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.

Design of a placebo-controlled clinical trial of long-acting diltiazem and aspirin versus aspirin alone in patients receiving thrombolysis with a first acute myocardial infarction. Incomplete Infarction Trial of European Research Collaborators Evaluating Prognosis Post-Thrombolysis (diltiazem) (INTERCEPT) Research Group

Several pharmacologic forms of adjunctive therapy, designed to enhance the efficacy of thrombolysis following acute myocardial infarction (AMI), are being explored. However, few studies have assessed the use of standard secondary prevention therapies (beta-blockers, angiotensin-converting enzyme inhibitors, magnesium, calcium antagonists, etc.) for antecedent thrombolysis. Although calcium antagonists have not been shown to alter post-AMI mortality, diltiazem has been shown to reduce recurrent nonfatal infarction and myocardial ischemia following non-Q-wave AMI. Because both non-Q-wave AMI and AMI treated with thrombolytic therapy result in early reperfusion and clinical manifestations of "incomplete infarction" (i.e., aborted transmural infarction), we hypothesize that prophylactic administration of diltiazem to AMI patients who receive thrombolysis before other therapies might decrease ischemic complications. We have initiated a multicenter, randomized, placebo-controlled, double-blind, parallel-group comparison of long-acting diltiazem 300 mg/day and aspirin 160 mg/day versus aspirin 160 mg/day alone in up to 920 patients with an uncomplicated first AMI (no heart failure or left ventricular dysfunction) within 36 to 96 hours of receiving thrombolysis. Active enrollment is under way at 46 centers in the United Kingdom, Belgium, The Netherlands, and Denmark. This trial (known as the Incomplete INfarction Trial of European Research Collaborators Evaluating Prognosis Post-Thrombolysis [diltiazem], or INTERCEPT) represents the first long-term, large-scale, prospective study of a calcium antagonist administered post-thrombolysis as adjunctive therapy to AMI patients in which the primary trial objective is to assess the effect of blinded therapy on the 6-month cumulative occurrence of a combined clinical end point (cardiac death, recurrent nonfatal AMI, and medically refractory ischemia).

Urinary excretion of HMMA in children

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Hypoglycaemia due to an insulin-receptor antibody in Hodgkin's disease

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