Dataset Information

Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA1C) 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 (HbA1C) 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 HbA1C 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 HbA1C 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 HbA1C 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 HbA1C 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.

SUBMITTER: Mburu JW

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA<sub>1C</sub>) cut-off thresholds predictive of poor tuberculosis treatment outcomes and associated risk factors.

Mburu Josephine W JW Kingwara Leonard L Ester Magiri M Andrew Nyerere N

Journal of clinical tuberculosis and other mycobacterial diseases 20180313

<h4>Background</h4>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<sub>1C</sub>) cut-off thresholds predictive of ...[more]

PMID: 31720385

Similar Datasets

Project description:Praziquantel (PZQ) is an effective chemotherapy for schistosomiasis mansoni and a mainstay for its control and potential elimination. However, it does not prevent against reinfection, which can occur rapidly in areas with active transmission. A guide to ranking the risk factors for Schistosoma mansoni reinfection would greatly contribute to prioritizing resources and focusing prevention and control measures to prevent rapid reinfection. The objective of the current study was to explore the relationship among the socioeconomic, demographic, and epidemiological factors that can influence reinfection by S. mansoni one year after successful treatment with PZQ in school-aged children in Northeastern Minas Gerais state Brazil. Parasitological, socioeconomic, demographic, and water contact information were surveyed in 506 S. mansoni-infected individuals, aged 6 to 15 years, resident in these endemic areas. Eligible individuals were treated with PZQ until they were determined to be negative by the absence of S. mansoni eggs in the feces on two consecutive days of Kato-Katz fecal thick smear. These individuals were surveyed again 12 months from the date of successful treatment with PZQ. A classification and regression tree modeling (CART) was then used to explore the relationship between socioeconomic, demographic, and epidemiological variables and their reinfection status. The most important risk factor identified for S. mansoni reinfection was their "heavy" infection at baseline. Additional analyses, excluding heavy infection status, showed that lower socioeconomic status and a lower level of education of the household head were also most important risk factors for S. mansoni reinfection. Our results provide an important contribution toward the control and possible elimination of schistosomiasis by identifying three major risk factors that can be used for targeted treatment and monitoring of reinfection. We suggest that control measures that target heavily infected children in the most economically disadvantaged households would be most beneficial to maintain the success of mass chemotherapy campaigns.

Dataset Information

Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA1C) cut-off thresholds predictive of poor tuberculosis treatment outcomes and associated risk factors.

Publications

Use of classification and regression tree (CART), to identify hemoglobin A1C (HbA<sub>1C</sub>) cut-off thresholds predictive of poor tuberculosis treatment outcomes and associated risk factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets