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Development of a clinical scoring system for the diagnosis of smear-negative pulmonary tuberculosis

  • Alonso Soto
  • , Lely Solari
  • , Juan Agapito
  • , Carlos Acuna-Villaorduna
  • , Marie Laurence Lambert
  • , Eduardo Gotuzzo
  • , Patrick Van der Stuyft
  • Hospital Nacional Hipolito Unanue
  • Universidad Peruana Cayetano Heredia
  • Institute of Tropical Medicine
  • Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogota

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

This study developed a clinical score based on clinical and radiographic data for the diagnosis of smear-negative pulmonary tuberculosis (SNPT). SNPT was defined as a positive culture in Ogawa in a patient with two negative sputum smears. Data from patients admitted to the emergency ward with respiratory symptoms and negative acid-fast bacilli (AFB) smears was analyzed by means of logistic regression to develop the predictive score.Two hundred and sixty two patients were included. Twenty patients had SNPT. The variables included in the final model were hemoptysis, weight loss, age > 45 years old, productive cough, upper-lobe infiltrate, and military infiltrate. With those, a score was constructed. The score values ranged from -2 to 6. The area under the curve for the ROC curve was 0.83 (95% CI 0.74-0.90). A score of value 0 or less was associated with a sensitivity of 93% and a score of more than 4 points was associated with a specificity of 92% for SNPT. Fifty-two point twenty-nine percent of patients had scores of less than one or more than four what provide strong evidence against and in favor; respectively, for the diagnosis of SNPT. The score developed is a cheap and useful clinical tool for the diagnosis of SNPT and can be used to help therapeutic decisions, patients with suspicion of having SNPT.

Original languageEnglish
Pages (from-to)128-132
Number of pages5
JournalBrazilian Journal of Infectious Diseases
Volume12
Issue number2
DOIs
StatePublished - Apr 2008
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Pulmonary
  • Regression analysis
  • Scoring methods
  • Tuberculosis

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