Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.
Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis.
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DOI：
10.1016/s2352-4642(23)00004-4
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发表时间：
2023-05
影响因子：
36.4
通讯作者：
Seddon, James A.
中科院分区：
文献类型：
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作者：
Gunasekera, Kenneth S.;Marcy, Olivier;Munoz, Johanna;Lopez-Varela, Elisa;Sekadde, Moorine P.;Franke, Molly F.;Bonnet, Maryline;Ahmed, Shakil;Amanullah, Farhana;Anwar, Aliya;Augusto, Orvalho;Aurilio, Rafaela Baroni;Banu, Sayera;Batool, Iraj;Brands, Annemieke;Cain, Kevin P.;Carratala-Castro, Lucia;Caws, Maxine;Click, Eleanor S.;Cranmer, Lisa M.;Garcia-Basteiro, Alberto L.;Hesseling, Anneke C.;Huynh, Julie;Kabir, Senjuti;Lecca, Leonid;Mandalakas, Anna;Mavhunga, Farai;Myint, AyeAye;Myo, Kyaw;Nampijja, Dorah;Nicol, Mark P.;Orikiriza, Patrick;Palmer, Megan;Sant'Anna, Clemax Couto;Siddiqui, Sara Ahmed;Smith, Jonathan P.;Song, Rinn;Thuong, Nguyen Thuy Thuong;Ung, Vibol;van der Zalm, Marieke M.;Verkuijl, Sabine;Viney, Kerri;Walters, Elisabetta G.;Warren, Joshua L.;Zar, Heather J.;Marais, Ben J.;Graham, Stephen M.;Debray, Thomas P. A.;Cohen, Ted;Seddon, James A.
许多儿童肺结核仍未得到诊断和治疗，相关的高发病率和死亡率。儿童结核病算法开发的最新进展纳入了预测模型，但到目前为止，研究规模较小，局限于局部，推广能力有限。我们的目标是评估目前使用的诊断算法的性能，并使用预测建模来开发基于证据的算法，以帮助向初级保健中心提出报告的儿童进行结核病治疗决策。在这项荟萃分析中，我们确定了来自世卫组织公开呼吁儿童和青少年结核病管理数据以及儿童结核病专家转介的个别参与者数据。我们纳入了前瞻性招募在结核病发病率高的国家的卫生保健中心就诊的10岁以下连续参与者的研究，以进行肺结核的临床评估。我们整理了参与者的个人数据，包括临床、细菌学和放射学信息，以及肺结核的标准化参考分类。使用这个数据集，我们首先回顾评估了几种现有的治疗决策算法的性能。然后，我们使用这些数据开发了两个多变量预测模型，其中包括在肺结核临床评估中使用的特征-一个有胸部X光特征，另一个没有-我们使用内部-外部交叉验证来研究每个模型的普适性。这两个模型的参数系数估计被分成两个评分系统，用于对具有预先指定的敏感性指标的结核病进行分类。这两个评分系统被用来开发两个实用的治疗决策算法，用于初级卫生保健环境。在来自12个国家的13项研究的4718名儿童中，1811名(38.4%)被归类为肺结核：541名(29.9%)经细菌检查确诊，1270名(70.1%)未确诊。现有的治疗决策算法具有高度可变的诊断性能。根据包括临床特征和胸部X线片特征的预测模型建立的评分系统对综合参考标准的综合敏感性为0.86[95%可信区间0·68~0.94]，特异性为0.37[0.15~0.66]。根据仅包括临床特征的模型得出的评分系统相对于综合参考标准的综合敏感性为0·84[95%可信区间0·66-0·93]，特异性为0·30[0·13-0·56]。每个模型的评分系统被放置在分类步骤之后，包括评估疾病敏锐度和与结核病相关的不良结果的风险，以开发治疗决策算法。我们采用了循证的方法来开发实用的算法来指导儿童的结核病治疗决定，而不考虑当地可用的资源。这一办法将使结核病发病率高、资源有限的初级保健环境中的卫生工作者能够启动儿童结核病治疗，以改善获得护理的机会，并减少结核病相关死亡率。这些算法已列入世卫组织关于儿童和青少年结核病管理的最新指南所附的业务手册。未来对算法的前瞻性评估，包括在这项工作中开发的算法，对于调查临床性能是必要的。世界卫生组织，美国国立卫生研究院。
Many children with pulmonary tuberculosis remain undiagnosed and untreated with related high morbidity and mortality. Recent advances in childhood tuberculosis algorithm development have incorporated prediction modelling, but studies so far have been small and localised, with limited generalisability. We aimed to evaluate the performance of currently used diagnostic algorithms and to use prediction modelling to develop evidence-based algorithms to assist in tuberculosis treatment decision making for children presenting to primary health-care centres. For this meta-analysis, we identified individual participant data from a WHO public call for data on the management of tuberculosis in children and adolescents and referral from childhood tuberculosis experts. We included studies that prospectively recruited consecutive participants younger than 10 years attending health-care centres in countries with a high tuberculosis incidence for clinical evaluation of pulmonary tuberculosis. We collated individual participant data including clinical, bacteriological, and radiological information and a standardised reference classification of pulmonary tuberculosis. Using this dataset, we first retrospectively evaluated the performance of several existing treatment-decision algorithms. We then used the data to develop two multivariable prediction models that included features used in clinical evaluation of pulmonary tuberculosis—one with chest x-ray features and one without—and we investigated each model's generalisability using internal–external cross-validation. The parameter coefficient estimates of the two models were scaled into two scoring systems to classify tuberculosis with a prespecified sensitivity target. The two scoring systems were used to develop two pragmatic, treatment-decision algorithms for use in primary health-care settings. Of 4718 children from 13 studies from 12 countries, 1811 (38·4%) were classified as having pulmonary tuberculosis: 541 (29·9%) bacteriologically confirmed and 1270 (70·1%) unconfirmed. Existing treatment-decision algorithms had highly variable diagnostic performance. The scoring system derived from the prediction model that included clinical features and features from chest x-ray had a combined sensitivity of 0·86 [95% CI 0·68–0·94] and specificity of 0·37 [0·15–0·66] against a composite reference standard. The scoring system derived from the model that included only clinical features had a combined sensitivity of 0·84 [95% CI 0·66–0·93] and specificity of 0·30 [0·13-0·56] against a composite reference standard. The scoring system from each model was placed after triage steps, including assessment of illness acuity and risk of poor tuberculosis-related outcomes, to develop treatment-decision algorithms. We adopted an evidence-based approach to develop pragmatic algorithms to guide tuberculosis treatment decisions in children, irrespective of the resources locally available. This approach will empower health workers in primary health-care settings with high tuberculosis incidence and limited resources to initiate tuberculosis treatment in children to improve access to care and reduce tuberculosis-related mortality. These algorithms have been included in the operational handbook accompanying the latest WHO guidelines on the management of tuberculosis in children and adolescents. Future prospective evaluation of algorithms, including those developed in this work, is necessary to investigate clinical performance. WHO, US National Institutes of Health.