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Two Clinical Prediction Tools to Inform Rapid Tuberculosis Treatment Decision-making in Children.
Brooks, Meredith B; Hussain, Hamidah; Siddiqui, Sara; Ahmed, Junaid F; Jaswal, Maria; Amanullah, Farhana; Becerra, Mercedes; Malik, Amyn A.
Afiliação
  • Brooks MB; Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Hussain H; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA.
  • Siddiqui S; Interactive Research and Development Global, Singapore.
  • Ahmed JF; Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA.
  • Jaswal M; The Indus Hospital and Health Network, Korangi Crossing, Karachi, Pakistan.
  • Amanullah F; The Indus Hospital and Health Network, Korangi Crossing, Karachi, Pakistan.
  • Becerra M; Interactive Research and Development Global, Singapore.
  • Malik AA; The Indus Hospital and Health Network, Korangi Crossing, Karachi, Pakistan.
Open Forum Infect Dis ; 10(6): ofad245, 2023 Jun.
Article em En | MEDLINE | ID: mdl-37351457
ABSTRACT

Background:

In the absence of bacteriologic confirmation to diagnose tuberculosis (TB) in children, it is suggested that treatment should be initiated when sufficient clinical evidence of disease is available. However, it is unclear what clinical evidence is sufficient to make this decision. To identify children who would benefit from rapid initiation of TB treatment, we developed 2 clinical prediction tools.

Methods:

We conducted a secondary analysis of a prospective intensified TB patient-finding intervention conducted in Pakistan in 2014-2016. TB disease was determined through either bacteriologic confirmation or a clinical diagnosis. We derived 2 tools 1 uses classification and regression tree (CART) analysis to develop decision trees, while the second uses multivariable logistic regression to calculate a risk score.

Results:

Of the 5162 and 5074 children included in the CART and prediction score, respectively, 1417 (27.5%) and 1365 (26.9%) were eligible for TB treatment. CART identified abnormal chest radiographs and family history of TB as the most important predictors (area under the receiver operating characteristic curve [AUC], 0.949). The final prediction score model included age group (0-4, 5-9, 10-14), weight <5th percentile, cough, fever, weight loss, chest radiograph suggestive of TB disease, and family history of TB; the identified best cutoff score was 9 (AUC, 0.985%).

Conclusions:

Use of clinical evidence was sufficient to accurately identify children who would benefit from treatment initiation. Our tools performed well compared with existing algorithms, though these results need to be externally validated before operationalization.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Open Forum Infect Dis Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Open Forum Infect Dis Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos