摘要
目的基于矢状位影像学参数和临床特征构建颅底凹陷患者复位术后生命质量的LASSO-logistic回归预测模型并对其进行验证。方法回顾性分析2015年8月至2020年8月空军军医大学唐都医院神经外科采用经颈后路复位减压植骨融合内固定术治疗的94例颅底凹陷患者的临床资料。基于患者的年龄、体重、手术前后疼痛数值评价量表(NRS)评分、颈椎功能障碍指数(NDI)及矢状位影像学相关参数,采用LASSO-logistic回归法筛选出鲁棒性最好的变量并构建颅底凹陷患者复位术后生命质量的预测模型。绘制受试者工作特征(ROC)曲线,并根据曲线下面积(AUC)判断该预测模型的效能。采用Bootstrap法进行500次重复抽样进行内部验证。结果LASSO-logistic回归的分析结果显示,共9个因素纳入预测模型,分别为:年龄、体重、术前NRS评分、术前NDI、术前头颈屈曲角(HNFA)、术后斜坡枢椎角(pCXA)、术后斜坡斜坡角(pCS)、术后延髓脊髓角(pCMA)及术后Boogaard角(pBoA)。通过绘制ROC曲线,发现该预测模型的AUC为0.893,灵敏度为79.4%,特异度为84.6%,阳性似然比为5.162,阴性似然比为0.243。内部验证的结果显示,AUC为0.885,灵敏度为81.3%,特异度为82.6%,阳性似然比为5.153,阴性似然比为0.237。结论基于年龄、体重、术前NRS评分、术前NDI、术前HNFA、pCXA、pCS、pCMA及pBoA构建的颅底凹陷患者复位术后生命质量LASSO-logistic预测模型拟合性较好。
Objective To develop and validate the LASSO-logistic regression prediction model for the health-related quality of life(HRQOL)of patients after reduction of basilar invagination(BI)based on the radiographic sagittal parameters and clinical characteristics.Methods A retrospective analysis was conducted on the clinical data of 94 patients with BI treated by reduction surgery at the Department of Neurosurgery,Tangdu Hospital,Air Force Medical University from August 2015 to August 2020.Based on the patient′s age,weight,pre-and post-operative numerical rating scale(NRS)score,neck disability index(NDI),and related radiographic sagittal parameters,the most robust variables were screened out by LASSO-logistic regression and a predictive model was developed for HRQOL of patients with BI after reduction.The receiver operating characteristic(ROC)curve was drawn,and the predictive value of the model was estimated based on the area under the curve(AUC).The Bootstrap method was used to carry out 500 repeated sampling for internal validation.Results The analysis results of LASSO-logistic regression showed that a total of 9 factors were included in the prediction model,namely:age,weight,preoperative NRS,preoperative NDI,preoperative head and neck flexion angle(HNFA),postoperative clivoaxial angle(pCXA),postoperative clivus angle(pCS),postoperative cervicomedullary angle(pCMA),postoperative Boogaard′s angle(pBoA).By drawing the ROC curve,it is found that the AUC of the prediction model was 0.893,the sensitivity was 79.4%,the specificity was 84.6%,the positive likelihood ratio was 5.162,and the negative likelihood ratio was 0.243.The internal validation results showed that the AUC was 0.885,the sensitivity was 81.3%,the specificity was 82.6%,the positive likelihood ratio was 5.153,and the negative likelihood ratio was 0.237.Conclusion Based on age,weight,preoperative NRS score,preoperative NDI,HNFA,pCXA,pCS,pCMA and pBoA,the LASSO-logistic predictive model for HRQOL of patients with BI after reduction has a high goodness of fi
作者
彭立玮
杨帆
毛紫龙
左威
程超
王鹏
熊东
张津安
张雷
李维新
Peng Liwei;Yang Fan;Mao Zilong;Zuo Wei;Cheng Chao;Wang Peng;Xiong Dong;Zhang Jin′an;Zhang Lei;Li Weixin(Department of Neurosurgery,Tangdu Hospital,Air Force Medical University,Xi′an 710038,China;Department of Plastic Surgery and Burns,Tangdu Hospital,Air Force Medical University,Xi′an 710038,China)
出处
《中华神经外科杂志》
CSCD
北大核心
2021年第10期997-1001,共5页
Chinese Journal of Neurosurgery
