*Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA †Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL ‡VA Greater Los Angeles Healthcare System, Los Angeles, CA §Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA ¶Department of Surgery, School of Medicine, Washington University in St Louis and Barnes Jewish Hospital, St Louis, MO; Center for Health Policy and the Olin Business School at Washington University in St Louis, St Louis, MO; and Department of Surgery, John Cochran Veterans Affairs Medical Center, St Louis, MO ‖RAND Corporation, Santa Monica, CA; and **UCLA Jonathan and Karin Fielding School of Public Health, Los Angeles, CA.
Ann Surg. 2015 Feb;261(2):290-6. doi: 10.1097/SLA.0000000000000707.
To compare the classification of hospital statistical outlier status as better or worse performance than expected for postoperative complications using Medicare claims versus clinical registry data.
Controversy remains as to the most favorable data source for measuring postoperative complications for pay-for-performance and public reporting polices.
Patient-level records (2005-2008) were linked between the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) and Medicare inpatient claims. Hospital statistical outlier status for better or worse performance than expected was assessed using each data source for superficial surgical site infection (SSI), deep/organ-space SSI, any SSI, urinary tract infection, pneumonia, sepsis, deep venous thrombosis, pulmonary embolism, venous thromboembolism, and myocardial infarction by developing hierarchical multivariable logistic regression models. Kappa statistics and correlation coefficients assessed agreement between the data sources.
A total of 192 hospitals with 110,987 surgical patients were included. Agreement on hospital rank for complication rates between Medicare claims and ACS-NSQIP was poor-to-moderate (weighted κ: 0.18-0.48). Of hospitals identified as statistical outliers for better or worse performance by Medicare claims, 26% were also identified as outliers by ACS-NSQIP. Of outliers identified by ACS-NSQIP, 16% were also identified as outliers by Medicare claims. Agreement between the data sources on hospital outlier status classification was uniformly poor (weighted κ: -0.02-0.34).
Despite using the same statistical methodology with each data source, classification of hospital outlier status as better or worse performance than expected for postoperative complications differed substantially between ACS-NSQIP and Medicare claims.
比较使用医疗保险索赔数据与临床注册数据对术后并发症进行分类，判断医院统计离群状态是表现优于预期还是劣于预期。
对于绩效付费和公共报告政策而言，衡量术后并发症的最佳数据来源仍存在争议。
患者层面的记录（2005-2008 年）在外科医师学院国家外科质量改进计划（ACS-NSQIP）和医疗保险住院索赔之间进行了链接。使用每个数据源（浅表手术部位感染[SSI]、深部/器官空间 SSI、任何 SSI、尿路感染、肺炎、败血症、深静脉血栓形成、肺栓塞、静脉血栓栓塞和心肌梗死）评估医院表现优于或劣于预期的统计离群状态，通过开发分层多变量逻辑回归模型。Kappa 统计量和相关系数评估了数据源之间的一致性。
共纳入 192 家医院的 110987 例手术患者。医疗保险索赔和 ACS-NSQIP 之间关于并发症发生率的医院等级的一致性为差到中等（加权κ：0.18-0.48）。医疗保险索赔确定为表现优于或劣于预期的统计离群的医院中，有 26%也被 ACS-NSQIP 确定为离群。ACS-NSQIP 确定的离群中，有 16%也被医疗保险索赔确定为离群。数据源之间关于医院离群状态分类的一致性普遍较差（加权κ：-0.02-0.34）。
尽管使用相同的统计方法对每个数据源进行分类，但 ACS-NSQIP 和医疗保险索赔对术后并发症的医院离群状态分类作为表现优于预期或劣于预期的结果存在显著差异。
