肺结核患者短程督导治疗期死亡概率预测模型建立与评价

目的 建立肺结核患者短程督导治疗期死亡风险预测模型，并对其预测效果进行评价，为降低肺结核病死率提供依据。方法 采用回顾性队列分析方法，收集天津市2014—2017年结核病管理信息系统中7 032例肺结核患者的基本信息、疾病特征和督导期治疗转归情况。采用多因素非条件Logistic回归分析筛选变量，构建患者督导期死亡风险预测模型，采用Hosmer-Lemeshow检验评价死亡预测模型的拟合优度，并绘制受试者工作特征（ROC）曲线评价模型预测效果。结果7 032例肺结核患者短程督导治疗期内存活6 711例（生存组），死亡321例（死亡组），病死率为4.56%。多因素Logistic回归分析结果显示，男性（OR=1.922）、高龄（OR=1.062）、复治肺结核（OR=1.539）、首次痰菌结果阳性（OR=1.936）、就诊延误（OR=1.401）、人类免疫缺陷病毒（HIV）阳性（OR=4.153）为肺结核患者死亡的独立 危 险 因 素 。 建 立Logistic回 归 方 程 为Logit（P）=ln［P（/ 1-P）］=0.653X1+0.061X2+0.431X4+0.661X5+0.337X6+1.424X9-9.191，其ROC曲线下面积为0.806（95%CI：0.784~0.828），预测概率最佳临界点0.054，敏感度为76.36%、特异度为81.58%。结论 该Logistic回归模型作为肺结核患者短程督导治疗期死亡风险预测模型，其拟合度和预测效能均较好，具有较好的预测价值。

Establishment and evaluation of a predictive model for probability of death in patients with pulmonary tuberculosis during directly observed treatment short-course

Objective To establish and evaluate a mathematical model for predicting the probability of death in patients with pulmonary tuberculosis (TB) during directly observed treatment short-course (DOTS) and to provide evidences for reducing TB-related mortality． Methods Retrospective cohort analysis was used to collect the basic information, disease characteristics and treatment outcomes of 7 032 TB patients from National TB Management Information System in Tianjin from 2014 to 2017. The multivariate and unconditional Logistic regression analysis was used to select the variables, and establish a predictive model for probability of death in patients with pulmonary TB during DOTS. The goodness of fit of the predictive model for probability of death was evaluated by Hosmer-Lemeshow test, and the receiver operating charactenstic (ROC) curve was constructed to assess the performance of the prediction model. Results There were 6 711 living cases (survival group) and 321 dead cases (death group) in 7 032 TB patients during DOTS, and the mortality rate was 4.56%. Multivariate Logistic regression analysis showed that male (OR=1.922), older age (OR=1.062), retreatment of tuberculosis (OR=1.539), first sputum bacteria positive (OR=1.936), delayed seeking medication (OR=1.401) and human immunodeficiency virus (HIV) positive (OR=4.153) were independent risk factors of mortality in the TB patients. The Logistic regression equation was established as follows: Logit (P) =ln P/(1-P)] = 0.653X1 +0.061X2 +0.431X4 +0.661X5 + 0.337X6 +1.424X9-9.191. The values of area under the ROC curve were 0.806 (95%CI：0.784-0.828). The best critical point of prediction probability was 0.054, the sensitivity was 76.36% and the specificity was 81.58%. Conclusion As a predictive model for probability of death in the TB patients during DOTS, its fitting degree and prediction efficiency of the Logistic regression model are better, and the model has better prediction value.