ML-predicted surgical site infections: An epidemiological study utilizing machine learning on routinely collected healthcare data to predict infection risk

Abstract

Background

Surgical site infections (SSIs) are a major public health issue, causing increased morbidity, longer hospital stays, and higher healthcare costs. Despite progress in infection control, predicting and preventing SSIs remain crucial for improving patient outcomes. This study examines the use of machine learning (ML) on routinely collected healthcare data (RCD) to predict SSIs in orthopaedic surgery, aiming to improve risk stratification and guide interventions.

Objectives

To develop, test, and validate an ML predictive model using RCD to assess SSI risk in orthopaedic surgery patients.

Methods

A retrospective study was carried out using RCD from a 1.2 million population in an Italian Local Health Authority, covering surgeries from 2017 to 2021. The population included patients undergoing hip or knee arthroplasty and open reduction of fractures. Several ML algorithms, including eXtreme Gradient Boosting (XGBoost), were used for model development. The models’ performance was assessed by recall, accuracy, and area under the receiver operating characteristic curve (AUC). A feature importance analysis identified key SSI risk predictors.

Results

The XGBoost model demonstrated superior performance, with a recall exceeding 70% and an AUC>0.70, overcoming other methods. Significant predictors included the ASA classification, opioid use, priority class of the surgery operation, and length of hospital stay.

Conclusions

ML models, particularly XGBoost, effectively predicted SSI risk in orthopaedic patients, offering a new approach to infection control and prevention. Incorporating ML and RCD highlights the potential for scalable, data-driven personalized medicine interventions. Future research will focus on model validation and integration of these tools into healthcare systems for enhanced patient management.