Unsuccessful treatment in pulmonary tuberculosis: factors and a consequent predictive model
Unsuccessful treatment in pulmonary tuberculosis: factors and a consequent predictive model
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DOI：
10.1093/eurpub/ckx136
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发表时间：
2018-04-01
影响因子：
4.4
通讯作者：
Nunes, Carla
中科院分区：
文献类型：
--
作者：
Costa-Veiga, Ana;Briz, Teodoro;Nunes, Carla
背景：治愈在肺部疾病(PTB)中尤其重要，因为治疗不成功会增加发病率和对抗生素的耐药性。这项研究旨在确定葡萄牙肺结核治疗失败的个别因素，并开发相应的预测模型。方法：利用葡萄牙结核病监测数据库(SVIG-TB)，对2000年至2012年葡萄牙大陆地区15岁以上肺结核病例进行分析。不成功的治疗包括世卫组织的类别(失败、违约、死亡和转出)。在文献回顾的基础上，预测因素涉及社会人口、行为、疾病相关和治疗相关因素。用二元Logistic回归估计治疗失败的因素，并建立预测风险模型。结果：肺结核患者转归不良率为11.9%。预测模型包括结核病/艾滋病合并感染(OR4.93)、年龄以上(OR4.37)、静脉药物滥用(OR2.29)、其他疾病(不包括艾滋病和糖尿病，OR2.09)和再治疗(OR1.44)，具有较好的效度。结论：肺结核患者的总体治疗失败率符合世界卫生组织85%的治疗成功率标准。治疗失败的预测模型被证明是很好的。诺模图表示法可以早期、直观地识别风险较高的肺结核患者。该模型有可能被广泛用作一种预测工具。
Background: Cure is particularly valuable in pulmonary cases (PTB), as unsuccessful treatment fuels incidence and resistance to antibiotics. This study aims to identify individual factors of PTB unsuccessful treatment in Portugal and to develop a consequent predictive model. Methods: Using the Portuguese TB surveillance database (SVIG-TB), PTB cases older than 15 years notified from 2000 to 2012 in Continental Portugal were analyzed. Unsuccessful treatment included the WHO categories (failure, default, death and transferred out). Based on a literature review, predictors involved sociodemographic, behavioral, disease-related and treatment-related factors. Binary logistic regression was used to estimate unsuccessful treatment factors and to develop the predictive risk model. Results: The unsuccessful outcome rate in PTB patients was of 11.9%. The predictive model included the following factors: TB/HIV co-infection (OR 4.93), age over 64 years (OR 4.37), IV drugs abuse (OR 2.29), other diseases (excluding HIV and Diabetes, OR 2.09) and retreatment (OR 1.44), displaying a rather good validity. Conclusion: The overall treatment unsuccessful treatment rate in PTB patients complies with the 85% WHO success threshold. The predictive model of unsuccessful treatment proved well. Nomogram representation allows an early, intuitive identification of PTB patients at increased risk. The model is liable to widespread use as a prognostic tool.