title: Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study.
abstract: BACKGROUND
  Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources.
  
  
  METHODS
  The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples.
  
  
  RESULTS
  It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%.
  
  
  CONCLUSIONS
  The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.
authors.affiliation: Tuberculosis Research Unit, Clementino Fraga Filho Hospital, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. fcqmello@hucff.ufrj.br
authors.forename: Fernanda Carvalho de Queiroz
authors.initials: FC
authors.lastname: Mello
authors.forename: Luiz Gustavo do Valle
authors.initials: LG
authors.lastname: Bastos
authors.forename: Sérgio Luiz Machado
authors.initials: SL
authors.lastname: Soares
authors.forename: Valéria M C
authors.initials: VM
authors.lastname: Rezende
authors.forename: Marcus Barreto
authors.initials: MB
authors.lastname: Conde
authors.forename: Richard E
authors.initials: RE
authors.lastname: Chaisson
authors.forename: Afrânio Lineu
authors.initials: AL
authors.lastname: Kritski
authors.forename: Antonio
authors.initials: A
authors.lastname: Ruffino-Netto
authors.forename: Guilherme Loureiro
authors.initials: GL
authors.lastname: Werneck
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001938:Brazil; D001999:Bronchoscopy; D003430:Cross-Sectional Studies; D003663:Decision Trees; D003951:Diagnostic Errors; D005260:Female; D006785:Hospitals, University; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009169:Mycobacterium tuberculosis / Q000302:isolation & purification; D011237:Predictive Value of Tests; D012372:ROC Curve; D011859:Radiography; D018570:Risk Assessment; D013183:Sputum / Q000382:microbiology; D014397:Tuberculosis, Pulmonary / Q000000981:diagnostic imaging* / Q000473:pathology*