
对国家创伤数据库 2010 年进行了分析。纳入年龄在 16 岁或以上、有钝器或穿透性创伤且损伤严重程度评分（ISS）为 9 或更高的患者。根据创伤质量改进计划方法的统计学可接受标准，为每个中心生成了风险调整后的死亡率，并据此对其进行排名。然后使用经验贝叶斯方法进行层次逻辑回归建模，根据可靠性对这些比率进行调整。通过（1）重新计算调整后死亡率的设施间差异，（2）比较调整前后的调整后的医院死亡率五分位数排名，来检查 RA 的影响。
共纳入 557 家医院（共 278558 名患者）。RA 显著降低了中心之间风险调整后死亡率的差异，从 14 倍（0.7-9.8%）降低至仅 2 倍（4.4-9.6%）。对于较小的中心，这种差异的降低更为明显。基于当前风险调整方法，共有 68 家“最佳”医院和 18 家“最差”医院在进行 RA 后被重新分类。
“可靠性调整”可显著降低因统计噪声而导致的风险调整后死亡率的差异，尤其是对于低容量中心。此外，缺乏 RA 对医院绩效评估产生了深远的影响，这表明在国家创伤数据库中，近六分之一的医院可能被不恰当地列入死亡率排名的最佳或最差五分位。在基于死亡率对创伤中心进行基准测试时，应考虑 RA。
