Prediction Models for Recurrence of Chronic Subdural Hematoma in Patients Underwent Twist-Drill Craniostomy Combined With Urokinase Instillation

The recurrence of chronic subdural hematoma (CSDH) is high post-treatment. In this study, we aimed to construct individualized models for prediction of the postoperative recurrence of CSDH in patients underwent twist-drill craniostomy combined with urokinase (UK) instillation. In total, 183 patients with CSDH were retrospectively enrolled. In summary, 21 candidate factors were retrieved from past medical records. The least absolute shrinkage and selection operator regression was adopted to reduce the high dimensionality of data. Four predictors: preoperative hematoma volume, encephalatrophy, brain re-expansion, and UK instillation frequency were filtered from the 21 candidate factors using the least absolute shrinkage and selection operator method. Binary logistic regression model was employed to establish preoperative and postoperative prediction models. The preoperative model included preoperative hematoma volume and encephalatrophy whereas the postoperative model included brain re-expansion and UK instillation frequency. The predictive performance of the nomograms was evaluated by the receiver operating characteristic curve and calibration chart. Area under curve of the preoperative and postoperative models were 0.755 (95% confidence interval: 0.690-0.889) and 0.782 (95% confidence interval: 0.720-0.936), respectively, indicating good discrimination ability. The calibration results showed good fitting between the predicted probability and the actual probability. Finally, a decision curve analysis revealed excellent clinical performance of the proposed nomograms. Functionally, the preoperative model was used to identify high-risk patients with CSDH and application of UK, while the postoperative model was applied to guide physician-patients communication during follow-up. These 2 prediction models provide a basis for further clinical and experimental studies.