The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis

Rosenthal, Alex; Gabrielian, Andrei; Engle, Eric; Hurt, Darrell E.; Alexandru, Sofia; Crudu, Valeriu; Sergueev, Eugene; Kirichenko, V. V.; Lapitskii, Vladzimir; Snezhko, Eduard; Kovalev, Vassili; Astrovko, Andrei; Skrahina, Alena; Taaffe, Jessica; Harris, Michael A.; Long, Alyssa; Wollenberg, Kurt; Akhundova, Irada; Ismayilova, Sharafat; Skrahin, Aliaksandr; Mammadbayov, Elcan; Gadirova, Hagigat; Abuzarov, Rafik; Seyfaddinova, Mehriban; Avaliani, Zaza; Strâmbu, Irina; Zaharia, Dragoş; Muntean, Alexandru; Ghita, Eugenia; Bogdan, Miron Alexandru; Mindru, Roxana; Spînu, Victor; Sora, Alexandra; Ene, Catalina; Vashakidze, Sergo; Shubladze, Natalia; Nanava, Ucha; Tuzikov, Alexander V.; Tartakovsky, Michael

doi:10.1128/jcm.01013-17

Cited by 99 publications

(85 citation statements)

References 21 publications

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“…The MDR-PTB cases and XDR-PTB cases were from the NIAID TB Portals Program (TBPP) <https://tbportals.niaid.nih.gov> (7). dsptb cases were from base the TBPP collection as well as the Shenzhen Center for Chronic Disease Control, Shenzhen, China (table-1).…”

Section: Methodsmentioning

confidence: 99%

Prediction of multiple drug resistant pulmonary tuberculosis against drug sensitive pulmonary tuberculosis by CT nodular consolidation sign

Huang

Skrahin

et al. 2019

Preprint

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Multidrug-resistant tuberculosis (mdrtb) refers to TB infection resistant to at least two most powerful anti-TB drugs, isoniazid and rifampincin. It has been estimated that globally 3.5% (which can be much higher in some regions) of newly diagnosed TB patients, and 20.5% of previously treated patients had mdrtb. Extensively drug-resistant TB (xdrtb) has resistance to rifampin and isoniazid, as well as to any member of the quinolone family and at least one of the second line injectable drugs: kanamycin, amikacin and capreomycin. xdrtb accounts for 4-20% of mdrtb. Early detection and targeted treatment are priorities for mdrtb/xdrtb control. The suspicion of mdr/xdr -pulmonary TB (mdrptb or xdrptb) by chest imaging shall suggest intensive diagnostic testing for mdrptb/xdrptb. We hypothesize that multiple nodular consolidation (NC) may serve one of the differentiators for separating dsptb vs mdrptb/xdrptb cases. For this study, mdrptb cases (n=310) and XDR-PTB cases (⋂=I58) were from the NIAID TB Portals Program (TBPP) <https://tbportals.niaid.nih.gov>. Drug sensitive pulmonary TB (dsptb) cases were from the TBPP collection (n=112) as well as the Shenzhen Center for Chronic Disease Control (n=111), Shenzhen, China, and we excluded patients with HIV(+) status. Our study shows NC, particularly multiple NCs, is more common in mdrptb than in dsptb, and more common in xdrptb than in mdrptb. For example, 2.24% of dsptb patients, 13.23% of mdrptb patients, and 20.89% of xdrptb patients, respectively, have NCs with diameter >= 10mm equal or more than 2 in number.

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Section: Methodsmentioning

confidence: 99%

Prediction of multiple drug resistant pulmonary tuberculosis against drug sensitive pulmonary tuberculosis by CT nodular consolidation sign

Huang

Skrahin

et al. 2019

Preprint

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“…The National Institute of Allergy and Infectious Diseases (NIAID) Office of Cyber Infrastructure and Computational Biology (OCICB) established the TB Portals Program as a multi-national collaboration for TB data sharing and analysis to advance TB research [ 8 ]. A consortium of clinicians and scientists from countries with a heavy burden of TB, especially drug-resistant TB, work together with data scientists and IT professionals to collect multidomain TB data and make it available to the clinical and research communities.…”

Section: Methodsmentioning

confidence: 99%

Feature selection and prediction of treatment failure in tuberculosis

et al. 2018

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BackgroundTuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control.ObjectiveTo identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.MethodsOn a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone.ResultsThe complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30–51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance.ConclusionMachine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.

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“…To understand the genomic basis of DR-TB in Moldova, we carried out whole-genome sequencing of Mtb isolates from 190 MDR (non-XDR), XDR, and drug-sensitive TB patients, identified and collected as part of the TB Portals Program [12]. Paired longitudinal samples from 87 of these patients with recurrent cases were used to characterize the nature of Mtb reactivation or reinfection in these cases.…”

Section: Introductionmentioning

confidence: 99%

A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status

et al. 2020

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Background: Recurrence of drug-resistant tuberculosis (DR-TB) after treatment occurs through relapse of the initial infection or reinfection by a new drug-resistant strain. Outbreaks of DR-TB in high burden regions present unique challenges in determining recurrence status for effective disease management and treatment. In the Republic of Moldova the burden of DR-TB is exceptionally high, with many cases presenting as recurrent. Methods: We performed a retrospective analysis of Mycobacterium tuberculosis from Moldova to better understand the genomic basis of drug resistance and its effect on the determination of recurrence status in a high DR-burden environment. To do this we analyzed genomes from 278 isolates collected from 189 patients, including 87 patients with longitudinal samples. These pathogen genomes were sequenced using Illumina technology, and SNP panels were generated for each sample for use in phylogenetic and network analysis. Discordance between genomic resistance profiles and clinical drug-resistance test results was examined in detail to assess the possibility of mixed infection. Results: There were clusters of multiple patients with 10 or fewer differences among DR-TB samples, which is evidence of person-to-person transmission of DR-TB. Analysis of longitudinally collected isolates revealed that many infections exhibited little change over time, though 35 patients demonstrated reinfection by divergent (number of differences > 10) lineages. Additionally, several same-lineage sample pairs were found to be more divergent than expected for a relapsed infection. Network analysis of the H3/4.2.1 clade found very close relationships among 61 of these samples, making differentiation of reactivation and reinfection difficult. There was discordance between genomic profile and clinical drug sensitivity test results in twelve samples, and four of these had low level (but not statistically significant) variation at DR SNPs suggesting low-level mixed infections. Conclusions: Whole-genome sequencing provided a detailed view of the genealogical structure of the DR-TB epidemic in Moldova, showing that reinfection may be more prevalent than currently recognized. We also found increased evidence of mixed infection, which could be more robustly characterized with deeper levels of genomic sequencing.

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The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis

Cited by 99 publications

References 21 publications

Prediction of multiple drug resistant pulmonary tuberculosis against drug sensitive pulmonary tuberculosis by CT nodular consolidation sign

Prediction of multiple drug resistant pulmonary tuberculosis against drug sensitive pulmonary tuberculosis by CT nodular consolidation sign

Feature selection and prediction of treatment failure in tuberculosis

A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status

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