Each day more than 500 children younger than 15 years die from tuberculosis. Considerable progress has been made to control tuberculosis, but the impact on reducing the burden of childhood tuberculosis lags behind that in adults. A key barrier to decreasing morbidity and mortality associated with childhood tuberculosis is the paucity of accurate and feasible diagnostic tools for this population. WHO estimates that 58% of children younger than 5 years with tuberculosis are never diagnosed or reported.
BACKGROUND
There are few data on the treatment of children and adolescents with multidrug-resistant (MDR) or rifampicin-resistant (RR) tuberculosis, especially with more recently available drugs and regimens. We aimed to describe the clinical and treatment characteristics and their associations with treatment outcomes in this susceptible population.
METHODS
We conducted a systematic review and individual participant data meta-analysis. Databases were searched from Oct 1, 2014, to March 30, 2020. To be eligible, studies must have included more than five children or adolescents (0-19 years of age) treated for microbiologically confirmed or clinically diagnosed MDR or RR tuberculosis within a defined treatment cohort, and reported on regimen composition and treatment outcomes. Abstracts were screened independently by two authors to identify potentially eligible records. Full texts were reviewed by two authors independently to identify studies meeting the eligiblity criteria. For studies meeting eligiblity criteria, anonymised individual patient data was requested and individiual level data included for analysis. The main outcome assessed was treatment outcome defined as treatment success (cure or treatment completed) versus unfavourable outcome (treatment failure or death). Multivariable logistic regression models were used to identify associations between clinical and treatment factors and treatment outcomes. This study is registered with Prospero (CRD42020187230).
FINDINGS
1417 studies were identified through database searching. After removing duplicates and screening for eligibility, the search identified 23 369 individual participants from 42 studies, mostly from India and South Africa. Overall, 16 825 (72·0%) were successfully treated (treatment completed or cured), 2848 died (12·2%), 722 (3·1%) had treatment failure, and 2974 (12·7%) were lost to follow-up. In primary analyses, the median age was 16 (IQR 13-18) years. Of the 17 764 (87·1%) participants with reported HIV status, 2448 (13·8%) were living with HIV. 17 707 (89·6%) had microbiologically confirmed tuberculosis. After adjusting for significant factors associated with treatment outcome, the use of two (adjusted odds ratio [OR] 1·41 [95% CI 1·09-1·82]; p=0·008) or three (2·12 [1·61-2·79]; p<0·0001) WHO-classified group A drugs (bedaquiline, moxifloxacin, levofloxacin, and linezolid) compared with the use of no group A drugs at all was positively associated with treatment success.
INTERPRETATION
Younger and clinically diagnosed children are underrepresented among those treated for MDR and RR tuberculosis and should be a focus for case-finding efforts. Overall treatment outcomes in our analysis were better than in adults but lower than the international targets of 90% or more individuals successfully treated. Treatment with more group A drugs was associated with better treatment outcomes in children and adolescents, highlighting the need for more rapid access to these drugs and improved regimens.
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.