Development and Prospective Validation of a Novel Risk Score for Predicting the Risk of Poor Surgical Site Healing in Patients Following Surgical Procedure for Spinal Tuberculosis: A Multi-Center Cohort Study.

Abstract

Background: The risk of poor surgical site healing in patients with spinal tuberculosis due to M. tuberculosis infection is known to be higher than in other surgical patients. Early identification and diagnosis are critical if we are to reduce the disability and mortality associated with spinal tuberculosis. We aimed to develop and validate a novel predictive score for predicting the risk of poor surgical site healing in patients following surgical procedure for spinal tuberculosis. Patients and Methods: We retrospectively analyzed the clinical data of patients with spinal tuberculosis who were hospitalized in the orthopedic ward of four regional medical centers in Guizhou Province between January 2015 and October 2022. Univariate and LASSO analysis was used to identify risk factors, construct and evaluate predictive models and novel predictive score for poor surgical site healing following the surgical procedure. Subsequently, 110 patients, admitted to four regional medical centers in Guizhou Province between January 2023 and February 2024, were used as an external prospective validation cohort to test the predictive efficacy of the prediction model. Results: Seven predictors were identified as risk factors for poor surgical site healing in patients undergoing surgical procedure for spinal tuberculosis. The areas under the receiver operating characteristic curve for a risk prediction model constructed based on the significant risk factors were 0.753 (95% CI: 0.693-0.813) and 0.779 (95% CI: 0.696-0.863) for the training and validation sets, respectively. Decision curve analysis demonstrated that the model yielded good clinical benefit. Finally, we applied the newly developed poor surgical site healing risk assessment score for the external prospective validation set; the area under the receiver operating characteristic curve for the poor surgical site healing risk assessment score was 0.846 (95% CI: 0.769-0.923) demonstrated that the model yielded better predictive effectiveness. Conclusion: The novel poor surgical site healing risk assessment score exhibits good discriminatory power and represents a beneficial predictive tool for facilitating suitable postoperative clinical management.