Christian Caprice K, Gustafson Michael L, Betensky Rebecca A, Daley Jennifer, Zinner Michael J
Department of Surgery, Brigham and Women's Hospital, Boston, MA 021115, USA.
Ann Surg. 2003 Oct;238(4):447-55; discussion 455-7. doi: 10.1097/01.sla.0000089850.27592.eb.
The original Leapfrog Initiative recommends selective referral based on procedural volume thresholds (500 coronary artery bypass graft [CABG] surgeries, 30 abdominal aortic aneurysm [AAA] repairs, 100 carotid endarterectomies [CEA], and 7 esophagectomies annually). We tested the volume-mortality relationship for these procedures in the University HealthSystem Consortium (UHC) Clinical DatabaseSM, a database of all payor discharge abstracts from UHC academic medical center members and affiliates. We determined whether the Leapfrog thresholds represent the optimal cutoffs to discriminate between high- and low-mortality hospitals.
Logistic regression was used to test whether volume was a significant predictor of mortality. Volume was analyzed in 3 different ways: as a continuous variable, a dichotomous variable (above and below the Leapfrog threshold), and a categorical variable. We examined all possible thresholds for volume and observed the optimal thresholds at which the odds ratio is the highest, representing the greatest difference in odds of death between the 2 groups of hospitals.
In multivariate analysis, a relationship between volume and mortality exists for AAA in all 3 models. For CABG, there is a strong relationship when volume is tested as a dichotomous or categorical variable. For CEA and esophagectomy, we were unable to identify a consistent relationship between volume and outcome. We identified empirical thresholds of 250 CABG, 15 AAA, and 22 esophagectomies, but were unable to find a meaningful threshold for CEA.
In this group of academic medical centers and their affiliated hospitals, we demonstrated a significant relationship between volume and mortality for CABG and AAA but not for CEA and esophagectomy, based on the Leapfrog thresholds. We described a new methodology to identify optimal data-based volume thresholds that may serve as a more rational basis for selective referral.
最初的“跨越计划”建议根据手术量阈值进行选择性转诊（每年500例冠状动脉搭桥术[CABG]、30例腹主动脉瘤[AAA]修复术、100例颈动脉内膜切除术[CEA]和7例食管切除术）。我们在大学卫生系统联盟（UHC）临床数据库SM中测试了这些手术的手术量与死亡率之间的关系，该数据库包含UHC学术医疗中心成员和附属医院所有付费方出院摘要。我们确定“跨越计划”的阈值是否代表区分高死亡率医院和低死亡率医院的最佳临界值。
采用逻辑回归分析来测试手术量是否是死亡率的显著预测因素。手术量以三种不同方式进行分析：作为连续变量；作为二分变量（高于和低于“跨越计划”阈值）；作为分类变量。我们研究了手术量的所有可能阈值，并观察到优势比最高时的最佳阈值，这代表两组医院在死亡几率上的最大差异。
在多变量分析中，AAA在所有三种模型中手术量与死亡率之间均存在关系。对于CABG，当手术量作为二分变量或分类变量进行测试时，存在很强的关系。对于CEA和食管切除术，我们无法确定手术量与结果之间存在一致关系。我们确定了250例CABG、15例AAA和22例食管切除术的经验阈值，但未能找到CEA的有意义阈值。
在这组学术医疗中心及其附属医院中，基于“跨越计划”阈值，我们证明了CABG和AAA的手术量与死亡率之间存在显著关系，而CEA和食管切除术则不然。我们描述了一种新方法，用于确定基于数据的最佳手术量阈值，这可能为选择性转诊提供更合理的依据。
