Development and validation of a nomogram to predict the risk of constipation after lumbar interbody fusion surgery

To understand the incidence of postoperative constipation and the risk factors of constipation in patients with lumbar interbody fusion, we constructed and verified the constipation risk prediction model, so as to provide reference for the prevention and treatment of postoperative constipation.

The data of patients undergoing lumbar interbody fusion in our hospital were retrospectively analyzed from December 2021 to December 2022. According to postoperative constipation, the patients were divided into constipation group and non-constipation group. Univariate logistic regression analysis and multivariate logistic regression analysis were used to determine independent risk factors for postoperative constipation. Based on independent risk factors, a nomogram was developed to predict the risk of constipation after lumbar interbody fusion. The prediction performance was assessed using receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). Finally, bootstrapping method for internal validation was further evaluated the nomogram.

A total of 282 patients participated in the study. 176 patients (62.41%) after lumbar interbody occurred constipation, and 106 patients were asymptomatic. Multivariate regression analysis showed independent risk factors, including the use of calcium channel blockers, polypharmacy, postoperative bed time, and constipation history. Multivariate regression analysis was used to establish the model. The C-index of the nomogram was 0.827 (95% CI 0.779-0.875), and the C-index of interval bootstrapping validation was 0.813 (95% CI 0.765-0.861), and the area under the AUC was 0.800. The nomogram showed good discrimination ability.

The use of calcium channel blockers, polypharmacy, postoperative bed time, and history of constipation are independent risk factors for postoperative constipation in patients undergoing lumbar interbody fusion. The constructed risk prediction model has good discriminative ability.