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

Content Provider	Springer Nature : BioMed Central
Author	Rodrigues, Moreno M. S. Barreto-Duarte, Beatriz Vinhaes, Caian L. Araújo-Pereira, Mariana Fukutani, Eduardo R. Bergamaschi, Keityane Bone Kristki, Afrânio Cordeiro-Santos, Marcelo Rolla, Valeria C. Sterling, Timothy R. Queiroz, Artur T. L. Andrade, Bruno B.
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 and 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 splitted our data into training (~ 80% data) and test (~ 20%) sets, and then compared the model metrics using the test data set. Results Of the 243,726 cases included, 41,373 experienced LTFU whereas 202,353 were successfully treated. 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 systems 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 sensitivity. A user-friendly web calculator app was developed ( 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 utilizing schooling level, sex, age, prior TB status, and substance use (drug, alcohol, and/or tobacco). This is a potential tool to assist in decision-making strategies to guide resource allocation, DOT indications, and improve TB treatment adherence.
Related Links	https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-024-18815-0.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14712458
DOI	10.1186/s12889-024-18815-0
Journal	BMC Public Health
Issue Number	1
Volume Number	24
Language	English
Publisher	BioMed Central
Publisher Date	2024-05-23
Access Restriction	Open
Subject Keyword	Public Health Medicine Epidemiology Biostatistics Vaccine Environmental Health Tuberculosis Score prediction Loss to follow-up Machine learning Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	3.9/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Loss-to-follow-up and delay to treatment initiation in Pakistan’s national tuberculosis control programme

Characteristics and determinants of loss to follow-up among tuberculosis (TB) patients who smoke in an industrial state of Malaysia: a registry-based study of the years 2013-2017

Predictors of loss to follow-up among adult tuberculosis patients in Southern Ethiopia: a retrospective follow-up study

Initial loss to follow up of tuberculosis patients in South Africa: perspectives of program managers

Patient and health system factors associated with pretreatment loss to follow up among patients diagnosed with tuberculosis using Xpert® MTB/RIF testing in Uganda

Travel distance to rifampicin-resistant tuberculosis treatment and its impact on loss to follow-up: the importance of continued RR-TB treatment decentralization in South Africa

Predictors of loss to follow-up before HIV treatment initiation in Northwest Ethiopia: a case control study

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS

Identifying risk factors for loss to follow-up in adults living with HIV in a high-burden district in Ghana

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

Similar Documents

Loss-to-follow-up and delay to treatment initiation in Pakistan’s national tuberculosis control programme

Characteristics and determinants of loss to follow-up among tuberculosis (TB) patients who smoke in an industrial state of Malaysia: a registry-based study of the years 2013-2017

Predictors of loss to follow-up among adult tuberculosis patients in Southern Ethiopia: a retrospective follow-up study

Initial loss to follow up of tuberculosis patients in South Africa: perspectives of program managers

Patient and health system factors associated with pretreatment loss to follow up among patients diagnosed with tuberculosis using Xpert® MTB/RIF testing in Uganda

Travel distance to rifampicin-resistant tuberculosis treatment and its impact on loss to follow-up: the importance of continued RR-TB treatment decentralization in South Africa

Predictors of loss to follow-up before HIV treatment initiation in Northwest Ethiopia: a case control study

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS

Identifying risk factors for loss to follow-up in adults living with HIV in a high-burden district in Ghana

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