梁达, 赵晓银, 商越, 王兆芬, 马斌忠. 青海省结核病患者治疗结局影响因素分析及贝叶斯网络模型研究[J]. 疾病监测, 2022, 37(2): 210-213. DOI: 10.3784/jbjc.202103230151
引用本文: 梁达, 赵晓银, 商越, 王兆芬, 马斌忠. 青海省结核病患者治疗结局影响因素分析及贝叶斯网络模型研究[J]. 疾病监测, 2022, 37(2): 210-213. DOI: 10.3784/jbjc.202103230151
Liang Da, Zhao Xiaoyin, Shang Yue, Wang Zhaofen, Ma Binzhong. Analysis of influencing factors and Bayesian network model study of treatment outcome in tuberculosis patients in Qinghai[J]. Disease Surveillance, 2022, 37(2): 210-213. DOI: 10.3784/jbjc.202103230151
Citation: Liang Da, Zhao Xiaoyin, Shang Yue, Wang Zhaofen, Ma Binzhong. Analysis of influencing factors and Bayesian network model study of treatment outcome in tuberculosis patients in Qinghai[J]. Disease Surveillance, 2022, 37(2): 210-213. DOI: 10.3784/jbjc.202103230151

青海省结核病患者治疗结局影响因素分析及贝叶斯网络模型研究

Analysis of influencing factors and Bayesian network model study of treatment outcome in tuberculosis patients in Qinghai

  • 摘要:
      目的  分析2011 — 2019年青海省登记报告的结核病患者治疗结局的影响因素,并通过构建贝叶斯网络模型进行因果效应推断和条件概率分析。
      方法  通过全国结核病管理系统导出2011 — 2019年青海省登记报告的结核病例信息,描述其治疗结局现状,并利用多因素logistic回归分析结核病患者治疗结局的影响因素,将具有统计学意义的影响因素纳入贝叶斯网络模型中进行因果关联和条件概率推断。
      结果  2011 — 2019年青海省结核病患者治疗成功率为88.86%。 多因素logistic回归分析结果显示,患者来源中的因症就诊、转诊和追踪以及诊断分型是影响结核病患者治疗结局的保护因素,而高年龄组(≥55岁)、农牧民、患者来源中的健康检查及其他接触者检查、重症、复治和非全程管理督导是危险因素。 通过构建贝叶斯网络模型可以得出,患者来源、是否重症和管理方式与治疗结局存在因果关联,当因症就诊的非重症结核病患者被全程管理督导时,其治疗成功率最高(95.63%),出现不良结局概率最低(4.37%)。
      结论  年龄、职业、患者来源、诊断分型、重症、治疗分类和管理方式是结核病患者治疗结局的影响因素,因症就诊的非重症结核病患者被全程管理督导时治疗成功率最高。

     

    Abstract:
      Objective  To analyze the influencing factors for treatment outcome in tuberculosis (TB) patients registered in Qinghai province from 2011 to 2019, infer the causal effect and conditional probability analysis by establishing the Bayesian network model.
      Methods  The TB cases information registered in Qinghai from 2011 to 2019 were collected from the National Tuberculosis Management System for a descriptive analysis on the treatment outcomes of TB patients, and multivariate logistic regression analysis was used to identify the factors affecting the treatment outcomes of TB patients. The influencing factors with statistical significance were used in the Bayesian network model for causal correlation and conditional probability inferences.
      Results  There success rate of treatment among TB patients in Qinghai from 2011 to 2019 were 88.86%. The results of multivariate Logistic regression analysis showed that clinical consultation, referral, follow-up and diagnostic type were protective factors affecting the treatment outcomes in TB patients, while older age (≥55 years old), being farmers and herdsmen, being detected in health examination and other contact examination, severe disease, retreatment and non-full course management were the risk factors. The Bayesian network model concluded that the source of patients, disease severity and management mode had casual correlation with the treatment outcomes in TB patients. The mild TB patients who had sought medical care and received full cause supervision management had highest treatment success rate (95.63%) .
      Conclusion  Age, occupation, source of patients, diagnostic type, disease severity, treatment classification and management mode were the influencing factors of treatment outcomes in TB patients. The treatment success rate was highest in mild TB patients who sought medical care due to illness and received full-cause supervision management.

     

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