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We estimated the rate of reactivation tuberculosis (TB) in the United States, overall and by population subgroup, using data on TB cases and Mycobacterium tuberculosis isolate genotyping reported to the Centers for Disease Control and Prevention during 2006–2008. The rate of reactivation TB was defined as the number of non-genotypically clustered TB cases divided by the number of person-years at risk for reactivation due to prevalent latent TB infection (LTBI). LTBI was ascertained from tuberculin skin tests given during the 1999–2000 National Health and Nutrition Examination Survey. Clustering of TB cases was determined using TB genotyping data collected by the Centers for Disease Control and Prevention and analyzed via spatial scan statistic. Of the 39,920 TB cases reported during 2006–2008, 79.7% were attributed to reactivation. The overall rate of reactivation TB among persons with LTBI was estimated as 0.084 (95% confidence interval (CI): 0.083, 0.085) cases per 100 person-years. Rates among persons with and without human immunodeficiency virus coinfection were 1.82 (95% CI: 1.74, 1.89) and 0.073 (95% CI: 0.070, 0.075) cases per 100 person-years, respectively. The rate of reactivation TB among persons with LTBI was higher among foreign-born persons (0.098 cases/100 person-years; 95% CI: 0.096, 0.10) than among persons born in the United States (0.082 cases/100 person-years; 95% CI: 0.080, 0.083). Differences in rates of TB reactivation across subgroups support current recommendations for targeted testing and treatment of LTBI.

SummaryBackgroundTackling tuberculosis requires testing and treatment of latent tuberculosis in high-risk groups. The aim of this study was to estimate the predictive values of the tuberculin skin test (TST) and two interferon-γ release assays (IGRAs) for the development of active tuberculosis in high-risk groups—ie, people in recent contact with active tuberculosis cases and from high-burden countries.MethodIn this prospective cohort study, we recruited participants from 54 centres (eg, clinics, community settings) in London, Birmingham, and Leicester in the UK. Participants were eligible if they were aged 16 years or older and at high risk for latent tuberculosis infection (ie, recent contact with someone with active tuberculosis [contacts] or a migrant who had arrived in the UK in the past 5 years from—or who frequently travelled to—a country with a high burden of tuberculosis [migrants]). Exclusion criteria included prevalent cases of tuberculosis, and participants who were treated for latent tuberculosis after a positive test result in this study. Each participant received three tests (QuantiFERON-TB Gold-In Tube, T-SPOT.TB, and a Mantoux TST). A positive TST result was reported using three thresholds: 5 mm (TST-5), 10 mm (TST-10), and greater than 5 mm in BCG-naive or 15 mm in BCG-vaccinated (TST-15) participants. Participants were followed up from recruitment to development of tuberculosis or censoring. Incident tuberculosis cases were identified by national tuberculosis databases, telephone interview, and review of medical notes. Our primary objective was to estimate the prognostic value of IGRAs compared with TST, assessed by the ratio of incidence rate ratios and predictive values for tuberculosis development. The study was registered with ClinicalTrials.gov, NCT01162265, and is now complete.FindingsBetween May 4, 2010, and June 1, 2015, 10 045 people were recruited, of whom 9610 were eligible for inclusion. Of this cohort, 4861 (50·6%) were contacts and 4749 (49·4%) were migrants. Participants were followed up for a median of 2·9 years (range 21 days to 5·9 years). 97 (1·0%) of 9610 participants developed active tuberculosis (77 [1·2%] of 6380 with results for all three tests). In all tests, annual incidence of tuberculosis was very low in those who tested negatively (ranging from 1·2 per 1000 person-years, 95% CI 0·6–2·0 for TST-5 to 1·9 per 1000 person-years, 95% CI 1·3–2·7, for QuantiFERON-TB Gold In-Tube). Annual incidence in participants who tested positively were highest for T-SPOT.TB (13·2 per 1000 person-years, 95% CI 9·9–17·4), TST-15 (11·1 per 1000 person-years, 8·3–14·6), and QuantiFERON-TB Gold In-Tube (10·1 per 1000 person-years, 7·4–13·4). Positive results for these tests were significantly better predictors of progression than TST-10 and TST-5 (eg, ratio of test positivity rates in those progressing to tuberculosis compared with those not progressing T-SPOT.TB vs TST-5: 1·99, 95% CI 1·68–2·34; p<0·0001). However, TST-5 identified a higher proportion of participants who progressed to acti...

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Background The number of proposed prognostic models for COVID-19, which aim to predict disease outcomes, is growing rapidly. It is not known whether any are suitable for widespread clinical implementation. We addressed this question by independent and systematic evaluation of their performance among hospitalised COVID-19 cases. Methods We conducted an observational cohort study to assess candidate prognostic models, identified through a living systematic review. We included consecutive adults admitted to a secondary care hospital with PCR-confirmed or clinically diagnosed community-acquired COVID-19 (1st February to 30th April 2020). We reconstructed candidate models as per their original descriptions and evaluated performance for their original intended outcomes (clinical deterioration or mortality) and time horizons. We assessed discrimination using the area under the receiver operating characteristic curve (AUROC), and calibration using calibration plots, slopes and calibration-in-the-large. We calculated net benefit compared to the default strategies of treating all and no patients, and against the most discriminating predictor in univariable analyses, based on a limited subset of a priori candidates. Results We tested 22 candidate prognostic models among a cohort of 411 participants, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. The highest AUROCs were achieved by the NEWS2 score for prediction of deterioration over 24 hours (0.78; 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78; 0.74-0.82). Calibration appeared generally poor for models that used probability outcomes. In univariable analyses, admission oxygen saturation on room air was the strongest predictor of in-hospital deterioration (AUROC 0.76; 0.71-0.81), while age was the strongest predictor of in-hospital mortality (AUROC 0.76; 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than using the most discriminating univariable predictors to stratify treatment, across a range of threshold probabilities. Conclusions Oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated offer incremental value for patient stratification to these univariable predictors.

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As the capacity for generating large scale data continues to grow the ability to extract meaningful biological knowledge from it remains a limitation. Here we describe the development of a new fixed repertoire of transcriptional modules. It is meant to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome profiling data. It is supported by customized resources, which include analysis workflows, fingerprint grid plots data visualizations, interactive web applications. These provide access to a vast number of module-specific functional profiling reports, reference transcriptional profiles, and give users the ability to visualize changes in transcript abundance across the modular repertoire at different granularity levels. A use case focusing on a set of six modules comprising interferon-inducible genes is also provided. Taken together, this well-characterized set of modules may be employed for the interpretation and benchmarking of blood transcriptome profiles obtained within and across patient cohorts.

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