| Author | Peetluk, Lauren S. | |
| Author | Rebeiro, Peter F. | |
| Author | Ridolf, Felipe M. | |
| Author | Andrade, Bruno B. | |
| Author | Cordeiro-Santos, Marcelo | |
| Author | Kritski, Afranio | |
| Author | Durovni, Betina | |
| Author | Calvacante, Solange | |
| Author | Figueiredo, Marina C. | |
| Author | Haas, David W. | |
| Author | Liu, Dandan | |
| Author | Rolla, Valeria C. | |
| Author | Sterling, Timothy R. | |
| Access date | 2024-06-27T01:44:58Z | |
| Available date | 2024-06-27T01:44:58Z | |
| Document date | 2022 | |
| Citation | PEETLUK, Lauren S. et al. A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomess. Clinical Infectious Diseases, v. 74, n. 6, p. 973-982, Mar. 2022. | en_US |
| ISSN | 1058-4838 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/64699 | |
| Description | Collaborators: Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil Network: Renata Spener-Gomes, Alexandra Brito de Souza, Jaquelane Silva Jesus, Aline Benjamin, Flavia Marinho Sant'Anna, Francine Peixoto Ignácio, Maria Cristina Lourenço, Adriano Gomes-Silva, Jamile G de Oliveira, Adriana S R Moreira, Anna Cristina Calçada Carvalho, Elisangela C Silva, Mayla Mello, Michael S Rocha, Betania Nogueira, Vanessa Nascimento, Saulo Nery, Alice M S Andrade, Hayna Malta-Santos, Jéssica Rebouças-Silva, André M C Ramos, Sayonara Melo, Juan M Cubillos-Angulo, Laise de Moraes. | en_US |
| Sponsorship | This work was supported by the Departamento de Ciência e Tecnologia–Secretaria de Ciência e Tecnologia–Ministério da Saúde, Brazil (25029.000507/2013-07 to V. C. R.), National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (U01 AI069923, R01 A1120790, K01 AI131895 to P. F. R.; F31 AI152614 to L. S. P.; AI077505, AI110527, AI120790, TR002243 to D. W. H.), and the National Center for Advancing Translational Sciences (CTSA; TL1 TR002244 to L. S. P.). | en_US |
| Language | eng | en_US |
| Publisher | Oxford | en_US |
| Previous version | https://www.arca.fiocruz.br/handle/icict/48243 | |
| Rights | open access | en_US |
| Title | A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes | en_US |
| Type | Article | en_US |
| DOI | 10.1093/cid/ciab598 | |
| Abstract | Background: Despite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status. Methods: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures. Results: Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval, .73-.80) and was well calibrated (optimism-corrected intercept and slope, -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. Conclusions: Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Epidemiology. Nashville, Tennessee, USA / Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, Tennessee, USA. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Epidemiology. Nashville, Tennessee, USA / Vanderbilt University School of Medicine. Department of Biostatistics. Nashville, Tennessee, USA. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, Tennessee, USA / Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Brazilian Institute for Tuberculosis Research. José Silveira Foundation. Salvador, BA, Brazil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Salvador University. Laureate Universities. Salvador, BA, Brazil / Escola Bahiana de Medicina e Saúde Pública. Salvador, BA, Brasil / Faculdade de Tecnologia e Ciências. Curso de Medicina. Salvador, BA, Brasil. | en_US |
| Affilliation | Fundação Medicina Tropical Dr. Heitor Vieira Dourado. Manaus, AM, Brasil / Universidade do Estado do Amazonas. Manaus, AM, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, Tennessee, USA. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, Tennessee, USA / Meharry Medical College. Department of Internal Medicine. Nashville, Tennessee, USA. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, Tennessee, USA. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, Tennessee, USA. | en_US |
| Subject | Pulmonary tuberculosis | en_US |
| Subject | Prognosis | en_US |
| Subject | Prediction model | en_US |
| Subject | Epidemiologic research | en_US |
| Subject | HIV coinfection | en_US |
| e-ISSN | 1537-6591 | |
