Development of a risk score for prediction of poor treatment outcomes among patients with multidrug-resistant tuberculosis

Risk factors for multidrug-resistant tuberculosis in the Central African Republic: A case-control study

BACKGROUND

The emergence and spread of multidrug-resistant tuberculosis (MDR-TB) presents a challenge to the "End TB by 2035" strategy. This study aimed to identify the risk factors associated with MDR-TB in patients admitted to the pneumo-physiology clinic of the National University Hospital of Bangui in Central African Republic.

METHODS

This was a "retrospective" chart review study. Cases were represented by patients more than 18 years of age treated for MDR-TB and controls were patients with "at least rifampicin-susceptible" TB treated "with first-line anti-TB regimen" and who at the end of treatment were declared cured. The status of "cured" was exclusively applicable to non-MDR TB. Risk factors associated with MDR-TB were identified by multivariate analysis.

RESULTS

We included 70 cases and 140 controls. The median age was 35 years, IQR (22;46 years). The main factors associated with the occurrence of MDR-TB in multivariate analysis were male gender (0 R = 3.02 [1.89-3.99], p = 0.001), residence in a peri-urban/urban area (0 R = 3.06 [2.21-4.01], p = 0.002), history of previous TB treatment (0 R= 3.99 [2.77-4.25], p < 0.001) and the presence of multidrug-resistant TB in the family (0 R=1.86 [1.27-2.45], p = 0.021).

CONCLUSION

The emergence of MDR-TB can be reduced by implementing appropriate strategies, such as preventive therapy in contacts of MDR-TB patients and detecting and appropriately treating MDR-TB patients to prevent further spread of infection.

Poor treatment outcome and associated risk factors among patients with isoniazid mono-resistant tuberculosis: A systematic review and meta-analysis

BACKGROUND

To date, isoniazid mono-resistant tuberculosis (TB) is becoming an emerging global public health problem. It is associated with poor treatment outcome. Different studies have assessed the treatment outcome of isoniazid mono-resistant TB cases, however, the findings are inconsistent and there is limited global comprehensive report. Thus, this study aimed to assess the poor treatment outcome and its associated risk factors among patients with isoniazid mono-resistant TB.

METHODS

Studies that reported the treatment outcomes and associated factors among isoniazid mono-resistant TB were searched from electronic databases and other sources. We used Joana Briggs Institute critical appraisal tool to assess the study's quality. We assessed publication bias through visual inspection of the funnel plot and confirmed by Egger's regression test. We used STATA version 17 for statistical analysis.

RESULTS

Among 347 studies identified from the whole search, data were extracted from 25 studies reported from 47 countries. The pooled successful and poor treatment outcomes were 78% (95%CI; 74%-83%) and 22% (95%CI; 17%-26%), respectively. Specifically, complete, cure, treatment failure, mortality, loss to follow-up and relapse rates were 34%(95%CI; 17%-52%), 62% (95%CI; 50%-73%), 5% (95%CI; 3%-7%), 6% (95%CI; 4%-8%), 12% (95%CI; 8%-17%), and 1.7% (95%CI; 0.4%-3.1%), respectively. Higher prevalence of pooled poor treatment outcome was found in the South East Asian Region (estimate; 40%, 95%C; 34%-45%), and African Region (estimate; 33%, 95%CI; 24%-42%). Previous TB treatment (OR; 1.74, 95%CI; 1.15-2.33), having cancer (OR; 3.53, 95%CI; 1.43-5.62), and being initially smear positive (OR; 1.26, 95%CI; 1.08-1.43) were associated with poor treatment outcome. While those patients who took rifampicin in the continuation phase (OR; 0.22, 95%CI; 0.04-0.41), had extrapulmonary TB (OR; 0.70, 95%CI; 0.55-0.85), and took second-line injectable drugs (OR; 0.54, 95%CI; 0.33-0.75) had reduced risk of poor treatment outcome.

CONCLUSION

Isoniazid mono-resistant TB patients had high poor treatment outcome. Thus, determination of isoniazid resistance pattern for all bacteriologically confirmed TB cases is critical for successful treatment outcome. PROSPERO registration number: CRD42022372367.

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.

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.

Tuberculosis Treatment Monitoring and Outcome Measures: New Interest and New Strategies

Despite the advent of new diagnostics, drugs and regimens, tuberculosis (TB) remains a global public health threat. A significant challenge for TB control efforts has been the monitoring of TB therapy and determination of TB treatment success. Current recommendations for TB treatment monitoring rely on sputum and culture conversion, which have low sensitivity and long turnaround times, present biohazard risk, and are prone to contamination, undermining their usefulness as clinical treatment monitoring tools and for drug development. We review the pipeline of molecular technologies and assays that serve as suitable substitutes for current culture-based readouts for treatment response and outcome with the potential to change TB therapy monitoring and accelerate drug development.

A Score to Predict the Risk of Major Adverse Drug Reactions Among Multi-Drug Resistant Tuberculosis Patients in Southern Ethiopia, 2014–2019

Background

Adverse events (AE) contribute to poor drug adherence and withdrawal, which contribute to a low treatment success rate. AE are commonly reported among multi-drug resistance tuberculosis (MDR-TB) patients in Ethiopia. However, predictors of AE among MDR-TB patients were limited in Ethiopia. Thus, the current study aimed to develop and validate a score to predict the risks of major AE among MDR-TB patients in Southern Ethiopia.

Methods

A retrospective follow-up study design was employed among MDR-TB patients from 2014–2019 in southern Ethiopia at selected hospitals. A least absolute shrinkage and selection operator algorithm was used to select the most potent predictors of the outcome. The adverse event risk score was built based on the multivariable logistic regression analysis. Discriminatory power and calibration were checked to evaluate the performance of the model. Bootstrapping method with 100 repetitions was used for internal model validation.

Results

History of baseline khat use, long-term drug regimen use, and having coexisting disorders (co-morbidity) were predictors of AEs. The score has a satisfactory discriminatory power (AUC = 0.77, 95% CI: 0.68, 0.82) and a modest calibration (Prob > chi² = 0.2043). It was found to have the same c-statistics after validation by bootstrapping method of 100 repetitions with replacement.

Conclusion

A history of baseline khat use, co-morbidity, and long-term drug regimen use are helpful to predict individual risk of major adverse events in MDR-TB patients with a satisfactory degree of accuracy (AUC = 0.77).

Treatment outcomes of drug resistant tuberculosis patients with multiple poor prognostic indicators in Uganda: A countrywide 5-year retrospective study

BACKGROUND

Comorbid conditions and adverse drug events are associated with poor treatment outcomes among patients with drug resistant tuberculosis (DR - TB). This study aimed at determining the treatment outcomes of DR - TB patients with poor prognostic indicators in Uganda.

METHODS

We reviewed treatment records of DR - TB patients from 16 treatment sites in Uganda. Eligible patients had confirmed DR - TB, a treatment outcome in 2014-2019 and at least one of 15 pre-defined poor prognostic indicators at treatment initiation or during therapy. The pre-defined poor prognostic indicators were HIV co-infection, diabetes, heart failure, malignancy, psychiatric illness/symptoms, severe anaemia, alcohol use, cigarette smoking, low body mass index, elevated creatinine, hepatic dysfunction, hearing loss, resistance to fluoroquinolones and/or second-line aminoglycosides, previous exposure to second-line drugs (SLDs), and pregnancy. Tuberculosis treatment outcomes were treatment success, mortality, loss to follow up, and treatment failure as defined by the World Health Organisation. We used logistic and cox proportional hazards regression analysis to determine predictors of treatment success and mortality, respectively.

RESULTS

Of 1122 DR - TB patients, 709 (63.2%) were male and the median (interquartile range, IQR) age was 36.0 (28.0-45.0) years. A total of 925 (82.4%) had ≥2 poor prognostic indicators. Treatment success and mortality occurred among 806 (71.8%) and 207 (18.4%) patients whereas treatment loss-to-follow-up and failure were observed among 96 (8.6%) and 13 (1.2%) patients, respectively. Mild (OR: 0.57, 95% CI 0.39-0.84, p = 0.004), moderate (OR: 0.18, 95% CI 0.12-0.26, p < 0.001) and severe anaemia (OR: 0.09, 95% CI 0.05-0.17, p < 0.001) and previous exposure to SLDs (OR: 0.19, 95% CI 0.08-0.48, p < 0.001) predicted lower odds of treatment success while the number of poor prognostic indicators (HR: 1.62, 95% CI 1.30-2.01, p < 0.001), for every additional poor prognostic indicator) predicted mortality.

CONCLUSION

Among DR - TB patients with multiple poor prognostic indicators, mortality was the most frequent unsuccessful outcomes. Every additional poor prognostic indicator increased the risk of mortality while anaemia and previous exposure to SLDs were associated with lower odds of treatment success. The management of anaemia among DR - TB patients needs to be evaluated by prospective studies. DR - TB programs should also optimise DR - TB treatment the first time it is initiated.

Factors associated with unfavorable treatment outcomes in patients with rifampicin-resistant tuberculosis in Colombia 2013-2015: A retrospective cohort study

Background

Multidrug- and rifampicin (RMP)-resistant tuberculosis (MDR/RR-TB) requires prolonged and expensive treatment, which is difficult to sustain in the Colombian health system. This requires the joint action of different providers to provide timely health services to people with TB. Identifying factors associated with unfavorable treatment outcomes in patients with MDR/RR-TB who received drug therapy between 2013 and 2015 in Colombia can help guide the strengthening of the national TB control program.

Method

A retrospective cohort study was conducted with all patients who received treatment for MDR/RR-TB between January 2013 and December 2015 in Colombia who were registered and followed up by the national TB control program. A multivariate logistic regression model was used to estimate the associations between the exposure variables with the response variable (treatment outcome).

Results

A total of 511 patients with MDR/RR-TB were registered and followed up by the national TB control program in Colombia, of whom 16 (3.1%) had extensive drug resistance, 364 (71.2%) had multidrug resistance, and 131 (25.6%) had RMP monoresistance. The mean age was 39.9 years (95% confidence interval (CI): 38.5–41.3), most patients were male 285 (64.6%), and 299 (67.8%) were eligible for subsidized health services. The rate of unfavorable treatment outcomes in the RR-TB cohort was 50.1%, with rates of 85.7% for patients with extensive drug resistance, 47.6% for patients with multidrug resistance, and 52.6% for patients with RMP monoresistance. The 511 MDR/RR-TB patients were included in bivariate and multivariate analyses, patients age ≥ 60 years (crude odds ratio (ORc) = 2.4, 95% CI 1.1–5.8; adjusted odds ratio (ORa) = 2.7, 95% CI 1.1–6.8) and subsidized health regime affiliation (ORc = 3.6, 95% CI 2.3–5.6; ORa = 3.4, 95% CI 2.0–6.0) were associated with unfavorable treatment outcomes.

Conclusion

More than 50% of the patients with MDR/RR-TB in Colombia experienced unfavorable treatment outcomes. The patients who were eligible for subsidized care were more likely to experience unfavorable treatment outcomes. Those who were older than 60 years were also more likely to experience unfavorable treatment outcomes.

Predicting treatment failure risk in a Chinese Drug-Resistant Tuberculosis with surgical therapy: Development and assessment of a new predictive nomogram

BACKGROUND

The aim of this study was to develop and internally validate a treatment failure risk nomogram in a Chinese population of patients with Drug-Resistant Tuberculosis with surgical therapy.

METHODS

We developed a prediction model based on a dataset of 132 drug-resistant tuberculosis (DR-TB) patients. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the treatment failure risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.

FINDINGS

Predictors contained in the prediction nomogram included Lesion, Treatment history, Recurrent chest infection (RCI) and Multidrug-resistant tuberculosis (MDR-TB) or Extensively drug-resistant tuberculosis (XDR-TB). The model displayed good discrimination with a C-index of 0.905 and good calibration. A high C-index value of 0.876 could still be reached in the interval validation. Decision curve analysis showed that the nomogram was clinically useful when an intervention was decided at the treatment failure possibility threshold of 1%.

INTERPRETATION

This study developed a novel nomogram with relatively good accuracy to help clinicians access the risk of treatment failure in MDR/XDR-TB patients when starting surgery. With an estimate of individual risk, clinicians and patients can make more suitable decisions regarding surgery. This nomogram requires external validation, and further research is needed to determine whether the nomogram is appropriate for predicting surgery risk in MDR/XDR-TB patients.