Development of treatment-decision algorithms for children evaluated for pulmonary tuberculosis: an individual participant data meta-analysis

Abstract

Background: 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.

Methods: 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.

Findings: 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.

Interpretation: 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.

Funding: WHO, US National Institutes of Health.

Figure 1

Performance of existing treatment-decision algorithms at classifying tuberculosis Retrospective estimates of the pooled sensitivity (A) and specificity (B) of eight algorithms to guide decisions to treat children with presumptive pulmonary tuberculosis, had they been used to evaluate the children for whom we have individual participant data records. The reference classification of pulmonary tuberculosis included bacteriologically confirmed pulmonary tuberculosis and unconfirmed pulmonary tuberculosis. Modifications were made to the algorithms to maximise the use of the available individual participant dataset. NTLP=National TB and Leprosy Program. *Performance estimates from Marcy and colleagues. The algorithm was derived from only HIV-positive children in the individual participant dataset that excludes data from the cohort comprising HIV-positive children from Burkina Faso, Cambodia, Cameroon, and Viet Nam (from which the algorithm was developed). †Performance estimates by Gunasekera and colleagues. The algorithm was derived from only HIV-negative children in the individual participant dataset that excludes data from the South Africa population (from which the algorithm was developed).

Figure 2

Forest plot depicting performance of scaled scores from prediction model to classify tuberculosis with 85% sensitivity Study-level and pooled estimates of the (A) sensitivity and (B) specificity of classifying tuberculosis (composite reference standard: bacteriologically confirmed pulmonary tuberculosis and unconfirmed pulmonary tuberculosis) of the scores derived from the prediction model developed from the individual participant dataset to classify tuberculosis with 85% sensitivity.

Figure 3

Treatment-decision algorithm including chest x-ray features derived from the prediction model Tuberculosis treatment-decision algorithm for use among children younger than 10 years with symptoms suggestive of pulmonary tuberculosis, reproduced from the operational handbook accompanying the WHO consolidated guidelines on the management of tuberculosis in children and adolescents., Selection steps before entering the scoring system reflect recommendations from the WHO expert panel to enrich the probability of tuberculosis among the population of children proceeding through the algorithm to the model such that the probability would more closely reflect the preselected population producing the data from which the prediction model was built, while balancing the consequences of untreated tuberculosis in children at high risk. Scores associated with features from clinical history and physical exam and chest x-ray translate to risk of tuberculosis and are scaled from the prediction model developed from the individual participant dataset. Guidance on the practical use of this algorithm is outlined in the WHO operational handbook. LF-LAM=lateral flow urine lipoarabinomannan assay. WRD=WHO-recommended rapid diagnostic test.