Treatment interruption and associated factors among patients registered on drug-resistant tuberculosis treatment in Amhara regional state, Ethiopia: 2010-2017

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

Introduction

Drug 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 methods

A 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.

Results

Educational 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.

Conclusion

Educational 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.

A modeling-based proposal for safe and efficacious reintroduction of bedaquiline after dose interruption: A population pharmacokinetics study

Bedaquiline (BDQ) is recommended for treatment of multidrug-resistant tuberculosis (MDR-TB) for the majority of patients. Given its long terminal half-life and safety concerns, such as QTc-prolongation, re-introducing BDQ after multiple dose interruption is not intuitive and there are currently no existing guidelines. In this simulation-based study, we investigated different loading dose strategies for BDQ re-introduction, taking safety and efficacy into account. Multiple scenarios of time and length of interruption as well as BDQ re-introduction, including no loading dose, 1- and 2-week loading doses (200 mg and 400 mg once daily), were simulated from a previously published population pharmacokinetic (PK) model describing BDQ and its main metabolite M2 PK in patients with MDR-TB. The efficacy target was defined as 95.0% of the average BDQ concentration without dose interruption during standard treatment. Because M2 is the main driver for QTc-prolongation, the safety limit was set to be below the maximal average M2 metabolite concentration in a standard treatment. Simulations suggest that dose interruptions between treatment weeks 3 and 72 (interruption length: 1 to 6 weeks) require a 2-week loading dose of 200 mg once daily in the typical patient. If treatment was interrupted for longer than 8 weeks, a 2-week loading dose (400 mg once daily) was needed to reach the proposed efficacy target, slightly exceeding the safety limit. In conclusion, we here propose a strategy for BDQ re-introduction providing guidance to clinicians for safe and efficacious BDQ dosing.