JMIR Publications

ABSTRACT

Background:

Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognosis and preventing the transmission of this deadly disease. However, the absence of validated predictive models remains a significant challenge.

Objective:

This study compares conventional logistic regression model with machine learning (ML) models using demographic and clinical data to predict outcomes at 2 and 6 months of treatment for MDR/RR-TB. The goal is to advance model applications, refine control strategies, and boost MDR/RR-TB cure rates.

Methods:

This retrospective study encompassed an internal cohort of 744 patients with MDR/RR-TB, collected between January 2017 and June 2023, as well as an external cohort comprising 137 patients with MDR/RR-TB, gathered from March 2021 to June 2022. The data on culture conversion at 2 months and 6 months were collected, while the external cohort tracked culture conversion at the same time points. The internal cohort was assigned as the training set, while the external cohort was used as the validation set. Logistic regression and 7 ML models were developed to predict the culture conversion of MDR/RR-TB at 2 and 6 months of treatment, respectively. The model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.

Results:

In internal cohort, culture conversion rates for MDR/RR-TB were 81.9% (485/592) at 2 months and 87.1% (406/466) at 6 months. The odds ratio (OR) for treatment success was 8.55 (95% CI: 3.31-22.08) at 2 months and 20.33 (95% CI: 6.90-59.86) at 6 months post-conversion, with sensitivities of 86.5% and 92.2%, and specificities of 57.1% and 63.2%, respectively. The artificial neural network (ANN) model was the best model for culture conversion at both 2 and 6 months of treatment, with AUCs of 0.82 (95% CI: 0.77-0.86) and 0.90 (95% CI: 0.86-0.93), respectively. The accuracy, sensitivity, and specificity of model were 0.74, 0.74, 0.75 at 2 months of treatment, and 0.80, 0.79, 0.87 at 6 months of treatment, respectively.

Conclusions:

The ML models based on 2- and 6-month culture conversion can accurately predict treatment outcomes for MDR/RR-TB patients. ML models, particularly the ANN model, outperform logistic regression model in both senses of stability and generalizability, which offer rapid and effective tools for evaluating therapeutic efficacy at the early stages of MDR/RR-TB treatment.