A nomogram for predicting the possibility of effusion deterioration in patients with traumatic subdural effusion

Traumatic subdural effusion (TSDE) may increase progressively or evolve into chronic subdural hematoma. These events, defined as deterioration of the effusion, often require close observation or even surgical treatment. The aim of our study was to develop and validate a nomogram for predicting the possibility of an effusion deteriorating in patients with TSDE based on the available clinical characteristics. Clinical data from 78 patients with TSDE were retrospectively analyzed. All patients were admitted from January 2019 to May 2022. Logistic regression was applied to the data to screen for independent predictors of effusion deterioration within six months; then, a predictive nomogram model was established in R language. The consistency, predictive accuracy and clinical utility of the model were evaluated with the C-index, calibration plots, ROC curves and decision curve analysis (DCA). Furthermore, we performed internal validation using a bootstrap approach to assess the effectiveness of the model. Time of effusion after trauma, maximum thickness of the effusion, CT value of the effusion as well as the use of atorvastatin were identified as predictors in the nomogram. The predictive model was well calibrated and demonstrated good discrimination (C-index: 0.893). The AUC of the model was 0.893 (95% CI: 0.824–0.962), and the modified C-index (0.865) indicated excellent performance in the internal validation. In addition, DCA revealed that the nomogram had clinical value. This predictive model can effectively assess the risk of effusion deterioration in TSDE patients within six months and identify high-risk patients early. •Traumatic subdural effusion may increase progressively or evolve into chronic subdural hematoma.•This study pioneers a nomogram to predict effusion deterioration using existing clinical characteristics.•Time of effusion after trauma, maximum thickness, CT value and the use of atorvastatin were independent predictors.•The model's robust predictive power and clinical utility assist clinicians in early identifying high-risk populations.