Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study.
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.
Mello
Fernanda Carvalho de Queiroz
FC
Tuberculosis Research Unit, Clementino Fraga Filho Hospital, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. fcqmello@hucff.ufrj.br
Bastos
Luiz Gustavo do Valle
LG
Soares
Sérgio Luiz Machado
SL
Rezende
Valéria M C
VM
Conde
Marcus Barreto
MB
Chaisson
Richard E
RE
Kritski
Afrânio Lineu
AL
Ruffino-Netto
Antonio
A
Werneck
Guilherme Loureiro
GL
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*