TY - JOUR
T1 - Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression
T2 - A cross-sectional study
AU - De Queiroz Mello, Fernanda Carvalho
AU - Do Valle Bastos, Luiz Gustavo
AU - Machado Soares, Sérgio Luiz
AU - Rezende, Valéria M.C.
AU - Conde, Marcus Barreto
AU - Chaisson, Richard E.
AU - Kritski, Afrânio Lineu
AU - Ruffino-Netto, Antonio
AU - Werneck, Guilherme Loureiro
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2006/2/23
Y1 - 2006/2/23
N2 - 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%. Conclusion: 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.
AB - 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%. Conclusion: 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.
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U2 - 10.1186/1471-2458-6-43
DO - 10.1186/1471-2458-6-43
M3 - Article
C2 - 16504086
AN - SCOPUS:33644965074
SN - 1471-2458
VL - 6
JO - BMC public health
JF - BMC public health
M1 - 43
ER -