Stuart Christina M, Fei Yizhou, Colborn Kathryn L, Zhuang Yaxu, Henderson William G, Dyas Adam R, Bronsert Michael R, Meguid Robert A
Department of Surgery, University of Colorado Anschutz Medical Campus.
Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado Anschutz Medical Campus.
Ann Surg. 2025 Apr 21. doi: 10.1097/SLA.0000000000006737.
To compare statistical models applied to electronic health record (EHR) data to predict and identify non-infectious postoperative complications. The models have been published and are part of the Automated Surveillance of Postoperative Infections (ASPIN) project, which has expanded to include non-infectious complications.
Postoperative complications occur in 15% of nonemergent inpatient surgeries. Most reporting of postoperative complications relies on manual chart abstraction.
Preoperative and postoperative probabilities of non-infectious complications for patients from 5 large hospitals in Colorado were estimated using ASPIN models that were developed using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) gold standard outcomes. Observed:expected (O:E) ratios were estimated by dividing the sum of the postoperative probabilities by the sum of the preoperative probabilities. O:E ratios were compared between local ACS-NSQIP patients using ACS-NSQIP data, local ACS-NSQIP patients using EHR data, and all patients undergoing operations in the study period using EHR data.
O:E ratios for 9 non-infectious postoperative complications were estimated. Comparison of the O:E ratios of ACS-NSQIP patients using ACS-NSQIP data vs. EHR data showed overlapping confidence intervals in 44 (98%) of 45 comparisons (5 hospitals x 9 outcomes) and agreement in outlier status for 35 (78%).
Risk-adjusted postoperative outcomes estimated using machine learning on EHR data were similar to those produced by manual chart review. These models could be used to augment manual chart review to guide surgical quality improvement.
比较应用于电子健康记录（EHR）数据的统计模型，以预测和识别非感染性术后并发症。这些模型已经发表，是术后感染自动监测（ASPIN）项目的一部分，该项目已扩展到包括非感染性并发症。
15%的非急诊住院手术会发生术后并发症。大多数术后并发症报告依赖于人工图表摘要。
使用基于美国外科医师学会国家外科质量改进计划（ACS-NSQIP）金标准结果开发的ASPIN模型，估计科罗拉多州5家大型医院患者非感染性并发症的术前和术后概率。观察值与期望值（O:E）比率通过将术后概率总和除以术前概率总和来估计。比较使用ACS-NSQIP数据的当地ACS-NSQIP患者、使用EHR数据的当地ACS-NSQIP患者以及研究期间使用EHR数据进行手术的所有患者的O:E比率。
估计了9种非感染性术后并发症的O:E比率。比较使用ACS-NSQIP数据与EHR数据的ACS-NSQIP患者的O:E比率，45次比较（5家医院×9种结果）中的44次（98%）显示置信区间重叠，35次（78%）的异常值状态一致。
使用EHR数据通过机器学习估计的风险调整后术后结果与人工图表审查产生的结果相似。这些模型可用于加强人工图表审查，以指导手术质量改进。
