Development and internal validation of a clinical prediction model for deep surgical site infection after open...

Early identification of patients at high risk of deep surgical site infection after open extremity fractures is crucial for timely intervention and improved outcomes. This study aimed to develop and internally validate a clinical prediction model, the BIGB²OSS score, for predicting deep surgical site infection within three months of injury using variables easily obtained in early post-injury phase.

A prospective cohort study was conducted at two university hospitals in Japan, including 570 consecutive patients with open extremity fractures. Data on 18 candidate predictors were collected in the early post-injury phase. The primary outcome was the occurrence of deep surgical site infection within three months of injury. A backward stepwise logistic regression model was used to build the prediction model. Model performance was assessed using the area under the curve (AUC) and internal validation with bootstrap methods.

The BIGB²OSS score was a 5-point model based on four predictors: BMI ≥ 25 (1 point), Gustilo-Anderson classification IIIA (1 point), Gustilo-Anderson IIIB or IIIC (2 points), OTA-OFC skin = 3 (1 point), and smoking history (1 point). The model showed fair discriminative ability with an AUC of 0.76 (95% CI 0.70-0.83). Internal validation using bootstrap resampling with 1000 repetitions yielded an optimism-adjusted C-statistic of 0.76 (95% CI 0.70-0.83). The estimated probabilities of deep surgical site infection were 2.7% for scores 0-1, 11% for scores 2-3, and 30% for scores 4-5, closely matching the actual prevalence.

The BIGB²OSS score is a simple clinical prediction model with fair discrimination for deep surgical site infection in patients with open extremity fractures. It uses early post-injury variables and may support timely risk communication and follow-up planning. Further external validation is needed to confirm its utility in diverse clinical settings.