Clinically Relevant Features for Predicting the Severity of Surgical Site Infections

Abstract

Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on the day of surgery, a future surgical site infection and its severity. Our dataset is extracted from the electronic health records of patients who underwent gastrointestinal surgery and developed either deep, shallow or no infection. We represent the patients using the concentrations of fourteen common blood components collected over the four weeks preceding the surgery partitioned into six time windows. A gradient boosting based classifier trained on our new set of features reports an AUROC of 0.991 for predicting a postoperative infection and and AUROC of 0.937 for classifying the severity of the infection. Further analyses support the clinical relevance of our approach as the most important features describe the nutritional status and the liver function over the two weeks prior to surgery.

Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review

“…Assessment of model performance was limited by the overall poor quality of reporting (particularly prognostic accuracy and model calibration), as well as the high risk of bias and scarce external validation. Overall, most models performed well with regard to derivation and internal validation, with the highest discrimination observed in models based on machine learning 31 , 43 . These are increasingly common in the literature 49 , with 30 per cent (3 of 10) of SSI models developed in the last 5 years using machine-learning approaches.…”