Journal Article > ResearchFull Text
Lancet Child Adolesc Health. 13 March 2023; Online ahead of print; DOI:10.1016/S2352-4642(23)00004-4
Gunasekera KS, Marcy O, Muñoz J, Lopez-Varela E, Sekadde MP, et al.
Lancet Child Adolesc Health. 13 March 2023; Online ahead of print; DOI:10.1016/S2352-4642(23)00004-4
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.
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.
Journal Article > ResearchAbstract Only
Int J STD AIDS. 1 December 2009; Volume 20 (Issue 12); 863-868.; DOI:10.1258/ijsa.2008.008401
Bhatt NB, Gudo ES, Sema C, Bila D, Di Mattei P, et al.
Int J STD AIDS. 1 December 2009; Volume 20 (Issue 12); 863-868.; DOI:10.1258/ijsa.2008.008401
Français
Seven hundred and four HIV-1/2-positive, antiretroviral therapy (ART) naïve patients were screened for HTLV-1 infection. Antibodies to HTLV-1 were found in 32/704 (4.5%) of the patients. Each co-infected individual was matched with two HIV mono-infected patients according to World Health Organization clinical stage, age +/-5 years and gender. Key clinical and laboratory characteristics were compared between the two groups. Mono-infected and co-infected patients displayed similar clinical characteristics. However, co-infected patients had higher absolute CD4+ T-cell counts (P = 0.001), higher percentage CD4+ T-cell counts (P < 0.001) and higher CD4/CD8 ratios (P < 0.001). Although HIV plasma RNA viral loads were inversely correlated with CD4+ T-cell-counts in mono-infected patients (P < 0.0001), a correlation was not found in co-infected individuals (P = 0.11). Patients with untreated HIV and HTLV-1 co-infection show a dissociation between immunological and HIV virological markers. Current recommendations for initiating ART and chemoprophylaxis against opportunistic infections in resource-poor settings rely on more readily available CD4+ T-cell counts without viral load parameters. These guidelines are not appropriate for co-infected individuals in whom high CD4+ T-cell counts persist despite high HIV viral load states. Thus, for co-infected patients, even in resource-poor settings, HIV viral loads are likely to contribute information crucial for the appropriate timing of ART introduction.