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

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 diff

SUBMITTER: Rodrigues MMS 

PROVIDER: S-EPMC11112756 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature