Examining nonadherence in the treatment of tuberculosis: The patterns that lead to failure

Abstract

Aims Adherence has been shown to be a major predictor of tuberculosis treatment failure and relapse. The current adherence metrics can be improved to provide higher resolution of adherence patterns and identify patients in need of alternative treatment interventions. We investigated how adherence patterns affect treatment outcomes, when adherence is likely to decrease during treatment and which patients are at risk of being nonadherent. Methods Individual‐level data were pooled from 3 clinical trials (n = 3724) for treatment of drug susceptible tuberculosis where monthly or weekly adherence patterns were collected and adherence patterns were quantified to assess the impact of clustered missed doses vs. randomly missed doses on tuberculosis treatment outcomes. Significance was determined through univariate and multivariate cox regression models. Results Patients who miss doses in clusters have an increased hazard risk for unfavourable outcomes and missing as little as 4 treatment days in 1 month resulted in 61% higher risk of unfavourable outcomes compared to patients who missed no treatment days (P < .01). Patients older than 50 years, and patients who experienced an adverse event were associated with lower adherence. Conclusion Our results show that the pattern in which patients miss their drugs is important to their overall outcomes and missing treatment days in clusters rather than randomly increases the risk of poor outcomes. In the future more intensive and longitudinal adherence measurements will be valuable for clinical trials and regimen design and interpretation.

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?

“…Other patterns of adherence, such as the timing of missed doses, may be important. 34,35 Third, adherence was dichotomized (100% vs <100% of doses received) before being included in the models used to predict adherence and outcomes. It is plausible that utilizing a continuous functional form of adherence may improve the ability of the more sophisticated methods to eliminate bias due to treatment non-adherence.…”

“…Second, we calculated adherence using the overall percentage of prescribed doses received. Other patterns of adherence, such as the timing of missed doses, may be important 34,35 . Third, adherence was dichotomized (100% vs <100% of doses received) before being included in the models used to predict adherence and outcomes.…”

Medication adherence research comes of age

“…The critical role of suboptimal adherence in treatment failure and the development of antimicrobial resistance is perhaps nowhere more evident than in the management of tuberculosis. Fox et al 15 provide insight into the relationship between medication‐taking patterns, the predictors of suboptimal adherence and treatment outcomes in a cohort of subjects ( n = 3724) with tuberculosis. It was reported that missing only 4 clustered treatment days in 1 month increased the risk of treatment failure or relapse by 61%.…”

“…As above, the cross-sectional nature of the oxypurinol plasma concentration data will limit the ability to detect longitudinal adherence behaviour and the use of plasma concentrations will be confounded by the white coat effect, the tendency of patients to change their medication-taking behaviour prior to a clinic visit.The critical role of suboptimal adherence in treatment failure and the development of antimicrobial resistance is perhaps nowhere more evident than in the management of tuberculosis. Fox et al15 provide insight into the relationship between medication-taking patterns, the predictors of suboptimal adherence and treatment outcomes in a cohort of subjects (n = 3724) with tuberculosis. It was reported that missing only 4 clustered treatment days in 1 month increased the risk of treatment failure or relapse by 61%.…”

The appearance ofsugImixed loci in three individuals during treatment for MDR-TB, supports the involvement ofsugIinMycobacterium tuberculosisd-cycloserine resistancein vivo

“…Not all antibiotic escape mutations are successful and multiple resistance mutations associated with a single drug have been reported [Trauner 2017]. This accumulation of mutations may be an indicator for a "fragile" regimen [Fox 2022] where the concentration / number of active of drugs is insufficient to prevent the accumulation of resistance mutations. Furthermore the particular spectrum of mutations seen might indicate the key drug(s) where selective pressure is being most strongly applied to the mycobacteria by the chosen regimen.…”