Prognostication of treatment non-compliance among patients with multidrug-resistant tuberculosis in the course of their follow-up: a logistic regression–based machine learning algorithm

Abstract

IntroductionDrug compliance is the act of taking medication on schedule or taking medication as prescribed and obeying other medical instructions. It is the most crucial aspect in the treatment of chronic diseases particularly for patients with multidrug-resistant tuberculosis (MDR-TB). Drug non-compliance is the main reason for causing drug resistance and poor treatment outcomes. Hence, developing a risk prediction model by using early obtainable prognostic determinants of non-compliance is vital in averting the existing, unacceptably high level of poor treatment outcomes and reducing drug resistance among MDR-TB patients.Materials and methodsA retrospective follow-up study was conducted on a total of 517 MDR-TB patients in Northwest Ethiopia. A logistic regression–based machine learning algorithm was used to develop a risk score for the prediction of treatment non-compliance among MDR-TB patients in selected referral hospitals of Northwest Ethiopia. The data were incorporated in EpiData version 3.1 and exported to STATA version 16 and R version 4.0.5 software for analysis. A simplified risk prediction model was developed, and its performance was reported. It was also internally validated by using a bootstrapping method.ResultsEducational status, registration group (previously treated/new), treatment support, model of care, and khat use were significant prognostic features of treatment non-compliance. The model has a discriminatory power of area under curve (AUC) = 0.79 with a 95% CI of 0.74–0.85 and a calibration test of p-value = 0.5. It was internally validated by using a bootstrapping method, and it has a relatively corrected discriminatory performance of AUC = 0.78 with a 95% CI of 0.73–0.86 and an optimism coefficient of 0.013.ConclusionEducational status, registration group, treatment supporter, model of care, and khat use are important features that can predict treatment non-compliance of MDR-TB patients. The risk score developed has a satisfactory level of accuracy and good calibration. In addition, it is clinically interpretable and easy to use in clinical practice, because its features are easily ascertainable even at the initial stage of patient enrolment. Hence, it becomes important to reduce poor treatment outcomes and drug resistance.

Genetic Characterization and Population Structure of Drug-Resistant Mycobacterium tuberculosis Isolated from Brazilian Patients Using Whole-Genome Sequencing

“…We observed the emergence of FQ-resistance-conferring mutations among one third of the patients that were followed-up with during treatment. These bacterial populations had likely been selected due non-compliance of drug treatment [ 44 , 45 ]. In addition, the emergence of phenotypic resistance to AMK in patient P39 and phenotypic resistance to KAN and CAP in patient P64 was observed, but without the detection of known mutations in rrs and eis .…”

Genetic Characterization and Population Structure of Drug Resistant <em>Mycobacterium tuberculosis </em>Isolated from Brazilian Patients Using Whole-Genome Sequencing

“…We observed emergence of FQ resistance conferring mutations among one third of the patients that were followed-up during treatment. These bacterial populations had likely been selected due non-compliance of drug treatment [39,40]. In addition, emergence of phenotypic resistance to AMK in patient P39, and phenotypic resistance to KAN and CAP in patient P64 was observed, but without detection of known mutations in rrs and eis.…”