Development of CART model for prediction of tuberculosis treatment loss to follow up in the state of São Paulo, Brazil: A case-control study.

Autor: Hokino Yamaguti V; Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, State of São Paulo, Brazil. Electronic address: verena.yamaguti@usp.br., Alves D; Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, State of São Paulo, Brazil; Center for Research in Health Technologies and Services (CINTESIS), University of Porto, Porto, Portugal. Electronic address: quiron@fmrp.usp.br., Charters Lopes Rijo RP; Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, State of São Paulo, Brazil; School of Technology and Management, Polytechnic Institute of Leiria, Leiria, Portugal; Institute for Systems and Computers Engineering (INESCC), Coimbra, Portugal; Center for Research in Health Technologies and Services (CINTESIS), University of Porto, Porto, Portugal. Electronic address: rui.rijo@ipleiria.pt., Brandão Miyoshi NS; Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, State of São Paulo, Brazil. Electronic address: newton.sbm@gmail.com., Ruffino-Netto A; Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, State of São Paulo, Brazil. Electronic address: aruffino@fmrp.usp.br.
Jazyk: angličtina
Zdroj: International journal of medical informatics [Int J Med Inform] 2020 Sep; Vol. 141, pp. 104198. Date of Electronic Publication: 2020 Jun 15.
DOI: 10.1016/j.ijmedinf.2020.104198
Abstrakt: Background: Tuberculosis is the leading cause of infectious disease-related death, surpassing even the immunodeficiency virus. Treatment loss to follow up and irregular medication use contribute to persistent morbidity and mortality. This increases bacillus drug resistance and has a negative impact on disease control.
Objective: This study aims to develop a computational model that predicts the loss to follow up treatment in tuberculosis patients, thereby increasing treatment adherence and cure, reducing efforts regarding treatment relapses and decreasing disease spread.
Methods: This is a case-controlled study. Included in the data set were 103,846 tuberculosis cases from the state of São Paulo. They were collected using the TBWEB, an information system used as a tuberculosis treatment monitor, containing samples from 2006 to 2016. This set was later resampled into 6 segments with a 1-1 ratio. This ratio was used to avoid any bias during the model construction.
Results: The Classification and Regression Trees were used as the prediction model. Training and test sets accounted for 70% in the former and 30% in the latter of the tuberculosis cases. The model displayed an accuracy of 0.76, F-measure of 0.77, sensitivity of 0.80 and specificity of 0.71. The model emphasizes the relationship between several variables that had been identified in previous studies as related to patient cure or loss to follow up treatment in tuberculosis patients.
Conclusion: It was possible to construct a predictive model for loss to follow up treatment in tuberculosis patients using Classification and Regression Trees. Although the fact that the ideal predictive ability was not achieved, it seems reasonable to propose the use of Classification and Regression Trees models to predict likelihood of treatment follow up to support healthcare professionals in minimising the loss to follow up.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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