Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA_1C) cut-off thresholds predictive of poor tuberculosis treatment outcomes and associated risk factors.

ABSTRACT:

Background

Rifampin-based therapy potentially exacerbates glycemic control among TB patients who are already at high risk of hyperglycemia. This impacts negatively to the optimal care of TB- diabetes mellitus co-affected patients. Classification and regression tree (CART), a machine-learning algorithm impervious to statistical assumptions is one of the ideal tools for clinical decision-making that can be used to identify hemoglobin A1C (HbA_1C) cut-off thresholds predictive of poor TB treatment outcomes in such populations.

Methods

340TB smear positive patients attending two peri-urban clinics were recruited and prospectively followed up for six months. Baseline HbA_1C and random blood glucose (RBG) levels were determined. CART was then used to identify c

SUBMITTER: Mburu JW 

PROVIDER: S-EPMC6830151 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature