MATERIAL and METHODS: The prediction model was developed from a training set of 272 patients with CSDH who had undergone standard burr hole with irrigation surgery. A separate external validation cohort comprising 112 patients who underwent the same operation was also included. Least absolute shrinkage and selection operator (LASSO) regression was adopted to minimize the high dimension of data and predictor selection. Binary logistic regression was used to develop the present model. Subsequently, a nomogram was established as the ultimate representation of the prediction model. Area under the curve (AUC) was used to identify the discrimination of the designed predictive nomogram. The calibration plot was used to verify the goodness-of-fit of the nomogram. Finally, Decision curve analysis (DCA) was employed to appraise the clinical applicability of the present nomogram.

RESULTS: A total of 3 independent variables were filtered by LASSO analysis from the 22 candidate factors. The AUC of the training and validation sets were 0.833 (95%CI: 0.774-0.894) and 0.817 (95%CI: 0.711-0.922), respectively, which indicated a good discrimination ability. The calibration charts showed that the prediction probability and the actual probability fitted well. The DCA of the prediction model indicated an excellent clinical efficacy.

CONCLUSION: The proposed nomogram can quantitatively and conveniently predict the recurrence rate of CSDH after burr hole with irrigation surgery. Besides it can facilitate customized treatment adjustment and follow-up of patients who are at a high-risk of recurrence. Keywords : Prediction model, Nomogram, Chronic subdural hematoma, Recurrence