Adverse effects and duration of treatment of TB in Canterbury, New Zealand

Phillipson, J. David; Kuruppu, N.; Chikura, T.; McLachlan, Caleb; McNeill, Laurie K.; Metcalf, Sarah; Pithie, Alan; Rhodes, Benjamin; Dalton, Simon C; Maze, Michael J.

doi:10.5588/ijtld.21.0345

Cited by 4 publications

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“…The present study revealed that the prevalence of ADRs was 29% among the study population, similar to various worldwide studies ranging from 8% to 85%. [3][4][5][6]14,[17][18][19][20] Several studies reported more ADR prevalence in drug-resistant TB patients, similar to the present study, where 50% of DRTB patients experienced ADRs. 19,[21][22][23] The variance in ADR prevalence between these studies could be due to several data collection variables including the ADR reporting mechanism, patient-reported (subjective) or clinician-detected (objective), and variations in the use of particular anti-tubercular drugs such as dosage and ancillary medications used for ADR management.…”

Section: Discussionsupporting

confidence: 87%

Characteristics and contributing factors of adverse drug reactions: an analytical study of patients with tuberculosis receiving treatment under the National TB Program of India

et al. 2022

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Background Tuberculosis (TB) continues to pose a serious threat to the public health system in India. Although the National Tuberculosis Elimination Program (NTEP) is providing a wide range of interventions from early diagnosis to complete treatment to reduce morbidity and mortality from TB, adverse drug reactions (ADR) remain a challenge in treatment adherence and completion. Methods An observational cross-sectional study was conducted in selected districts of Gujarat state. A total of 593 reported TB patients were recruited with an adjusted unified distribution based on the type of cases, site of diseases, and service facility through a simple random sampling method. A semi-structured questionnaire tool was used to collect socio-demographic, clinical, and ADR-related data from the TB patients. Data was analyzed for the frequency, percentage, chi-squared, and adjusted odds ratio to find the association between the variables. Results The majority of the study participants were male (87.2%), aged 15 to 60 (57.8%), daily laborers (22.4%), and married (64.2%). Over 75% of individuals had pulmonary TB, with 87% having experienced their first episode, 83% being new cases, and 44.7% having a history of addiction. ADR with mild symptoms was reported by more than a quarter (29%) of TB patients during the intensive phase (77%). The association between ADR experience and drug susceptibility was significant (p<0.005) and drug-resistant TB patients experience two times more ADRs than drug-sensitive TB patients (OR 2.04). Binomial logistic regression was carried out to describe the association between various variables and occurrence of ADRs. Conclusion The study highlighted a need to enhance health care providers’ capacity and program structure for managing ADRs among TB patients. In order to completely eliminate TB across the country, it also emphasized the attention for a holistic and all-encompassing strategy for managing TB patients at the field level.

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Section: Discussionsupporting

confidence: 87%

Characteristics and contributing factors of adverse drug reactions: an analytical study of patients with tuberculosis receiving treatment under the National TB Program of India

et al. 2022

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“…Depending on the type of medication, it may be taken regularly (e.g., for chronic diseases), or intermittently (e.g., for symptom relief). There was a relatively low rate of TB-ADR (11%) compared to rates in the literature 39,40 and this may have affected the performance of the prediction model. Moreover, the lack of association between CM and risk of TB-ADR among PWH may have been due to the sample size; and because we only considered baseline information about ART, and not the possible time-varying association of ART and TB-ADR.…”

Section: Discussionmentioning

confidence: 84%

Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil

Ridolfi,

Amorim,

Peetluk

et al. 2024

The Journal of Infectious Diseases

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Background Tuberculosis (TB) treatment–related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates. Methods We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk. Results There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74–.83) and fit (optimism-corrected slope and intercept of −0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75–4.09]) and CM use (HR, 5.26 [95% CI, 2.63–10.52]) increased TB-ADR risk. Conclusions The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.

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Prediction models for adverse drug reactions during tuberculosis treatment in Brazil

Ridolfi,

Amorim,

Peetluk

et al. 2023

Preprint

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Background: Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADR) can negatively affect adherence and treatment success rates. Methods: We developed two prediction models for TB-ADR. We included drug-susceptible pulmonary TB participants who initiated standard TB therapy. TB-ADR were determined by physician-assigned attributions of causality, and described according to affected organ system, timing, and grade. Potential predictors of TB-ADR included concomitant medication (CM) use, HIV-status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (e.g., NAT2 acetylator profile). Bootstrapped backwards selection was used to develop the models. Cox proportional hazards regression was used to evaluate TB-ADR risk. Results: There were 156 TB-ADR among 102 (11%) of the 945 participants included. Most TB-ADR were hepatic (n=82;53%), grade 2 (n=121;78%), and occurred in NAT2 slow acetylators (n=62;61%). The main prediction model included CM use, HbA1c, alcohol-use, HIV-infection, BMI, and age. The alternative model included the same variables, except replaced BMI with NAT2. Both models had good performance and fit. CM use and HIV-infection increased TB-ADR risk. Conclusions: The model with only clinical variables and that with NAT2 were highly predictive of TB-ADR. The NAT2 model provides rationale to evaluate isoniazid dose adjustment and ADR risk.

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Adverse effects and duration of treatment of TB in Canterbury, New Zealand

Cited by 4 publications

References 0 publications

Characteristics and contributing factors of adverse drug reactions: an analytical study of patients with tuberculosis receiving treatment under the National TB Program of India

Characteristics and contributing factors of adverse drug reactions: an analytical study of patients with tuberculosis receiving treatment under the National TB Program of India

Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil

Prediction models for adverse drug reactions during tuberculosis treatment in Brazil

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