Volume 97, Issue 6
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645



Multidrug-resistant tuberculosis (MDR-TB) has become a major public health problem. We tried to apply the classification tree model in building and evaluating a risk prediction model for MDR-TB. In this case–control study, 74 newly diagnosed MDR-TB patients served as the case group, and 95 patients without TB from the same medical institution served as the control group. The classification tree model was built using Chi-square Automatic Interaction Detectormethod and evaluated by income diagram, index map, risk statistic, and the area under receiver operating characteristic (ROC) curve. Four explanatory variables (history of exposure to TB patients, family with financial difficulties, history of other chronic respiratory diseases, and history of smoking) were included in the prediction model. The risk statistic of misclassification probability of the model was 0.160, and the area under ROC curve was 0.838 ( < 0.01). These suggest that the classification tree model works well for predicting MDR-TB. Classification tree model can not only predict the risk of MDR-TB effectively but also can reveal the interactions among variables.


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  1. Stellah GM, Isaack AL, Alexander WM, Riziki MK, Scott KH, , 2015. The influence of mining and human immunodeficiency virus infection among patients admitted for retreatment of tuberculosis in northern Tanzania. Am J Trop Med Hyg 93: 212215. [Google Scholar]
  2. World Health Organization, 2016. Global Tuberculosis Report. Available at: http://www.who.int/tb/publications/global_report/en/. Accessed February 2, 2017.
  3. Zhou ML, 2012. Analysis of the case detection and short-term effect of the Wuhan MDR-TB project. Chin J Antituberculosis 34: 299303. [Google Scholar]
  4. Zhang GL, 2013. Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS One 8: e80969. [Google Scholar]
  5. Chen W, Shu W, Wang M, Hou YC, Xia YY, Xu WG, Bai LQ, Nie SF, Cheng SM, Xu YH, , 2013. Pulmonary tuberculosis incidence and risk factors in rural areas of China: a Cohort Study. PLoS One 8: e58171. [Google Scholar]
  6. Gunther G, 2015. Multidrug-resistant tuberculosis in Europe, 2010–2011. Emerg Infect Dis 21: 409416. [Google Scholar]
  7. Zetola NM, Modongo C, Kip EC, Gross R, Bisson GP, Collman RG, , 2012. Alcohol use and abuse among patients with multidrug-resistant tuberculosis in Botswana. Int J Tuberc Lung Dis 16: 15291534. [Google Scholar]
  8. Liang L, 2012. Factors contributing to the high prevalence of multidrug-resistant tuberculosis: a study from China. Thorax 67: 632638. [Google Scholar]
  9. Jenkins HE, Gegia M, Furin J, Kalandadze I, Nanava U, Chakhaia T, Cohen T, , 2014. Geographical heterogeneity of multidrug-resistant tuberculosis in Georgia, January 2009 to June 2011. Euro Surveill 19: 2938. [Google Scholar]
  10. Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A, , 2011. Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes 4: 299. [Google Scholar]
  11. Zhang JF, Goode KM, Rigby A, Balk AHMM, Cleland JG, , 2013. Identifying patients at risk of death or hospitalization due to worsening heart failure using decision tree analysis: evidence from the Trans-European Network-Home-Care Management System (TEN-HMS) study. Int J Cardiol 163: 149156. [Google Scholar]
  12. Baltzer PAT, Dietzel M, Gröschel T, Kaiser WA, , 2012. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur J Radiol 81 (Suppl 1): S4S5. [Google Scholar]
  13. Gan XM, Xu YH, Liu L, Huang SQ, Xie DS, Wang XH, Liu JP, Nie SF, , 2011. Predicting the incidence risk of ischemic stroke in a hospital population of southern China: a classification tree analysis. J Neurol Sci 306: 108114. [Google Scholar]
  14. Miller B, Fridline M, Liu PY, Marino D, , 2014. Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults. Comput Math Methods Med 2014: 242717. [Google Scholar]
  15. World Health Organization, 2010. Multidrug and Extensively Drug-Resistant Tuberculosis(MXDR-TB) Global Report on Surveillance and Response. Available at: http://www.who.int/tb/ publications/global_report/en/. Accessed November 16, 2016.
  16. Thein TL, Leo YS, Lee VJ, Sun Y, Lye DC, , 2011. Validation of probability equation and decision tree in predicting subsequent dengue hemorrhagic fever in adult dengue inpatients in Singapore. Am J Trop Med Hyg 85: 942945. [Google Scholar]
  17. Horner SB, Fireman GD, Wang EW, , 2010. The relation of student behavior, peer status, race, and gender to decisions about school discipline using CHAID decision trees and regression modeling. J Sch Psychol 48: 135161. [Google Scholar]
  18. Lahmann NA, Tannen A, Dassen T, Kottner J, , 2011. Friction and shear highly associated with pressure ulcers of residents in long-term care–classification tree analysis (CHAID) of Braden items. J Eval Clin Pract 17: 168173. [Google Scholar]
  19. Becerra MC, Franke MF, Appleton SC, Joseph JK, Bayona J, Atwood SS, Mitnick CD, , 2013. Tuberculosis in children exposed at home to multidrug-resistant tuberculosis. Pediatr Infect Dis J 32: 115119. [Google Scholar]
  20. Seddon JA, Hesseling AC, Godfrey-Faussett P, Fielding K, Schaaf HS, , 2013. Risk factors for infection and disease in child contacts of multidrug-resistant tuberculosis: a cross-sectional study. BMC Infect Dis 13: 392. [Google Scholar]
  21. Furukawa NW, Haider MZ, Allen SJ, Carlson SL, Lindquist SW, , 2017. Resistance to first-line antituberculosis drugs in Washington state by region of birth and implications for latent tuberculosis treatment among foreign-born individuals. Am J Trop Med Hyg 96: 543549. [Google Scholar]
  22. Vashakidze L, 2009. Prevalence and risk factors for drug resistance among hospitalized tuberculosis patients in Georgia. Int J Tuberc Lung Dis 13: 11481153. [Google Scholar]
  23. Li XX, 2015. Comparing risk factors for primary multidrug-resistant tuberculosis and primary drug-susceptible tuberculosis in Jiangsu province, China: a Matched-Pairs Case-Control Study. Am J Trop Med Hyg 92: 280285. [Google Scholar]
  24. Wang K, 2014. Factors contributing to the high prevalence of multidrug-resistant tuberculosis among previously treated patients: a case-control study from China. Microb Drug Resist 20: 294300. [Google Scholar]
  25. Yang XJ, Yuan YL, Pang Y, Wang B, Bai YL, Wang YH, Yu BZ, Zhang ZY, Fan M, Zhao YL, , 2015. The burden of MDR/XDR tuberculosis in coastal plains population of China. PLoS One 10: e117361. [Google Scholar]
  26. Chen S, 2013. Risk factors for multidrug resistance among previously treated patients with tuberculosis in eastern China: a case-control study. Int J Infect Dis 17: e1116e1120. [Google Scholar]
  27. Zhao P, Li XJ, Zhang SF, Wang XS, Liu CY, , 2012. Social behaviour risk factors for drug resistant tuberculosis in mainland China: a meta-analysis. J Int Med Res 40: 436445. [Google Scholar]
  28. Liu CH, Li L, Chen Z, Wang Q, Hu YL, Zhu BL, Woo PCY, , 2011. Characteristics and treatment outcomes of patients with MDR and XDR tuberculosis in a TB referral hospital in Beijing: a 13-year experience. PLoS One 6: e19399. [Google Scholar]
  29. Bartu V, Kopecka E, Havelkova M, , 2010. Factors associated with multidrug-resistant tuberculosis: comparison of patients born inside and outside of the Czech Republic. J Int Med Res 38: 11561163. [Google Scholar]
  30. Rifat M, Milton AH, Hall J, Oldmeadow C, Islam MA, Husain A, Akhanda MW, Siddiquea BN, , 2014. Development of multidrug resistant tuberculosis in Bangladesh: a case-control study on risk factors. PLoS One 9: e105214. [Google Scholar]
  31. Skrahina A, 2013. Multidrug-resistant tuberculosis in Belarus: the size of the problem and associated risk factors. Bull World Health Organ 91: 3645. [Google Scholar]
  32. Liu Q, Zhu LM, Shao Y, Song HH, Li GL, Zhou Y, Shi JY, Zhong CQ, Chen C, Lu W, , 2013. Rates and risk factors for drug resistance tuberculosis in northeastern China. BMC Public Health 13: 1171. [Google Scholar]
  33. Ricks PM, Mavhunga F, Modi S, Indongo R, Zezai A, Lambert LA, DeLuca N, Krashin JS, Nakashima AK, Holtz TH, , 2012. Characteristics of multidrug-resistant tuberculosis in Namibia. BMC Infect Dis 12: 385. [Google Scholar]
  34. Daniel O, Osman E, , 2011. Prevalence and risk factors associated with drug resistant TB in south west, Nigeria. Asian Pac J Trop Med 4: 148151. [Google Scholar]
  35. Caminero JA, , 2010. Multidrug-resistant tuberculosis: epidemiology, risk factors and case finding. Int J Tuberc Lung Dis 14: 382390. [Google Scholar]
  36. Lomtadze N, Aspindzelashvili R, Janjgava M, Mirtskhulava V, Wright A, Blumberg HM, Salakaia A, , 2009. Prevalence and risk factors for multidrug-resistant tuberculosis in the Republic of Georgia: a population-based study. Int J Tuberc Lung Dis 13: 6873. [Google Scholar]

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  • Received : 12 Jan 2017
  • Accepted : 23 Aug 2017
  • Published online : 25 Sep 2017

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