RESUMO
Background: Nigeria is among the top five countries that have the highest gap between people reported as diagnosed and estimated to have developed tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted active case finding (ACF) interventions. Leveraging community-level data together with granular sociodemographic contextual information can unmask local hotspots that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined with open-source high-resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value < 0.001) than the yield in other locations in all four states. Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.
RESUMO
AIMS: Early recognition of adverse remodelling is important since outcome is unfavorable once patients with a systemic right ventricle (sRV) become symptomatic. We aimed assessing prognostic markers linked to short-term clinical evolution in this population. METHODS AND RESULTS: Thirty-three patients (76% male) with sRV (atrial switch repair for D-transposition of the great arteries and congenitally corrected transposition of the great arteries) underwent detailed phenotyping including exercise cardiac magnetic resonance and were followed over mean follow-up time of 3 years. Mean age was 40 ± 8 (range 26-57) years at latest follow-up. Adverse outcome was a composite of heart failure (HF) and tachyarrhythmia. Descriptive statistics and univariate cox regression analyses were performed. When compared with baseline: (i) most patients remained in New York Heart Association functional class I (76%), (ii) the degree of severity of the systemic atrioventricular valve regurgitation rose, and (iii) more electrical instability was documented at latest follow-up. Six (18%) of a total of 9 events were counted as first cardiovascular events (9% HF and 9% arrhythmia). NT-proBNP, oxygen pulse, left ventricle end-diastolic volume index (LVEDVi), and stroke volume index (SVi) of the subpulmonary left ventricle (LV) both in rest and at peak exercise were significantly associated with the first cardiovascular event. CONCLUSION: NT-proBNP was by far the best prognostic marker for clinical outcome. Adverse remodelling with increase of LVEDVi and SVi of the subpulmonary LV at rest and during exercise were associated with worse clinical outcome. We theorize that remodelling of the subpulmonary ventricle might be an early sign of a failing sRV circulation.