Risk stratification algorithm for MDR-TB patients based on pooled individual data analysis.
Maria Garcia-Cremades (1,2,3), Jonathon R. Campbell (4,5), Natasha Strydom (2), Belén P. Solans (2,6), Payam Nahid (6,7), Dick Menzies (4,5), Rada M. Savic (2,6,7)
(1) Department of Pharmaceutics and Food Technology, School of Pharmacy, Complutense University of Madrid, Madrid, Spain, (2) Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA, (3) Institute of Industrial Pharmacy, Complutense University of Madrid, Madrid, Spain, (4) Department of Medicine, McGill University, Montreal, QC, Canada, (5) Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada, (6) UCSF Center for Tuberculosis, University of California, San Francisco, CA, USA, (7) Division of Pulmonary and Critical Care Medicine, San Francisco General Hospital, University of California, San Francisco, CA, USA
Introduction/Objectives: Treatment for multidrug-resistant tuberculosis (MDR-TB) is complex and long, and often include second-line drugs that have significant toxicities and modest efficacy. WHO guidelines recommend patient-centered care to ensure improved treatment outcomes. However, the great heterogeneity of disease and treatment composition makes it difficult to standardize individualized treatment and ensure that all patients receive optimal drug combinations and treatment duration. The aim of this analysis is to identify participant phenotypes who require shorter or longer treatment durations to optimize their chances of cure.
Methods: Individual participant data from 12,938 patients with pulmonary TB and confirmed rifampin resistance, included in 52 published observational and experimental studies, were pooled, and considered for analysis. Participants who were lost to follow up were excluded. Initial regimen, baseline clinical, demographic, and on-treatment characteristics were evaluated as predictors of unfavorable TB outcomes, which included treatment failure and death, using mixed-effects logistic regression models in R. Predictor exact regression coefficients were used to categorize low, medium, and high-risk phenotypes for unfavorable outcomes for each participant.
Results: The analysis dataset included 7,750 MDR-TB patients, including 5,869 patients with treatment success (76%), 628 patients with treatment failure (8%), and 1,253 patients who died during the study (16%). HIV infection, previous treatment with second line drugs, older age, low BMI, smoking, AFB positivity, extrapulmonary involvement, cavitary disease and resistance to pyrazinamide, fluoroquinolones and injectable drugs were all associated with TB unfavorable outcome (p<0.01). The use of linezolid and levofloxacin were significantly associated with lower odds of unfavorable outcome, while the use of kanamycin or capreomycin and PAS were associated with higher odds of unfavorable outcome (p<0.01). HIV seropositivity was the most significant baseline risk factor for unfavorable outcomes, with an adjusted OR of 2.3 (95% CI, 1.7–3), whereas six months culture conversion was the most significant predictor of treatment outcome (aOR, 0.06; 95%CI, 0.04-0.09), improving discrimination with an increase in ROC AUC from 0.73 to 0.83. The proportion of patients with treatment success (i.e., no unfavorable outcome) over the duration of treatment was significantly different between low, medium, and high-risk phenotypes (p<0.001). Additionally, cure rates were similar for patients with a low-risk phenotype regardless of treatment duration, but were much higher in participants with a high-risk phenotype who received a longer (77%) versus a shorter duration regimen (25%).
Conclusions: In this study, we used pooled individual data to develop a risk stratification algorithm that stratified MDR-TB patients into low-, medium- and high- risk groups. We identified patients at low risk for unfavorable TB outcomes, who achieved cure with shorter treatment durations, and patients at high risk who needed longer durations to maximize their chances of cure. Our results showed that stratified medicine approaches are feasible in MDR-TB. Stratification of patients with MDR-TB disease into low, medium, and high-risk phenotypes can help identify those who require longer or more intensive regimens to maximize cure rates.