Which Adverse Effects Influence the Dropout Rate in Selective Serotonin Reuptake Inhibitor (Ssri) Treatment?

The association between side effects and adherence to antidepressants among primary care community-dwelling older adults

Aims: To evaluate the association between adherence to antidepressants and self-reported side effects while considering their tolerability among primary care community-dwelling older adults.Methods: This is a secondary analysis of data available for 137 individuals participating in the third wave of the Étude sur la Santé des Aînés - Services study (2015-2017) conducted among older adults aged 65 years and older. Adherence to antidepressants was assessed with the 4-item Medication Assessment Questionnaire. Side effects were also self-reported from a list of 20 potential side effects related to antidepressant use, while considering their tolerability (tolerable and non-tolerable) and were then grouped into seven categories specific to organ or function systems. Multilevel logistic regression analyses were carried out to assess the association between adherence and the presence of side effects accounting for participants nested within primary health clinics.Results: In this study, 69.3% of participants were adherent to their antidepressants and 30.7% were non-adherent. Participants reporting sleep disturbance (OR = 0.58, 95% CI = 0.47-0.72), gastrointestinal system (OR = 0.64, 95% CI = 0.45-0.92), and nervous system (OR = 0.60, 95% CI = 0.48-0.78) related side effects were less likely to be adherent to their antidepressants. Participants reporting palpitations were more likely to adhere to antidepressants (OR = 2.20, 95% CI = 1.03-4.67). With regards to severity, participants reporting non-tolerable nervous system related side effects were less likely to be adherent (OR = 0.37, 95% CI = 0.26-0.53) and those reporting non-tolerable gastrointestinal system related side effects reported higher adherence to antidepressants (OR = 1.82, 95% CI = 1.08-3.08).Conclusion: Adherence to antidepressants was associated with side effects, and more precisely with sleep disturbance, gastrointestinal and nervous systems side effects.

How to improve adherence to antidepressant treatments in patients with major depression: a psychoeducational consensus checklist

Studies conducted in primary care as well as in psychiatric settings show that more than half of patients suffering from major depressive disorder (MDD) have poor adherence to antidepressants. Patients prematurely discontinue antidepressant therapy for various reasons, including patient-related (e.g., misperceptions about antidepressants, side-effects, and lack of tolerability), clinician-related (e.g., insufficient instruction received by clinicians about the medication, lack of shared decision-making, and follow-up care), as well as structural factors (e.g., access, cost, and stigma). The high rate of poor adherence to antidepressant treatments provides the impetus for identifying factors that are contributing to noncompliance in an individual patient, to implement a careful education about this phenomenon. As adherence to antidepressants is one of the major unmet needs in MDD treatment, being associated with negative outcomes, we sought to identify a series of priorities to be discussed with persons with MDD with the larger aim to improve treatment adherence. To do so, we analyzed a series of epidemiological findings and clinical reasons for this phenomenon, and then proceeded to define through a multi-step consensus a set of recommendations to be provided by psychiatrists and other practitioners at the time of the first (prescription) visit with patients. Herein, we report the results of this initiative.

Use of Selective Serotonin Reuptake Inhibitors and Risk of Hip Fracture in the Elderly: A Case-Control Study in Taiwan

BACKGROUND

This population based study investigated the relationship between use of selective serotonin reuptake inhibitors (SSRIs) and hip fractures in the elderly in Taiwan.

METHODS

Analysis of a random sample of 1 million insurance enrollees' data identified 4,891 patients with newly diagnosed hip fractures and 4,891 controls without hip fracture between 2000 and 2011. Both cases and controls were ≥65 years of age and were matched by sex, age, comorbidities, and index year of hip fracture diagnosis. Patients were considered current SSRI users if their last SSRI tablet was taken ≤7 days before the hip fracture diagnosis. Late use of SSRIs was defined as taking the last SSRI tablet ≥8 days before the hip fracture diagnosis. Non-SSRI users comprised individuals who never had an SSRI prescription. Odds ratios (ORs) and 95% confidence intervals (CIs) for hip fracture associated with SSRI use was estimated by multivariate unconditional logistic regression.

RESULTS

After adjustment for covariants, multivariate regression analysis showed that the adjusted OR of hip fracture was 2.17 for current SSRI users (95% CI: 1.60-2.93) compared with those who never used SSRIs. The adjusted OR was 1.11 for individuals with late use of SSRIs (95% CI: 0.96-1.28) and was not significant.

CONCLUSIONS

Current use of SSRIs was associated with a 2.17-fold increase in the odds of hip fracture in the elderly in Taiwan. Clinicians should consider the possibility of SSRI-associated hip fracture among old people currently taking SSRIs.

Utilizing yeast chemogenomic profiles for the prediction of pharmacogenomic associations in humans

Understanding the genetic basis underlying individual responses to drug treatment is a fundamental task with implications to drug development and administration. Pharmacogenomics is the study of the genes that affect drug response. The study of pharmacogenomic associations between a drug and a gene that influences the interindividual drug response, which is only beginning, holds much promise and potential. Although relatively few pharmacogenomic associations between drugs and specific genes were mapped in humans, large systematic screens have been carried out in the yeast Saccharomyces cerevisiae, motivating the constructing of a projection method. We devised a novel approach for the prediction of pharmacogenomic associations in humans using genome-scale chemogenomic data from yeast. We validated our method using both cross-validation and comparison to known drug-gene associations extracted from multiple data sources, attaining high AUC scores. We show that our method outperforms a previous technique, as well as a similar method based on known human associations. Last, we analyze the predictions and demonstrate their biological relevance to understanding drug response.