Background: Skin and soft tissue infection (SSTI) after hernia surgery is infrequent yet catastrophic and is associated with mesh infection, interventions, and hernia recurrence. Although hernia repair is one of the most common general surgery procedures, uncertainty persists regarding incidence of long-term infections. Our goal is to develop a machine learning regression model that detects the occurrence of long-term hernia-associated SSTI. Patients and Methods: The data set consisted of veterans receiving hernia repair with implanted synthetic mesh during 2008-2015. The outcome of interest was occurrence of SSTI related to the index hernia surgery over a five-year follow-up. A neural network regression was fit on a medical record reviewed sample, then applied to the study population. Results: The study population was 96,435 surgeries, of which 76,886 (79.7%) were inguinal, 11,177 (11.6%) were umbilical, and 8,372 (8.7%) were ventral. In the training set, 40 patients had SSTI probability ≥90%, of whom 38 (95%) had a true SSTI. In 249 patients with SSTI probability <10%, only five (2%) patients had a true SSTI. In the testing set, nine patients were assigned a probability >90% and all were true-positives. In 100 patients with probability <10%, only two (2%) patients had a true infection. C-statistics were 0.929 in the training set and 0.901 in the testing set. Conclusions: The model showed excellent discrimination between those with and without infection and had good calibration. The model could be used to reduce the cost of detecting long-term infections.
Keywords: hernia; infection surveillance; machine learning; surgical infection; surgical outcomes.