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 - Anley, Denekew Tenaw; Akalu, Temesgen Yihunie; Dessie, Anteneh Mengist; Anteneh, Rahel Mulatie; Zemene, Melkamu Aderajew; Bayih, Wubet Alebachew; Solomon, Yenealem; Gebeyehu, Natnael Atnafu; Kassie, Gizachew Ambaw; Mengstie, Misganaw Asmamaw; Abebe, Endeshaw Chekol; Seid, Mohammed Abdu; et al. (Frontiers in Digital Health, 2023)
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.