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

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

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

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\u27s 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 Healt

A Prospective Evaluation of Xpert MTB/RIF Ultra for Childhood Pulmonary Tuberculosis in Uganda

BackgroundXpert MTB/RIF Ultra (Xpert Ultra) has improved the sensitivity to detect pulmonary tuberculosis (TB) in adults. However, there have been limited prospective evaluations of its diagnostic accuracy in children.MethodsWe enrolled children undergoing assessment for pulmonary TB in Kampala, Uganda, over a 12-month period. Children received a complete TB evaluation and were classified as Confirmed, Unconfirmed, or Unlikely TB. We calculated the sensitivity and specificity of Xpert Ultra among children with Confirmed vs Unlikely TB. We also determined the diagnostic accuracy with clinical, microbiological, and extended microbiological reference standards (MRSs).ResultsOf the 213 children included, 23 (10.8%) had Confirmed TB, 88 (41.3%) had Unconfirmed TB, and 102 (47.9%) had Unlikely TB. The median age was 3.9 years, 13% were HIV-positive, and 61.5% were underweight. Xpert Ultra sensitivity was 69.6% (95% confidence interval [CI]: 47.1-86.8) among children with Confirmed TB and decreased to 23.4% (95% CI: 15.9-32.4) with the clinical reference standard. Specificity was 100% (95% CI: 96.4-100) among children with Unlikely TB and decreased to 94.7% (95% CI: 90.5-97.4) with a MRS. Sensitivity was 52.9% (95% CI: 35.1-70.2) and specificity 95.5% (95% CI: 91.4-98.1) with the extended MRS. Of the 26 positive Xpert Ultra results, 6 (23.1%) were "Trace-positive," with most (5/6) occurring in children with Unconfirmed TB.ConclusionsXpert Ultra is a useful tool for diagnosing pulmonary TB in children, but there remains a need for more sensitive tests to detect culture-negative TB

The Role of C-Reactive Protein as a Triage Tool for Pulmonary Tuberculosis in Children.

BackgroundC-reactive protein (CRP) has shown promise as a triage tool for pulmonary tuberculosis (TB) in adults living with the human immunodeficiency virus. We performed the first assessment of CRP for TB triage in children.MethodsSymptomatic children less than 15 years old were prospectively enrolled in Kampala, Uganda. We completed a standard TB evaluation and measured CRP using a point-of-care assay. We determined the sensitivity and specificity of CRP to identify pulmonary TB in children using 10 mg/L and 5 mg/L cut-off points and generated a receiver operating characteristic (ROC) curve to determine alternative cut-offs that could approach the target accuracy for a triage test (≥90% sensitivity and ≥70% specificity).ResultsWe included 332 children (median age 3 years old, interquartile range [IQR]: 1-6). The median CRP level was low at 3.0 mg/L (IQR: 2.5-26.6) but was higher in children with Confirmed TB than in children with Unlikely TB (9.5 mg/L vs. 2.9 mg/L, P-value = .03). At a 10 mg/L cut-off, CRP sensitivity was 50.0% (95% confidence interval [CI], 37.0-63.0) among Confirmed TB cases and specificity was 63.3% (95% CI, 54.7-71.3) among children with Unlikely TB. Sensitivity increased to 56.5% (95% CI, 43.3-69.0) at the 5 mg/L cut-off, but specificity decreased to 54.0% (95% CI, 45.3-62.4). The area under the ROC curve was 0.59 (95% CI, 0.51-0.67), and the highest sensitivity achieved was 66.1% at a specificity of 46.8%.ConclusionsCRP levels were low in children with pulmonary TB, and CRP was unable to achieve the accuracy targets for a TB triage test

The socioeconomic burden of pediatric tuberculosis and role of child-sensitive social protection

Abstract Background Households of children with tuberculosis (TB) experience financial and social hardships, but TB-specific social protection initiatives primarily focus on adults. Methods We conducted a single-arm, pilot study of multi-component supportive benefits for children with pulmonary TB in Kampala, Uganda. At diagnosis, participants received in-kind coverage of direct medical costs, a cash transfer, and patient navigation. Caregivers were surveyed before diagnosis and 2 months into TB treatment on social and financial challenges related to their child’s illness, including estimated costs, loss of income and dissaving practices. Results We included 368 children from 321 households. Pre-diagnosis, 80.1% of caregivers reported that their child’s illness negatively impacted household finances, 44.1% of caregivers missed work, and 24% engaged in dissaving practices. Catastrophic costs (> 20% annual income) were experienced by 18.4% (95% CI 13.7–24.0) of households. School disruption was common (25.6%), and 28% of caregivers were concerned their child was falling behind in development. Two months post-diagnosis, 12 households (4.8%) reported being negatively affected by their child’s TB disease (difference -75.2%, 95% CI -81.2 to -69.2, p < 0.001), with limited ongoing loss of income (1.6%) or dissavings practices (0.8%). Catastrophic costs occurred in one household (0.4%) at 2 months post-diagnosis. Conclusions Households face financial and social challenges prior to a child’s TB diagnosis, and child-sensitive social protection support may mitigate ongoing burden

The socioeconomic burden of pediatric tuberculosis and role of child-sensitive social protection

BackgroundHouseholds of children with tuberculosis (TB) experience financial and social hardships, but TB-specific social protection initiatives primarily focus on adults.MethodsWe conducted a single-arm, pilot study of multi-component supportive benefits for children with pulmonary TB in Kampala, Uganda. At diagnosis, participants received in-kind coverage of direct medical costs, a cash transfer, and patient navigation. Caregivers were surveyed before diagnosis and 2&nbsp;months into TB treatment on social and financial challenges related to their child's illness, including estimated costs, loss of income and dissaving practices.ResultsWe included 368 children from 321 households. Pre-diagnosis, 80.1% of caregivers reported that their child's illness negatively impacted household finances, 44.1% of caregivers missed work, and 24% engaged in dissaving practices. Catastrophic costs (&gt; 20% annual income) were experienced by 18.4% (95% CI 13.7-24.0) of households. School disruption was common (25.6%), and 28% of caregivers were concerned their child was falling behind in development. Two months post-diagnosis, 12 households (4.8%) reported being negatively affected by their child's TB disease (difference -75.2%, 95% CI -81.2 to -69.2, p &lt; 0.001), with limited ongoing loss of income (1.6%) or dissavings practices (0.8%). Catastrophic costs occurred in one household (0.4%) at 2&nbsp;months post-diagnosis.ConclusionsHouseholds face financial and social challenges prior to a child's TB diagnosis, and child-sensitive social protection support may mitigate ongoing burden

Additional file 1 of The socioeconomic burden of pediatric tuberculosis and role of child-sensitive social protection

Additional file 1