ArticleFrontiers in digital health2023
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.
Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Illicit substance use and its impact on tuberculosis care engagement: A systematic review and Meta-analysis of prevalence.Journal of clinical tuberculosis and other mycobacterial diseases · 2026Review
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
- S-nitrosylation-triggered secretion of mycobacterial PknG leads to phosphorylation of SODD to prevent apoptosis of infected macrophages.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integrating narrative and bibliometric approaches to examine factors and impacts of tuberculosis treatment non-compliance.Multidisciplinary respiratory medicine · 2025Article
- Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.Scientific reports · 2025Article
- What are the Real-Life Dilemmas and Facilitators of Medication Adherence of Patients with Drug-Resistant Tuberculosis: A Qualitative Exploration of Patient Perspectives.Infection and drug resistance · 2025Article
- Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province.BMC infectious diseases · 2024Article
- Genetic Characterization and Population Structure of Drug-ResistantAntibiotics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.