基于Lasso-Logistic回归构建美沙酮维持治疗门诊患者继续使用毒品风险预测模型  

Based on Lasso-Logistic regression to build a continued drug use risk prediction model for methadone maintenance treatment patients

作　　者：刘雪娇 罗巍[1] 张波[2] 闻品渊 吴尊友[1] LIU Xue-jiao;LUO Wei;ZHANG Bo;WEN Pin-yuan;WU Zun-you(Department of HIV Prevention and Intervention,National Center for AIDS/STD Control and Prevention,Chinese Center for Diseases Control and Prevention,Beijing 102206,China;Yunnan Institute of Drug Abuse,Kunming 650228,China)

机构地区：[1]中国疾病预防控制中心性病艾滋病预防控制中心宣传教育与预防干预室,北京102206 [2]云南省药物依赖防治研究所,昆明650228

出　　处：《中华疾病控制杂志》2021年第12期1369-1373,1402,共6页Chinese Journal of Disease Control & Prevention

基　　金：国家科技重大专项(2018ZX10721102)。

摘　　要：目的探讨美沙酮维持治疗(methadone maintenance treatment,MMT)门诊患者入组治疗后继续使用毒品的影响因素,构建并验证继续使用毒品风险预测模型。方法以2013―2017年入组云南省MMT门诊的患者为研究对象,利用Lasso回归筛选出与患者继续使用毒品相关的变量,用于构建多因素Logistic回归分析模型;采用Bootstrap法进行模型内部验证并用列线图实现模型的可视化。结果本研究纳入的7899名研究对象在接受治疗的6个月内,共有4125(52.22%)人发生了继续使用毒品行为。Lasso回归筛选出9个与继续使用毒品相关的变量,分别是男性、独居、目前无工作、家庭关系一般、家庭关系较差、吸毒时长、过去1个月有注射行为、过去3个月曾被公安抓捕和过去3个月因吸毒而违法犯罪。预测模型的曲线下面积(area under curve,AUC)为0.70(95%CI:0.69~0.72)。结论本研究构建的风险预测模型具有较好的预测能力,可用于指导MMT门诊工作人员提早识别出治疗期间继续使用毒品的高风险人群。Objective To explore the associated risk factors of continued drug use in methadone maintenance treatment(MMT) patients,and to build a continued drug use risk prediction model.Methods Opioid users who newly enrolled in MMT clinics in Yunnan Province during 2013-2017 were included in the study.We used Lasso regression analysis to screen risk factors for continued drug use.We constructed a prediction model based on screened risk factors above by multivariable Logistic regression analysis.We used bootstrap method to internally validate the model.Further,a nomogram was established to visualize the model.Results Of 7 899 eligible patients,4 125(52.22%) cases continued to drug use in the first 6 months following enrollment.Nine variables collected at baseline for MMT patients were selected for the prediction model.i.e.,male,living alone,unemployed,normal family relationship,bad family relationship,duration of drug use,drug injection behavior in past month,caught by the police in past three months,and criminal behavior in past three months.The model exhibited well discrimination with an area under curve(AUC) of 0.70(95% CI:0.69-0.72).Conclusion The model presents good performance in predicting continued drug use risk for MMT newly enrolled patients,which can be used to help clinic staff to find early patients at high-risk of continuing use opioid while receiving MMT service.

关 键 词：美沙酮维持治疗 Lasso回归 风险预测模型 列线图 

分 类 号：R749.053[医药卫生—神经病学与精神病学] R181.2[医药卫生—临床医学]