Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil.
Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil.
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巴西结核病治疗期间药物不良反应的预测模型。
DOI：
10.1093/infdis/jiae025
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
RegionalProspectiveOb
中科院分区：
文献类型：
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作者：
Ridolfi,Felipe;Amorim,Gustavo;Peetluk,LaurenS;Haas,DavidW;Staats,Cody;Araújo-Pereira,Mariana;Cordeiro-Santos,Marcelo;Kritski,AfrânioL;Figueiredo,MarinaC;Andrade,BrunoB;Rolla,ValeriaC;Sterling,TimothyR;RegionalProspectiveOb
BackgroundTuberculosis（TB）治疗相关的药物不良反应（TB-ADRs）会对依从性和治疗成功率产生负面影响。MethodsWe开发了TB-ADRs的预测模型，考虑了开始标准TB治疗的药物敏感性肺结核患者。TB-ADR由参与者的主治医生确定，评估与TB药物的因果关系，受影响的器官系统和等级。TB-ADR的潜在基线预测因素包括合并用药（CM）使用、人类免疫缺陷病毒（HIV）状态、糖化血红蛋白（HbA 1c）、年龄、体重指数（BMI）、性别、物质使用和TB药物代谢变量（NAT 2乙酰化特征）。这些模型是通过自举向后选择开发的。考克斯回归被用来评估TB-ADR risk.ResultsThere是156 TB-ADR的945（11%）的参与者中的102。大多数TB-ADR为肝脏（n = 82 [53%]），严重程度为中度（2级; n = 121 [78%]），发生于NAT 2缓慢乙酰化者（n = 62 [61%]）。主要预测模型包括CM使用、HbA 1c、酒精使用、HIV血清阳性、BMI和年龄，具有稳健的性能（c-统计量= 0.79 [95%置信区间{CI}，0.74 - 0.83）和拟合（优化校正的斜率和截距分别为-0.09和0.94）。用NAT 2替代BMI的替代模型具有类似的性能。HIV血清阳性（风险比[HR]，2.68 [95%CI，1.75-4.09]）和CM使用（HR，5.26 [95%CI，2.63-10.52]）增加TB-ADR风险。
BackgroundTuberculosis (TB) treatment–related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates.MethodsWe developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk.ResultsThere were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred inNAT2slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74–.83) and fit (optimism-corrected slope and intercept of −0.09 and 0.94, respectively). An alternative model replacing BMI withNAT2had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75–4.09]) and CM use (HR, 5.26 [95% CI, 2.63–10.52]) increased TB-ADR risk.ConclusionsThe models, with clinical variables and withNAT2, were highly predictive of TB-ADRs.