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Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database

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Multiple imputation analysis effectively predicted tuberculosis treatment outcomes and identified risk factors for death, including TB clinical form and absent isoniazid or rifampicin in treatment regimens.

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Area of Science:

  • Medical research
  • Public health
  • Epidemiology

Background:

  • Understanding early predictors of treatment outcomes is crucial for efficient tuberculosis (TB) management and resource allocation.
  • Predicting TB treatment success requires addressing challenges like incomplete health records and data imbalance.

Purpose of the Study:

  • To predict TB treatment outcomes using real-world health data.
  • To identify risk factors associated with mortality during TB treatment.
  • To evaluate the effectiveness of multiple imputation analysis (MIA) in handling missing data for outcome prediction.

Main Methods:

  • Employed upweighting and multiple imputation analysis (MIA) to manage missing data and response imbalance.
  • Utilized logistic regression (LOGIT), random forest, and stochastic gradient boosting for outcome prediction.
  • Conducted interpretation of LOGIT models, comparing MIA with complete case analysis (CCA).

Main Results:

  • Multiple imputation analysis (MIA) proved effective for handling missing data in TB patient records.
  • MIA-derived LOGIT models identified more statistically significant covariates associated with TB treatment outcomes than CCA.
  • Factors increasing odds of death included TB clinical form (pulmonary and extrapulmonary), retreatment after abandonment, and absence of isoniazid or rifampicin in the treatment regimen.

Conclusions:

  • MIA is a suitable method for addressing missing data in TB treatment outcome prediction.
  • Identified key risk factors for mortality during TB treatment, informing clinical practice.
  • Recommended the use of interpretable LOGIT models for predicting TB treatment outcomes.