Development and Validation of a Nomograph Model for Post-Operative Central Nervous System Infection after Craniocerebral Surgery

Abstract

Purpose: A nomograph model of predicting the risk of post-operative central nervous system infection (PCNSI) after craniocerebral surgery was established and validated. Methods: The clinical medical records of patients after cranial surgery in Renmin Hospital of Wuhan University from January 2020 to September 2022 were collected, of whom 998 patients admitted to Shouyi Hospital District were used as the training set and 866 patients admitted to Guanggu Hospital District were used as the validation set. Lasso regression was applied to screen the independent variables in the training set, and the model was externally validated in the validation set. Results: A total of 1864 patients after craniocerebral surgery were included in this study, of whom 219 (11.75%) had PCNSI. Multivariate logistic regression analysis showed that age > 70 years, a previous history of diabetes, emergency operation, an operation time ≥ 4 h, insertion of a lumbar cistern drainage tube ≥ 72 h, insertion of an intracranial drainage tube ≥ 72 h, intraoperative blood loss ≥ 400 mL, complicated with shock, postoperative albumin ≤ 30 g/L, and an ICU length of stay ≥ 3 days were independent risk factors for PCNSI. The area under the curve (AUC) of the training set was 0.816 (95% confidence interval (95%CI), 0.773–0.859, and the AUC of the validation set was 0.760 (95%CI, 0.715–0.805). The calibration curves of the training set and the validation set showed p-values of 0.439 and 0.561, respectively, with the Hosmer–Lemeshow test. The analysis of the clinical decision curve showed that the nomograph model had high clinical application value. Conclusion: The nomograph model constructed in this study to predict the risk of PCNSI after craniocerebral surgery has a good predictive ability.