Abstract / Summary
Background: Chronic subdural hematoma (CSDH) is the most common bleeding disorder in the nervous system and has a high recurrence rate, posing a serious threat to the lives of elderly individuals. Early identification and adjustment of the treatment regimen in the perioperative period is crucial. The aim of this study was to create a perioperative predictive nomogram for the early prediction of recurrent CSDH (R-CSDH). Methods: A predictive model was developed including 632 patients with CSDH hospitalized between 1 January 2021 and 31 December 2025 from Wuhan University People’s Hospital. The 206 patients from the First People’s Hospital of Xiantao City who were hospitalized between 1 January 2022 and 31 December 2025 formed an external validation cohort. Minimum absolute shrinkage was used to optimize feature selection, and logistic regression analysis was applied to further select features and construct a risk prediction model for recurrent CSDH. The calibration, discrimination, and clinical utility of the predictive models were assessed using calibration plots, the C-index, the area under the curve (AUC), and decision curve analysis. External validation was performed by an external validation cohort. Findings: Age, CT hematoma size, hematoma location, atrophy, density, surgical procedure and D-dimer were the predictors for constructing the predictive nomogram, and the results showed that a logistic regression model based on the above seven factors had good calibration and discriminative ability (C-index: 0.925; 95% confidence interval: 0.893–0.957). The AUC of this predictive model was 0.92487. The calibration curve of the nomogram was close to the ideal diagonal. In addition, the decision curve analysis showed that the net benefit of the model was significantly better. External validation demonstrated the reliability of the predictive nomogram. Interpretation: D-dimer was found to be a novel factor for recurrent chronic subdural hematoma. Our study provides clinicians with an accurate tool for identification and early intervention and therapy to reduce the incidence of R-CSDH.