Machine learning algorithms using national registry data to predict loss to follow- up during tuberculosis treatment

Author:

Rodrigues Moreno M. S.1,Barreto-Duarte Beatriz2,Vinhaes Caian L.3,Araújo-Pereira Mariana4,Fukutani Eduardo R.5,Bergamaschi Keityane Bone1,Kristki Afrânio6,Cordeiro-Santos Marcelo7,Rolla Valeria C.8,Sterling Timothy R.9,Queiroz Artur T. L.1,Andrade Bruno B.5

Affiliation:

1. Fundação Oswaldo Cruz

2. Universidade Salvador

3. Escola Bahiana de Medicina e Saúde Pública

4. Centro Universitário Faculdade de Tecnologia e Ciências

5. Instituto Gonçalo Moniz, Fundação Oswaldo Cruz

6. Universidade Federal do Rio de Janeiro

7. Fundação Medicina Tropical Doutor Heitor Vieira Dourado

8. Instituto Nacional de Infectologia Evandro Chagas

9. Vanderbilt University School of Medicine

Abstract

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.

Publisher

Research Square Platform LLC

Reference28 articles.

1. WHO. Global tuberculosis report 2023 [Internet]. [cited 2023 Nov 28]. Available from: https://www.who.int/publications-detail-redirect/9789240083851.

2. Rapid communication. : key changes to the treatment of drug-resistant tuberculosis [Internet]. [cited 2023 Dec 4]. Available from: https://www.who.int/publications-detail-redirect/WHO-UCN-TB-2022-2.

3. WHO consolidated guidelines on tuberculosis. : module 4: treatment: drug-susceptible tuberculosis treatment [Internet]. [cited 2023 Dec 4]. Available from: https://www.who.int/publications-detail-redirect/9789240048126.

4. The World Bank Group. The World Bank In Brazil [Internet]. World Bank. [cited 2023 Dec 4]. Available from: https://www.worldbank.org/en/country/brazil/overview.

5. Campos T. Manual SINAN – Normas e Rotinas 2a edição – Portal da Vigilância em Saúde [Internet]. 2018 [cited 2023 Nov 28]. Available from: http://vigilancia.saude.mg.gov.br/index.php/download/manual-sinan-normas-e-rotinas-2a-edicao/.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3