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美沙酮维持治疗患者复吸毒品风险预测:基于lasso-logistic模型分析的回顾性队列研究

A prediction model for drug relapse among patients undergoing methadone maintenance treatment: a retrospective cohort study using lasso-logistic regression

  • 摘要:
    目的 分析福建省美沙酮维持治疗(MMT)患者复吸毒品(复吸)影响因素并构建风险预测模型。
    方法 采用回顾性队列研究,资料来源于戒毒药物维持治疗数据信息管理系统,纳入2006—2023年福建省参加MMT达到180天或期间复吸的患者。采用lasso-logistic回归筛选影响因素,构建风险预测模型。
    结果 共纳入MMT患者7 601例,复吸率为75.58%。女性(OR=0.801,95%CI=0.695~0.923)、吸毒年限越长(OR=0.985,95%CI=0.974~0.996)、入组年龄越大(OR=0.988,95%CI=0.981~0.997)是复吸的保护因素;入组前1个月有注射吸毒行为(OR=1.156,95%CI=1.034~1.293)、有工作(OR=1.184,95%CI=1.059~1.326)、吸毒朋友介绍来参加MMT(OR=1.273,95%CI=1.026~1.582)、入组前1个月平均每天吸毒次数越多(OR=1.018,95%CI=1.005~1.033)、来门诊路上花费时间越长(OR=1.004,95%CI=1.002~1.008)、参加过MMT(OR=1.217,95%CI=1.095~1.355)、乘公共汽车来MMT(OR=1.429,95%CI=1.193~1.712)和其他交通工具来MMT(OR=2.890,95%CI=2.033~4.106)是复吸的危险因素。风险预测模型的曲线下面积(AUC)为0.602(95%CI=0.588~0.617)。
    结论 福建省MMT患者复吸率较高,构建的风险预测模型可用于识别复吸高危人群。

     

    Abstract:
    Objective To analyze the influencing factors of drug relapse among patients undergoing methadone maintenance treatment (MMT) in Fujian province and construct a risk prediction model.
    Methods A retrospective cohort study was conducted. Data were sourced from the Data Information Management System of Drug Maintenance Treatment. Patients who underwent MMT for 180 days or relapsed in Fujian from 2006 to 2023 were included. Lasso-logistic regression was employed to screen the influencing factors, and a risk prediction model was constructed.
    Results The relapse rate of patients undergoing MMT was 75.58% (5 745/7 601). Female gender (OR = 0.801, 95%CI: 0.695–0.923), longer drug use duration (OR = 0.985, 95%CI: 0.974–0.996), and older enrollment age (OR = 0.988, 95%CI: 0.981–0.997) were protective factors for drug relapse. The risk factors for drug relapse were injection drug use in the month before enrollment (OR = 1.156, 95%CI: 1.034–1.293), being employed (OR = 1.184, 95%CI: 1.059–1.326), being referred to MMT by drug-using friends (OR = 1.273, 95%CI: 1.026–1.582), more frequent daily drug use in the month before enrollment (OR = 1.018, 95%CI: 1.005–1.033), longer travel time to the clinic (OR = 1.004, 95%CI: 1.002–1.008), previous participation in MMT (OR = 1.217, 95%CI: 1.095–1.355), coming to MMT by bus (OR = 1.429, 95%CI: 1.193–1.712), and using other means of transportation to MMT (OR = 2.890, 95%CI: 2.033–4.106). The area under curve (AUC) of the constructed risk prediction model was 0.602 (95%CI: 0.588–0.617).
    Conclusions The drug relapse rate of patients undergoing MMT is high in Fujian, and the constructed risk prediction model can be used to identify high-risk populations of drug relapse.

     

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