Treatment outcomes of multidrug-resistant tuberculosis in Hangzhou, China, 2011 to 2015

Treatment Outcomes and Associated Influencing Factors Among Patients with Rifampicin-Resistant Tuberculosis: A Multicenter, Retrospective, Cohort Study in China

Objective

Rifampin-resistant tuberculosis (RR-TB) remains a serious global public health concern. We assessed treatment outcomes and associated influencing factors among RR-TB patients in China.

Methods

This research enrolled 1339 patients who started RR-TB treatment between May 2018 and April 2020 in China retrospectively. Data were collected from the electronic medical records. Multivariable logistic regression analysis was used to identify the influencing factors related to unfavorable outcomes.

Results

Of the 1339 RR-TB patients, 78.8% (1055/1339) achieved treatment success (cured or treatment completed), 5.1% (68/1339) experienced treatment failure, 1.1% (15/1339) died during treatment, 10.1% (135/1339) were lost to follow-up, and 4.9% (66/1339) were not evaluated. About 67.7% (907/1339) of patients experienced at least one adverse event (AE). The most common AE was hypohepatia (507/1339, 37.9%), followed by hyperuricemia (429/1339, 32.0%), anemia (368/1339, 27.5%), electrolyte disturbance (318/1339, 23.7%), peripheral neuritis (245/1339, 18.3%), and gastrointestinal reactions (203/1339, 15.2%). Multivariate analysis showed that age ≥60 years [adjusted odds ratio (aOR): 1.96, 95% confidence interval (CI): 1.39-2.77], national minority (aOR: 2.36, 95% CI: 1.42-3.93), smoking (aOR: 1.50, 95% CI: 1.10-2.04), cardiopathy (aOR: 2.90, 95% CI: 1.33-6.31), tumors (aOR: 9.84, 95% CI: 2.27-42.67), immunocompromise (aOR: 2.17, 95% CI: 1.21-3.91), re-treated TB (aOR: 1.46, 95% CI: 1.08-1.97), and experienced gastrointestinal reactions (aOR: 2.27, 95% CI: 1.52-3.40) were associated with unfavorable outcomes. Body mass index (BMI) ≥18.5 kg/m2, regimens containing bedaquiline and experienced adverse events (AEs) such as hypohepatia, leukopenia, peripheral neuritis, and optic neuritis were associated with favorable outcomes.

Conclusion

High rates of treatment success were achieved for RR-TB patients at tertiary tuberculosis hospitals in China. Age ≥60 years, national minority, smoking status, comorbidities, re-treated TB, and experienced gastrointestinal reactions were independent prognostic factors for unfavorable treatment outcomes.

Global treatment outcomes of extensively drug-resistant tuberculosis in adults: A systematic review and meta-analysis

INTRODUCTION

Historically, extensively drug-resistant tuberculosis has been notoriously difficult to treat with devasting outcomes. As we are coming to the end of an era where the 2006 extensively drug-resistant tuberculosis definitions and old treatment regimens are being replaced, we aimed to estimate the proportion of extensively drug-resistant tuberculosis patients globally who achieved successful treatment outcomes.

METHODS

We conducted a systematic review of PubMed/MEDLINE, Scopus, Web of Science, and Embase from January 1, 2005, through April 3, 2023. Included studies reported WHO treatment outcomes, or adaptions hereof, for pre-extensively and/or extensively drug-resistant tuberculosis patients according to the 2006 WHO definition. Eligible studies included cohorts of at least 10 adults (aged>18 years) that were not pregnant. Using a random-effects model, we calculated pooled proportions of treatment outcomes and performed sensitivity and subgroup analyses. PROSPERO registration number: CRD42022340961.

RESULTS

Among 5056 studies reviewed, we identified 94 studies from 26 countries, involving 10,223 extensively drug-resistant tuberculosis patients. The pooled proportion of successful treatment outcomes was 44.2% (95%CI: 38.3-50.3). Sensitivity analyses consistently produced similar estimates. A slight improvement in treatment outcomes was observed after 2013. Furthermore, 25 studies reported outcomes for 3564 individuals with pre-extensively drug-resistant tuberculosis, of which 63.3% achieved successful treatment (95%CI: 43.1-72.5).

CONCLUSION

Globally, the success rate of extensively drug-resistant tuberculosis treatment is 44.2%, far below the WHO's target rate of 75%. These results may serve as a reference for future studies assessing extensively drug-resistant tuberculosis treatment outcomes under the 2021 definition treated with better treatment regimens available. Comprehensive surveillance data of extensively drug-resistant tuberculosis outcomes from the whole world are desirable to monitor treatment progress.

Development and validation of a prediction model for unsuccessful treatment outcomes in patients with multi-drug resistance tuberculosis

BACKGROUND

The World Health Organization has reported that the treatment success rate of multi-drug resistance tuberculosis is approximately 57% globally. Although new drugs such as bedaquiline and linezolid is likely improve the treatment outcome, there are other factors associated with unsuccessful treatment outcome. The factors associated with unsuccessful treatment outcomes have been widely examined, but only a few studies have developed prediction models. We aimed to develop and validate a simple clinical prediction model for unsuccessful treatment outcomes in patients with multi-drug resistance pulmonary tuberculosis (MDR-PTB).

METHODS

This retrospective cohort study was performed between January 2017 and December 2019 at a special hospital in Xi'an, China. A total of 446 patients with MDR-PTB were included. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to select prognostic factors for unsuccessful treatment outcomes. A nomogram was built based on four prognostic factors. Internal validation and leave-one-out cross-validation was used to assess the model.

RESULTS

Of the 446 patients with MDR-PTB, 32.9% (147/446) cases had unsuccessful treatment outcomes, and 67.1% had successful outcomes. After LASSO regression and multivariate logistic analyses, no health education, advanced age, being male, and larger extent lung involvement were identified as prognostic factors. These four prognostic factors were used to build the prediction nomograms. The area under the curve of the model was 0.757 (95%CI 0.711 to 0.804), and the concordance index (C-index) was 0.75. For the bootstrap sampling validation, the corrected C-index was 0.747. In the leave-one-out cross-validation, the C-index was 0.765. The slope of the calibration curve was 0.968, which was approximately 1.0. This indicated that the model was accurate in predicting unsuccessful treatment outcomes.

CONCLUSIONS

We built a predictive model and established a nomogram for unsuccessful treatment outcomes of multi-drug resistance pulmonary tuberculosis based on baseline characteristics. This predictive model showed good performance and could be used as a tool by clinicians to predict who among their patients will have an unsuccessful treatment outcome.

Development and Validation of a Nomogram for the Prediction of Unfavorable Treatment Outcome Among Multi-Drug Resistant Tuberculosis Patients in North West Ethiopia: An Application of Prediction Modelling

Background

Multidrug-resistant tuberculosis (MDR-TB) is a global problem and a health security threat, which makes “Ending the global TB epidemic in 2035” unachievable. Globally, the unfavourable treatment outcome remains unacceptably high. Therefore, this study aimed to develop a risk prediction model for unfavorable treatment outcomes in MDR-TB patients, which can be used by clinicians as a simple clinical tool in their decision-making.

Objective

The objective of this study was to develop and validate a risk prediction model for the prediction of unfavorable treatment outcomes among MDR-TB patients in North-West Ethiopia.

Methods

We used MDR-TB data collected from the University of Gondar and Debre Markos referral hospitals. A retrospective follow-up study was conducted and a total of 517 patients were included in the study. STATA version 16 statistical software and R version 4.0.5 were used for the analysis. Descriptive statistics were carried out. A multivariable model was fitted using all potent predictors selected by the lasso regression method. A simplified risk prediction model (nomogram) was developed based on the binomial logit-based model, and its performance was described by assessing its discriminatory power and calibration. Finally, decision curve analysis (DCA) was done to evaluate the clinical and public health impact of the developed model.

Results

The developed nomogram comprised six predictors: baseline anemia, major adverse event, comorbidity, age, marital status, and treatment supporter. The model has a discriminatory power of 0.753 (95% CI: 0.708, 0.798) and calibration test of (P-value = 0.695). It was internally validated by bootstrapping method, and it has a relatively corrected discrimination performance (AUC = 0.744, 95CI: 0.699, 0.788). The optimism coefficient was found to be 0.009. The decision curve analysis showed the net benefit of the model as threshold probabilities varied.

Conclusion

The developed nomogram can be used for individualized prediction of unfavorable treatment outcomes in MDR-TB patients for it has a satisfactory level of accuracy and good calibration. The model is clinically interpretable and was found to have added benefits in clinical practice.