Establishment and validation of a prediction model for self-absorption probability of chronic subdural hematoma - Tian, Ye; Wang, Dong; Zhang, Xinjie; Wei, Huijie; Wei, Yingsheng; An, Shuo; Gao, Chuang; Huang, Jinhao; Sun, Jian; Jiang, Rongcai; Zhang, Jianning (Frontiers in Neurology, 2022)
Chronic subdural hematoma (CSDH) is common in the elderly people combined with clear or occult traumatic brain injury history. Surgery is a traditional method to remove the hematomas, but it carries significant risk of recurrence and poor outcome. Non-surgical treatment have been recently considered effectiveness and safety on some patients with CSDH. But it’s a challenge to speculate which part of patients could obtain benefits from non-surgical treatment. Objective: To establish and validate a new prediction model of self-absorption probability with chronic subdural hematoma. Methods—The prediction model was established base on the data from a randomized clinical trial, which enrolled 196 CSDH patients from February 2014 to November 2015. The following subjects were extracted: demographic characteristic, medical history, hematoma characters in imaging at admission, and clinical assessments. The outcome was self-absorption at 8th week after admission. Lasso regression model was implemented for data dimensionality reduction and feature selection. Multivariable logistic regression was adopted to establish the model, while the experimental results presented by nomogram. Discrimination, calibration, and clinical usefulness were used to evaluated the performance of nomogram. Totally 60 consecutive patients were involved in the external validation, which enrolled in a proof-of-concept clinical trial from July 2014 to December 2018. Results—Diabetes mellitus history, hematoma volume at admission, presence of basal ganglia suppression, presence of septate hematoma, and usage of atorvastatin were the strongest predictors of self-absorption. The model had good discrimination (AUC, 0.713 [95% CI, 0.637 to 0.788]) and good calibration (P = 0.986). The nomogram in the validation cohort still had good discrimination (AUC, 0.709 [95% CI, 0.574 to 0.844]) and good calibration (P = 0.441). Decision curve analysis proved that the nomogram was clinically effective. Conclusions—This prediction model can be used to obtain self-absorption probability in patients with CSDH, assisting in guiding the choice of therapy, whether undergo non-surgical treatment or surgery.