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

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 cut-off thresholds and rank outcome predictors at end of therapy by determining Risk ratios (RR) and 95% confidence interval (CI) of each predictor threshold. Fractal geometry law explained effect of weight, while U-shaped curve explained effect of HbA(1C) on these clinical outcomes. RESULTS: Of the 340 patients enrolled: 84%were cured, 7% completed therapy and 9% had unfavorable outcomes out of which 4% (n = 32) had microbiologic failure. Using CART HbA(1C) identified thresholds were >2.95%, 2.95–4.55% and >4.55%, containing 8/11 (73%), 111/114 (97%) and 189/215 (88%) of patients who experienced favorable outcomes. RR for favorable outcome in patients with weight <53.25 Kg compared to >53.25 Kg was 0.61 (95% CI, 0.45–0.88) among patients with HbA1C >4.55%. Simulation of the CART model with 13 patients data failed therapy revealed that 8/11 (73%) of patients with HbA1C <2.95%, 111/114 (97%) with HbA(1C) between 2.95% and 4.55% and 189/215 (88%) of patients with HbA1c >4.55% experienced microbiologic failure. CONCLUSION: Using fractal geometry relationships to drug pharmacokinetics, low weight has profound influence on failure of anti-tuberculosis treatment among patients at risk for diabetes mellitus.