Systematic evaluation of machine learning models for postoperative surgical site infection prediction

作者：Anna M van Boekel, S. L. van der Meijden, S. Arbous, Rob G H H Nelissen, K. Veldkamp, Emma B Nieswaag, Kim F T Jochems, Jeroen Holtz, A. Veenstra, Jeroen Reijman, Y. de Jong, H. van Goor, M. Wiewel, J. Schoones, Bart F. Geerts, Mark G J de Boer · 发表于：PLoS ONE · 年份：2024 · DOI：10.1371/journal.pone.0312968 · 被引用次数：16 · 研究领域：Medicine

Background Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools. Objective The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs. Methods A systematic search in PubMed, Embase and the Cochrane library was performed from inception until July 2023. Exclusion criteria were the absence of reported model validation, SSIs as part of a composite adverse outcome, and pediatric populations. ML performance measures were evaluated, and ML performances were compared to regression-based methods for studies that reported both methods. Risk of bias (ROB) of the studies was assessed using the Prediction model Risk of Bias Assessment Tool. Results Of the 4,377 studies screened, 24 were included in this review, describing 85 ML models. Most models were only internally validated (81%). The C-statistic was the most used performance measure (reported in 96% of the studies) and only two studies reported calibration metrics. A total of 116 different predictors were described, of which age, steroid use, sex, diabetes, and smoking were most frequently (100% to 75%) incorporated. Thirteen studies compared ML models to regression-based models and showed a similar performance of both modelling methods....