Predicting treatment failure, death and drug resistance using a computed risk score among newly diagnosed TB patients in Tamaulipas, Mexico
Predicting treatment failure, death and drug resistance using a computed risk score among newly diagnosed TB patients in Tamaulipas, Mexico
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DOI：
10.1017/s0950268817001911
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发表时间：
2017-10-01
影响因子：
4.2
通讯作者：
Restrepo, B. I.
中科院分区：
文献类型：
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作者：
Abdelbary, B. E.;Garcia-Viveros, M.;Restrepo, B. I.
本研究的目的是开发一种方法，用于识别墨西哥塔毛利帕斯州新诊断的结核病（TB）患者的TB不良事件风险。2006年至2013年期间的监测数据（8431名受试者）用于根据预测模型制定风险评分。最终的模型显示，治疗方案失败的结核病患者更有可能最多接受小学教育，多药耐药（MDR）-结核病，抗酸杆菌涂片上有很少到中度的杆菌。死亡的结核病患者更有可能是患有耐多药结核病、艾滋病毒、营养不良和报告过度饮酒的老年男性。改良的风险评分对治疗失败和死亡具有较强的预测性（c-统计量分别为0.65和0.70），对耐药性具有中等的预测性（c-统计量为0.57）。在合并糖尿病的结核病患者中，风险评分显示死亡的可预测性中等（c-统计量0.68）。我们的研究结果表明，在临床环境中，使用我们的结核病治疗失败或死亡的风险评分将有助于识别这些个体，以进行量身定制的管理，以预防这些不良事件。相比之下，结核病监测数据集中的可用变量不是耐药性的可靠预测因素，这表明需要在诊断时进行及时检测。
The purpose of this study was to develop a method for identifying newly diagnosed tuberculosis (TB) patients at risk for TB adverse events in Tamaulipas, Mexico. Surveillance data between 2006 and 2013 (8431 subjects) was used to develop risk scores based on predictive modelling. The final models revealed that TB patients failing their treatment regimen were more likely to have at most a primary school education, multi-drug resistance (MDR)-TB, and few to moderate bacilli on acid-fast bacilli smear. TB patients who died were more likely to be older males with MDR-TB, HIV, malnutrition, and reporting excessive alcohol use. Modified risk scores were developed with strong predictability for treatment failure and death (c-statistic 0.65 and 0.70, respectively), and moderate predictability for drug resistance (c-statistic 0.57). Among TB patients with diabetes, risk scores showed moderate predictability for death (c-statistic 0.68). Our findings suggest that in the clinical setting, the use of our risk scores for TB treatment failure or death will help identify these individuals for tailored management to prevent these adverse events. In contrast, the available variables in the TB surveillance dataset are not robust predictors of drug resistance, indicating the need for prompt testing at time of diagnosis.