Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis

Background: Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms. Methods: MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under the receiver operating characteristic curve [AUC]) were pooled using a random effect model. Stratified analysis was applied to different study characteristic levels. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed. Results: Of 945 articles identified, 108 algorithms from 32 articles were included in this review. The overall pooled estimate of the SSI incidence rate was 3.67%, 95% CI: 3.58–3.76. Mixed-use of structured and textual data-based algorithms (pooled estimates of sensitivity 0.83, 95% CI: 0.78–0.87, specificity 0.92, 95% CI: 0.86–0.95, AUC 0.92, 95% CI: 0.89–0.94) outperformed algorithms solely based on structured data (sensitivity 0.56, 95% CI:0.43–0.69, specificity 0.95, 95% CI:0.91–0.97, AUC = 0.90, 95% CI: 0.87–0.92). Conclusions: ML algorithms developed with structured and textual data provided optimal performance. External validation of ML algorithms is needed to translate current knowledge into clinical practice.