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.

中文翻译：

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在巴西圣保罗州开发可预测结核病治疗损失的CART模型：病例对照研究。

背景

结核病是与传染病相关的死亡的主要原因，甚至超过了免疫缺陷病毒。后续治疗损失和不规则用药会导致持续的发病率和死亡率。这增加了芽孢杆菌的耐药性，对疾病控制有负面影响。

目的

这项研究旨在开发一种计算模型，该模型可以预测结核病患者后续治疗的损失，从而增加治疗依从性和治愈率，减少有关治疗复发的努力并减少疾病传播。

方法

这是一个病例对照研究。数据集中包括来自圣保罗州的103846例肺结核病例。使用TBWEB（一种用作结核病治疗监测仪的信息系统）收集了这些样本，其中包含2006年至2016年的样本。此样本集随后以1-1的比例重新采样为6个片段。该比率用于避免模型构建过程中的任何偏差。

结果

分类树和回归树用作预测模型。培训和测试集在前者中占70％，在后者中占30％。该模型显示的准确度为0.76，F-测度为0.77，灵敏度为0.80，特异性为0.71。该模型强调了先前研究中已确定的与患者治愈或结核病患者后续治疗损失相关的几个变量之间的关系。

结论

有可能使用分类树和回归树为结核病患者建立损失预测模型以进行后续治疗。尽管没有达到理想的预测能力这一事实，建议使用分类和回归树模型来预测后续治疗的可能性似乎是合理的，以支持医疗保健专业人员将后续损失降至最低。