摘要
目的:建立针刺治疗失眠症的疗效预测模型,并构建针刺方案适宜人群画像,以帮助提高临床疗效。方法:113例失眠症患者的相关数据被纳入分析,采用针刺通督调卫组方[百会、印堂、申脉(双侧)、照海(双侧)]治疗,每次30 min,每周3次,共治疗2周。利用匹兹堡睡眠质量指数(PSQI)减分率作为总体临床疗效评价指标。结合单因素logistic回归和Boruta算法进行特征选择,通过比较3种Boosting算法的预测准确性筛选最佳算法,使用网格搜索和十折交叉验证对最佳算法进行超参数调优,通过分层随机划分法筛选最佳数据集划分方式,根据约登指数寻找最佳截断值,构建模型并进行性能评价,最终通过SHAP分析对模型进行可视化解释。结果:纳入模型的特征包括N1期占总睡眠时长比例、N2期占总睡眠时长比例、自关灯时间起R期潜伏期、自关灯时间起N2期潜伏期、入睡后清醒时间、PSQI睡眠效率得分、老舌。XGBoost是最佳算法,最佳概率阈值为0.76,对应的精确度为0.91,召回率为0.91,F1分数为0.91,准确率为0.91,曲线下面积(AUC)为0.82。符合以下条件的患者更容易对针刺通督调卫组方产生应答:N1期占总睡眠时长比例约6%~70%,自关灯时间起N2期潜伏期低于约40 min,入睡后清醒时长小于约75 min或约为100~300 min,自关灯时间起R期潜伏期大于约75 min,N2期占总睡眠时长比例约20%~50%,PSQI睡眠效率得分为2或3分,舌象无“老舌”。结论:采用XGBoost建立的电针治疗失眠症疗效预测模型和借助SHAP初步构建的电针适宜人群画像,为针刺治疗失眠症提供了较可靠的辅助决策工具。
Objective To establish a predictive model of acupuncture treatment of insomnia and to create a profile of suitable populations for acupuncture schemes,so as to help improve clinical efficacy.Methods The data was sourced from a prospective clinical study on acupuncture treatment of insomnia by“Tongdu Tiaowei”acupoint prescription(Baihui[GV20],Yintang[EX-HN3],bilateral Shenmai[BL62]and bilateral Zhaohai[KI6]).Data from 113 insomnia patients were included in the analysis of the present study,with the reduction rate of the Pittsburgh Sleep Quality Index(PSQI)served as the overall clinical efficacy evaluation.First,the feature selection was performed using univariate logistic regression and Boruta algorithm,and the prediction accuracy of the three boosting algorithms—adaptive boosting,gradient boosting,and extreme gradient boosting(XGBoost)—was compared for selecting the best algorithm.The grid search and ten-fold cross-validation were used to optimize the hyperparameters of the best algorithm.The optimal dataset partitioning method was selected using stratified random partitioning,and the best cut-off value was determined based on the Youden index.The predictive model for the therapeutic efficacy was constructed and its performance was evaluated.Finally,SHAP(shapley additive explanation)analysis was used to visually interpret the model.Results The features included in the model were the proportion of stage N1 to total sleep duration,the proportion of stage N2 to total sleep duration,R latency from lights out,stage N2 latency from lights out,the awake time after sleep onset,PSQI sleep efficiency score,and the presence of an old tongue(a tongue picture of a dry,rough texture and an old body).XGBoost was identified as the best algorithm,with the optimal probability threshold of 0.76,a corresponding precision of 0.91,a recall of 0.91,a F1 score of 0.91,an accuracy of 0.91,and an area under curve(AUC)of 0.82.Patients who meet the following conditions are more likely to respond to“Tongdu Tiaowei”acuoint stimulation:the proportion of N1 phase was about 6%—70% of the total sleep duration,N2 phase latency was less than about 40 min from the time when the lights were off,the wakefulness time was less than about 75 min or 100—300 min after falling asleep,the R phase latency was more than about 75 min from the time when the lights were off.The N2 phase were about 20%—50% of the total sleep duration,PSQI sleep efficiency score was 2 or 3,and there was no appearance of“old tongue”.Conclusion The predictive model of the efficacy of acupuncture treatment for insomnia established using XGBoost,along with the preliminary profile of the suitable population constructed using SHAP,provides a reliable auxiliary decision-making tool for acupuncture treatment of insomnia.
作者
王驰
秦珊
刘成勇
王晓秋
刘恺
江静
刘恩琦
孙菊光
陆瑾
丁敏
吴文忠
WANG Chi;QIN Shan;LIU Cheng-yong;WANG Xiao-qiu;LIU Kai;JIANG Jing;LIU En-qi;SUN Ju-guang;LU Jin;DING Min;WU Wen-zhong(Department of Acupuncture-moxibustion and Rehabilitation,the Affiliated Hospital of Nanjing University of Chinese Medicine,Nanjing 210001,China;Department of Acupuncture-moxibustion,Xuzhou Hospital of Traditional Chinese Medicine,Xuzhou 221018,Jiangsu Province;Department of Acupuncture-moxibustion,Nanjing Hospital of Chinese Medicine,Nanjing 210022;Department of Acupuncture-moxibustion,Wuxi Hospital of Chinese Medicine,Wuxi 214071,Jiangsu Province)
出处
《针刺研究》
北大核心
2025年第8期954-964,共11页
Acupuncture Research
基金
江苏省科学技术厅(重点研发项目)(No.BE2021751)
国家自然科学基金面上项目(No.82274631)
国家自然科学基金青年项目(No.82205252)
江苏省科学技术厅(社发面上项目)(No.BE2023793)
国家中医临床研究基地开放课题(No.JD2022SZXMS01)
江苏省研究生科研与实践创新计划项目(No.SJCX23_0869)
江苏省自然科学基金青年基金项目(No.BK20210986)。
关键词
失眠症
针刺
机器学习
预测模型
辅助决策
Insomnia
Acupuncture
Machine learning
Predictive model
Auxiliary decision-making
