Zhuang Yaxu, Dyas Adam, Meguid Robert A, Henderson William G, Bronsert Michael, Madsen Helen, Colborn Kathryn L
Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus.
Department of Biostatistics and Informatics, Colorado School of Public Health.
Ann Surg. 2024 Apr 1;279(4):720-726. doi: 10.1097/SLA.0000000000006106. Epub 2023 Sep 27.
To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data.
Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner. However, there are no specific models for risk-adjusting infectious complication rates using EHR data.
Preoperative EHR data from 30,639 patients (2013-2019) were linked to the American College of Surgeons National Surgical Quality Improvement Program preoperative data and postoperative infection outcomes data from 5 hospitals in the University of Colorado Health System. EHR data included diagnoses, procedures, operative variables, patient characteristics, and medications. Lasso and the knockoff filter were used to perform controlled variable selection. Outcomes included surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia up to 30 days postoperatively.
Among >15,000 candidate predictors, 7 were chosen for the surgical site infection model and 6 for each of the urinary tract infection, sepsis, and pneumonia models. Important variables included preoperative presence of the specific outcome, wound classification, comorbidities, and American Society of Anesthesiologists physical status classification. The area under the receiver operating characteristic curve for each model ranged from 0.73 to 0.89.
Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.
利用结构化电子健康记录（EHR）数据评估术后感染的术前风险。
术后感染的监测和报告主要通过对一小部分患者进行成本高昂、劳动密集型的人工病历审查来完成。将统计模型应用于术后EHR数据的自动化方法已显示出有望增强人工审查，因为它们可以及时涵盖所有手术。然而，目前尚无使用EHR数据对感染并发症发生率进行风险调整的具体模型。
将2013年至2019年30639例患者的术前EHR数据与美国外科医师学会国家外科质量改进计划的术前数据以及科罗拉多大学健康系统5家医院的术后感染结局数据相链接。EHR数据包括诊断、手术、手术变量、患者特征和用药情况。使用套索法和仿冒筛选器进行可控变量选择。结局包括术后30天内的手术部位感染、尿路感染、脓毒症/感染性休克和肺炎。
在超过15000个候选预测因素中，手术部位感染模型选择了7个，尿路感染、脓毒症和肺炎模型各选择了6个。重要变量包括特定结局的术前存在情况、伤口分类、合并症以及美国麻醉医师协会身体状况分类。每个模型的受试者工作特征曲线下面积在0.73至0.89之间。
开发了使用EHR数据预测术后感染风险的简洁术前模型，其表现与现有的使用人工病历审查的美国外科医师学会国家外科质量改进计划风险模型相当。这些模型可用于及时估计应用于大量EHR数据的风险调整后术后感染率。
