关键词: 颅骨钻孔引流术, 慢性硬膜下血肿, 治疗效果, 临床决策曲线, 人工神经网络模型

Abstract: Objective Based on the artificial neural network diagram,a sub-model of failure factors of chronic subdural hematoma treated by skull drilling and drainage was constructed,in order to provide clinical basis for diagnosis and treatment.Methods A total of 300 CSDH patients admitted to our hospital from April 2020 to August 2021 were selected as the research subjects.After hospitalization,all patients were treated with cranial trephination and drainage.According to the treatment results,they were divided into successful group (n=252) and failed group (n=48).The clinical data of the two groups of patients were compared,and the factors affecting treatment failure of CSDH patients were analyzed by multivariate Logistic regression method.The artificial neural network model was constructed,and the author characteristic (ROC) curve was used to evaluate the differentiation of the model and the effectiveness of the clinical decision curve.Results The age,operation time,intraoperative blood loss,serum NSE,proportion of secondary intracranial hematoma,serum TSP1 and TSP2 in the successful group were significantly lower than those in the failed group (P＜0.05),and the average diameter of bone pore was significantly higher than that in the failed group (P＜0.05).Multivariate analysis showed that long operation time,large amount of intraoperative blood loss,increased serum NSE level,secondary intracranial hematoma,and increased TSP1 and TSP2 levels were independent risk factors for the treatment failure of CSDH patients by cranial trephine drainage (P＜0.05).However,slightly larger mean bone pore diameter was a protective factor for CSDH patients' treatment failure through cranial drainage (P＜0.05).ROC curve showed that the AUC of the artificial neural network model was 0.935(95%CI:0.871~0.986,P＜0.001),and clinical decision curve showed that the artificial neural network model was effective.Conclusion Clinical observation of CSDH patients' operation time,intraoperative blood loss,serum NSE leveletc,can predict the therapeutic effect of cranial drilling and drainage and take corresponding prevention and treatment measures to reduce the mortality of CSDH patients.

Key words: Cranial drilling and drainage, Chronic subdural hematoma, Therapeutic effect, Clinical decision curve, Artificial neural network model