| Author | Rodrigues, Moreno M. S. | |
| Author | Barreto-Duarte, Beatriz | |
| Author | Vinhaes, Caian L. | |
| Author | Araújo-Pereira, Mariana | |
| Author | Fukutani, Eduardo R. | |
| Author | Bergamaschi, Keityane Bone | |
| Author | Kristki, Afrânio | |
| Author | Cordeiro-Santos, Marcelo | |
| Author | Rolla, Valeria C. | |
| Author | Sterling, Timothy R. | |
| Author | Queiroz, Artur T. L. | |
| Author | Andrade, Bruno B. | |
| Access date | 2024-08-14T13:30:13Z | |
| Available date | 2024-08-14T13:30:13Z | |
| Document date | 2024 | |
| Citation | RODRIGUES, Moreno M. S. et al. Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment. BMC Public Health, v. 24, n. 1, p. 1-9, May 2024. | en_US |
| ISSN | 1471-2458 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/65365 | |
| Sponsorship | Intramural Research Program of the Fundação Oswaldo Cruz (B.B.A.), Departamento de Ciência e Tecnologia (DECIT) - Secretaria de Ciência e Tecnologia (SCTIE) – Ministério da Saúde (MS), Brazil [25029.000507/2013-07 to V.C.R.], the National Institutes of Allergy and Infectious Diseases [U01-AI069923 to T.R.S, MSR, ALK, TRS, BBA, and MCS] and, Programa Inova FIOCRUZ/Edital Inovação Amazônia (Fiocruz, FAPEAM and FAPERO to MR). MAP and B.B.D received a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance code: 001). B.B.A, A.L.K., M.C.S., and V.C.R. are senior investigators of CNPq/Ministry of Science Technology. All authors
have read and agreed to the submitted version of the anuscript. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. | en_US |
| Language | eng | en_US |
| Publisher | BioMed Central | en_US |
| Rights | open access | en_US |
| Subject in Portuguese | Perda de acompanhamento | en_US |
| Subject in Portuguese | Aprendizado de máquina | en_US |
| Subject in Portuguese | Previsão de pontuação | en_US |
| Subject in Portuguese | Tuberculose | en_US |
| Title | Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment | en_US |
| Type | Article | en_US |
| DOI | 10.1186/s12889-024-18815-0 | |
| Abstract | Background: Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN). Methods: We performed a retrospective study of all TB cases reported to SINAN between 2015-2022; excluding children (<18 years-old), vulnerable groups or drug-resistant TB. For the score, data before treatment initiation were used. We trained and internally validated three different prediction scoring systems, based on Logistic Regression, Random Forest, and Light Gradient Boosting. Before applying our models we split our data into train (~80% data) and test (~20%), and then we compare model metrics using a test data set. Results: Of the 243,726 cases included, 41,373 experienced LTFU whereas 202,353 were successfully treated and cured. The groups were different with regards to several clinical and sociodemographic characteristics. The directly observed treatment (DOT) was unbalanced between the groups with lower prevalence in those who were LTFU. Three models were developed to predict LTFU using 8 features (prior TB, drug use, age, sex, HIV infection and schooling level) with different score composition approaches. Those prediction scoring system exhibited an area under the curve (AUC) ranging between 0.71 and 0.72. The Light Gradient Boosting technique resulted in the best prediction performance, weighting specificity, and sensibility. A user-friendly web calculator app was created (https://tbprediction.herokuapp.com/) to facilitate implementation. Conclusions: Our nationwide risk score predicts the risk of LTFU during ATT in Brazilian adults prior to treatment commencement. This is a potential tool to assist in decision-making strategies to guide resource allocation, DOT indications, and improve TB treatment adherence. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Fiocruz Rondônia. Laboratório de Análise e Visualização de Dados. Porto Velho, RO, Brasil. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Universidade Federal do Rio de Janeiro. Programa de Pós-Graduação em Clínica Médica. Rio de Janeiro, RJ, Brasil / Faculdade ZARNS. Instituto de Pesquisa Clínica e Translacional. Curso de Medicina. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Universidade de São Paulo. Faculdade de Medicina. Hospital das Clínicas de São Paulo. Departamento de Infectologia. São Paulo, SP, Brasil / Escola Bahiana de Medicina e Saúde Pública. Curso de Medicina. Salvador, BA, Brasil. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Faculdade ZARNS. Instituto de Pesquisa Clínica e Translacional. Curso de Medicina. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Fiocruz Rondônia. Laboratório de Análise e Visualização de Dados. Porto Velho, RO, Brasil. | en_US |
| Affilliation | Universidade Federal do Rio de Janeiro. Programa de Pós-Graduação em Clínica Médica. Rio de Janeiro, RJ, Brasil / Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Programa Acadêmico de Tuberculose. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Fundação Medicina Tropical Doutor Heitor Vieira Dourado. Manaus, AM, Brasil / Universidade Nilton Lins. Faculdade de Medicina. Manaus, AM, Brasil. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica em Micobacteriose. Rio de Janeiro, RJ, Brasil. | en_US |
| Affilliation | Vanderbilt University School of Medicine. Department of Medicine. Division of Infectious Diseases. Nashville, TN, USA. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil. | en_US |
| Affilliation | Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative. Salvador, BA, Brasil / Universidade Federal do Rio de Janeiro. Programa de Pós-Graduação em Clínica Médica. Rio de Janeiro, RJ, Brasil / Faculdade ZARNS. Instituto de Pesquisa Clínica e Translacional. Curso de Medicina. Salvador, BA, Brasil / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Pesquisa Clínica e Translacional. Salvador, BA, Brasil / Escola Bahiana de Medicina e Saúde Pública. Curso de Medicina. Salvador, BA, Brasil / Universidade Federal da Bahia. Faculdade de Medicina. Salvador, BA, Brasil / Universidade Federal do Rio de Janeiro. Faculdade de Medicina. Programa Acadêmico de Tuberculose. Rio de Janeiro, RJ, Brasil / Division of Infectious Diseases. Department of Medicine. Vanderbilt University School of Medicine. Nashville, TN, USA / Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Laboratório de Inflamação e Biomarcadores. Salvador, BA, Brasil. | en_US |
| Subject | Loss to follow-up | en_US |
| Subject | Machine learning | en_US |
| Subject | Score prediction | en_US |
| Subject | Tuberculosis | en_US |
| DeCS | Aprendizado de máquina | en_US |
| DeCS | Tuberculose | en_US |
| e-ISSN | 1471-2458 | |
| xmlui.metadata.dc.subject.ods | 04 Educação de qualidade | |
