Dataset Information

Association between polypharmacy and depression relapse in individuals with comorbid depression and type 2 diabetes: a UK electronic health record study.

ABSTRACT:

Background

Individuals with physical comorbidities and polypharmacy may be at higher risk of depression relapse, however, they are not included in the 'high risk of relapse' group for whom longer antidepressant treatment durations are recommended.

Aims

In individuals with comorbid depression and type 2 diabetes (T2DM), we aimed to investigate the association and interaction between depression relapse and (a) polypharmacy, (b) previous duration of antidepressant treatment.

Method

This was a cohort study using primary care data from the UK Clinical Practice Research Datalink (CPRD) from years 2000 to 2018. We used Cox regression models with penalised B-splines to describe the association between restarting antidepressants and our two exposures.

Results

We identified 48 001 individuals with comorbid depression and T2DM, who started and discontinued antidepressant treatment during follow-up. Within 1 year of antidepressant discontinuation, 35% of participants restarted treatment indicating depression relapse. As polypharmacy increased, the rate of restarting antidepressants increased until a maximum of 18 concurrent medications, where individuals were more than twice as likely to restart antidepressants (hazard ratio (HR) = 2.15, 95% CI 1.32-3.51). As the duration of previous antidepressant treatment increased, the rate of restarting antidepressants increased - individuals with a previous duration of ≥25 months were more than twice as likely to restart antidepressants than those who previously discontinued in <7 months (HR = 2.36, 95% CI 2.25-2.48). We found no interaction between polypharmacy and previous antidepressant duration.

Conclusions

Polypharmacy and longer durations of previous antidepressant treatment may be associated with depression relapse following the discontinuation of antidepressant treatment.

SUBMITTER: Jeffery A

PROVIDER: S-EPMC9929703 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Association between polypharmacy and depression relapse in individuals with comorbid depression and type 2 diabetes: a UK electronic health record study.

Jeffery Annie A Bhanu Cini C Walters Kate K Wong Ian C K ICK Osborn David D Hayes Joseph F JF

The British journal of psychiatry : the journal of mental science 20230301 3

<h4>Background</h4>Individuals with physical comorbidities and polypharmacy may be at higher risk of depression relapse, however, they are not included in the 'high risk of relapse' group for whom longer antidepressant treatment durations are recommended.<h4>Aims</h4>In individuals with comorbid depression and type 2 diabetes (T2DM), we aimed to investigate the association and interaction between depression relapse and (a) polypharmacy, (b) previous duration of antidepressant treatment.<h4>Metho ...[more]

PMID: 36451601

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Project description:Background: Type 2 diabetes (T2D)-related depression has a significant impact on quality of life and leads to greater morbidity and mortality. Current educational and treatment programs for T2D rarely include a specific depression-prevention component, focusing largely on remediating depressive symptoms that have reached clinical levels. Objective: Given the vast field of research on the association between T2D and depression, and the unknown status of prevention efforts for the latter, the goal of this scoping review was to conduct a synopsis of intervention strategies specifically targeting the prevention of depression among adults with T2D. Eligibility Criteria: (1) participants aged 18 and over with T2D; (2) experimental and quasi-experimental designs (3) intervention strategies seeking to prevent the onset or worsening of (non-clinical) depressive symptoms; (4) a valid measure of depressive symptoms; (5) full-text articles available in English or French. Sources of Evidence: Databases including Medline, PubMed, and SCOPUS were searched between 2000 and 2018 resulting in 4,219 potential articles. Charting Methods: This review was conducted in-line with the current methodological framework for scoping reviews. Titles, abstract and full text articles were screened independently and in duplicate. A narrative analysis was conducted to synthesize study characteristics and the nature of intervention strategies and components. Results: Twelve studies were identified with the primary aim of preventing the incidence of depressive symptoms or improving non-clinical depression levels. Individual and group-based approaches included educational interventions incorporating diabetes self-management, problem-solving, and resilience-focused approaches, emotion-targeted techniques as well as alternative interventions. Self-monitoring, home practices, and motivational interviewing were common elements. Conclusions: This review lays the groundwork for future studies seeking to develop, validate, and improve prevention strategies targeting the diabetes-depression comorbidity. More studies over longer periods and with larger samples are needed to capture the effects of prevention efforts.

Project description:BackgroundCardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.MethodsWe used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means.ResultsThe standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654-0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646-0.656) or means (C-index = 0.650, 95% CI: 0.645-0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004-0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000-0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000-0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002-0.005).ConclusionIncorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.

Dataset Information

Association between polypharmacy and depression relapse in individuals with comorbid depression and type 2 diabetes: a UK electronic health record study.

Background

Aims

Method

Results

Conclusions

Publications

Association between polypharmacy and depression relapse in individuals with comorbid depression and type 2 diabetes: a UK electronic health record study.

Similar Datasets

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