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

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...

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Veröffentlicht in:	BMC public health 2024-05, Vol.24 (1), p.1385-9, Article 1385
Hauptverfasser:	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
Format:	Artikel
Sprache:	eng
Schlagworte:	Adolescent Adult Alcohol use Algorithms Antitubercular agents Antitubercular Agents - therapeutic use Brazil - epidemiology Decision making Decision trees Diabetes Diagnosis Dosage and administration Drug resistance Drug resistance in microorganisms Drug therapy Drug use Ethics Female Group theory Health aspects Health services HIV Human immunodeficiency virus Humans Infections Information systems Loss to follow-up Lost to Follow-Up Machine Learning Male Medical personnel Methods Middle Aged Patient outcomes Prediction models Prevention Registries Regression analysis Resource allocation Retrospective Studies Risk Score prediction Self report Sex Sexually transmitted diseases STD Substance use Tobacco Tuberculosis Tuberculosis - drug therapy Tuberculosis - epidemiology Variables Young Adult
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Zusammenfassung:	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). We performed a retrospective study of all TB cases reported to SINAN between 2015 and 2022; excluding children (
ISSN:	1471-2458 1471-2458
DOI:	10.1186/s12889-024-18815-0