Meguid Robert A, Bronsert Michael R, Juarez-Colunga Elizabeth, Hammermeister Karl E, Henderson William G
*Surgical Outcomes and Applied Research Program, University of Colorado School of Medicine, Aurora†Department of Surgery, University of Colorado School of Medicine, Aurora‡Adult and Child Center for Health Outcomes Research and Delivery Science, University of Colorado School of Medicine, Aurora§Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora¶Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora.
Ann Surg. 2016 Jun;263(6):1042-8. doi: 10.1097/SLA.0000000000001669.
To use factor analysis to cluster the 18 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) perioperative complications into a reproducible, smaller number of clinically meaningful groups of postoperative complications, facilitating and streamlining future study and application in live clinical settings.
The ACS NSQIP collects and reports on eighteen 30-day postoperative complications (excluding mortality), which are variably grouped in published analyses using ACS NSQIP data. This hinders comparison between studies of this widely used quality improvement dataset.
Factor analysis was used to develop a series of complication clusters, which were then analyzed to identify a parsimonious, clinically meaningful grouping, using 2,275,240 surgical cases in the ACS NSQIP Participant Use File (PUF), 2005 to 2012. The main outcome measures are reproducible, data-driven, clinically meaningful clusters of complications derived from factor solutions.
Factor analysis solutions for 5 to 9 latent factors were examined for their percent of total variance, parsimony, and clinical interpretability. Applying the first 2 of these criteria, we identified the 7-factor solution, which included clusters of pulmonary, infectious, wound disruption, cardiac/transfusion, venous thromboembolic, renal, and neurological complications, as the best solution for parsimony and clinical meaningfulness. Applying the last (clinical interpretability), we combined the wound disruption with the infectious clusters resulting in 6 clusters for future clinical applications.
Factor analysis of ACS NSQIP postoperative complication data provides 6 clinically meaningful complication clusters in lieu of 18 postoperative morbidities, which will facilitate comparisons and clinical implementation of studies of postoperative morbidities.
运用因子分析将美国外科医师学会国家外科质量改进计划（ACS NSQIP）的18种围手术期并发症聚类为数量可再现且更少的具有临床意义的术后并发症组，以促进并简化未来在实际临床环境中的研究与应用。
ACS NSQIP收集并报告了18种30天术后并发症（不包括死亡率），在使用ACS NSQIP数据进行的已发表分析中，这些并发症的分组方式各不相同。这妨碍了对这个广泛使用的质量改进数据集的研究之间的比较。
使用因子分析来建立一系列并发症聚类，然后利用2005年至2012年ACS NSQIP参与者使用文件（PUF）中的2275240例手术病例进行分析，以确定一个简洁且具有临床意义的分组。主要结局指标是源自因子分析结果的可再现、数据驱动且具有临床意义的并发症聚类。
检查了5至9个潜在因子的因子分析结果在总方差百分比、简洁性和临床可解释性方面的情况。应用前两个标准，我们确定了7因子解决方案为简洁性和临床意义方面的最佳方案，该方案包括肺部、感染性、伤口裂开、心脏/输血、静脉血栓栓塞、肾脏和神经系统并发症聚类。应用最后一个标准（临床可解释性），我们将伤口裂开聚类与感染性聚类合并，从而得到6个聚类以供未来临床应用。
对ACS NSQIP术后并发症数据进行因子分析可提供6个具有临床意义的并发症聚类，以替代18种术后发病情况，这将有助于术后发病情况研究的比较和临床实施。
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