Factors determining successful treatment outcome among notified tuberculosis patients in Belagavi district of North Karnataka, India

Kumar, Anil; Harakuni, Sheetal; Paranjape, Ramesh; Korabu, Anil S.; Prasad, Jang Bahadur

doi:10.1016/j.cegh.2024.101505

Clinical Epidemiology and Global Health

2024

DOI: 10.1016/j.cegh.2024.101505

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Factors determining successful treatment outcome among notified tuberculosis patients in Belagavi district of North Karnataka, India

Anil Kumar,

Sheetal Harakuni,

Ramesh Paranjape

et al.

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Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm

Hosu,

Faye,

Apalata

2024

Pathogens

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Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed with DR-TB and received treatment, was conducted. Student’s t-test was performed to assess differences between two means and ANOVA between groups. The Chi-square test with or without trend or Fischer’s exact test was used to test the degree of association of categorical variables. Logistic regression was used to determine predictors of DR-TB treatment outcomes. A decision tree classifier, which is a supervised machine learning algorithm, was also used. Python version 3.8. and R version 4.1.1 software were used for data analysis. A p-value of 0.05 with a 95% confidence interval (CI) was used to determine statistical significance. A total of 456 DR-TB patients were included in the study, with more male patients (n = 256, 56.1%) than female patients (n = 200, 43.9%). The overall treatment success rate was 61.4%. There was a significant decrease in the % of patients cured during the COVID-19 pandemic compared to the pre-pandemic period. Our findings showed that machine learning can be used to predict TB patients’ treatment outcomes.

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Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm

Hosu,

Faye,

Apalata

2024

Pathogens

View full text Add to dashboard Cite

show abstract

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Factors determining successful treatment outcome among notified tuberculosis patients in Belagavi district of North Karnataka, India

Cited by 1 publication

References 10 publications

Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm

Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm

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