{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ridolfi F"],"funding":["National Institute of Allergy and Infectious Diseases","Conselho Nacional de Desenvolvimento Científico e Tecnológico","Departamento de Ciência e Tecnologia","NIAID NIH HHS","Coordenação de Aperfeiçoamento de Pessoal de Nível Superior"],"pagination":["813-823"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10938211"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["229(3)"],"pubmed_abstract":["<h4>Background</h4>Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates.<h4>Methods</h4>We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk.<h4>Results</h4>There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74-.83) and fit (optimism-corrected slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75-4.09]) and CM use (HR, 5.26 [95% CI, 2.63-10.52]) increased TB-ADR risk.<h4>Conclusions</h4>The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs."],"journal":["The Journal of infectious diseases"],"pubmed_title":["Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil."],"pmcid":["PMC10938211"],"funding_grant_id":["U01 AI069923","R01 AI120790","R01 AI147765","R01 A1120790","F31 AI152614","U01 AI172064"],"pubmed_authors":["Peetluk LS","Rocha M","Rezende A","Staats C","Sterling TR","Andrade BB","Cordeiro-Santos M","de Oliveira JG","Carvalho AC","Benjamin A","Regional Prospective Observational Research in Tuberculosis (RePORT)–Brazil Consortium","Amorim G","Marin J","Kritski AL","Nogueira B","Ridolfi F","Rolla VC","Brito A","Turner M","Figueiredo MC","Sant'Anna FM","Spener R","Haas DW","Araujo-Pereira M"],"additional_accession":[]},"is_claimable":false,"name":"Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil.","description":"<h4>Background</h4>Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates.<h4>Methods</h4>We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk.<h4>Results</h4>There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74-.83) and fit (optimism-corrected slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75-4.09]) and CM use (HR, 5.26 [95% CI, 2.63-10.52]) increased TB-ADR risk.<h4>Conclusions</h4>The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2025-04-03T23:36:38.81Z","creation":"2025-04-03T23:36:38.81Z"},"accession":"S-EPMC10938211","cross_references":{"pubmed":["38262629"],"doi":["10.1093/infdis/jiae025"]}}