METHODS: This retrospective record review included all microbiologically confirmed pulmonary MDR/RR-TB patients treated with an all-oral LTR between August 2019 and February 2021 across nine PMDT centres in Pakistan. Sociodemographic and clinical data were retrieved from the Electronic Nominal Recording and Reporting System. Treatment outcomes, defined by WHO criteria, were analysed using SPSS and multivariate binary logistic regression to identify factors associated with unsuccessful outcomes. A p-value  5 drugs (OR:3.12, 95 %CI:1.36-11.64, p = 0.013) were significantly associated with death and treatment failure. Whereas, lung cavitation had statistically significant association with LTFU (OR:2.66, 95 %CI:1.10-7.32, p = 0.045).

METHODS: This study uses outpatient data from the HKL's Patient Management System (SPP) throughout 2019. The final data set has 246,943 appointment records with 13 attributes used for both descriptive and predictive analyses. The predictive analysis was carried out using seven machine learning algorithms, namely, logistic regression (LR), decision tree (DT), k-near neighbours (k-NN), Naïve Bayes (NB), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP).

RESULTS: The descriptive analysis showed that the no-show rate was 28%, and attributes such as the month of the appointment and the gender of the patient seem to influence the possibility of a patient not showing up. Evaluation of the predictive model found that the GB model had the highest accuracy of 78%, F1 score of 0.76 and area under the curve (AUC) value of 0.65.