Impact of Antidepressant Therapy on Survival in Glioblastoma Patients

Overview

This retrospective cohort study evaluated the association between antidepressant therapy and overall survival in glioblastoma patients, accounting for molecular and socioeconomic factors. The analysis incorporated time-varying Cox regression models to adjust for confounders and immortal time bias, revealing differential impacts of antidepressant classes on survival outcomes.

Background

Glioblastoma is the most common primary brain malignancy in adults with a median survival of approximately 15 months despite aggressive treatment. Depression affects nearly 40% of glioblastoma patients and is linked to poorer outcomes. Antidepressants may influence glioblastoma progression through biological mechanisms and by improving depressive symptoms, potentially enhancing treatment adherence and daily functioning. However, prior studies have reported conflicting results regarding the impact of antidepressant use on glioblastoma survival.

Data Highlights

The study included adult glioblastoma patients treated between 2008 and 2023 with complete medication records. Variables analyzed included age, race, gender, insurance status, extent of resection, IDH mutation, MGMT methylation, chemotherapy, radiotherapy, socioeconomic status via Area Deprivation Index (ADI), and rural-urban classification (RUCA). Antidepressant use was categorized into SSRIs, SNRIs, serotonin modulators, tricyclic antidepressants, and atypical antidepressants. Multivariate Cox regression with time-varying covariates was used to assess survival associations.

Key Findings

• Antidepressant therapy was common among glioblastoma patients, with usage categorized into five pharmacologic classes.
• Multivariate analysis adjusted for demographic, molecular, treatment, and socioeconomic factors to isolate antidepressant effects on survival.
• Time-varying Cox regression models addressed immortal time bias by accounting for changes in antidepressant exposure over follow-up.
• Previous literature shows conflicting survival associations with antidepressant use, highlighting the need for this comprehensive analysis.
• The study uniquely incorporated socioeconomic deprivation (ADI) and rural-urban status (RUCA) as covariates in survival modeling.

Clinical Implications

Clinicians should consider the potential differential effects of antidepressant classes on glioblastoma survival when managing depression in these patients. Incorporating molecular and socioeconomic factors into treatment planning may better inform prognosis and therapeutic decisions. Further prospective studies are warranted to clarify causality and optimize antidepressant selection in this population.

Conclusion

This study advances understanding of how antidepressant therapy relates to survival in glioblastoma patients by integrating comprehensive clinical, molecular, and socioeconomic data. The findings underscore the complexity of antidepressant effects and the importance of individualized patient management.

References

1. Ostrom et al. 2020 -- CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States
2. Stupp et al. 2005 -- Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma
3. Massie et al. 2011 -- Depression in Patients with Glioblastoma
4. Caudill et al. 2019 -- SSRI Therapy and Glioblastoma Survival
5. Seliger et al. 2021 -- Antidepressant Use and Survival in Glioblastoma
6. Edstrom et al. 2022 -- Multicenter Registry Analysis of Antidepressants in Glioblastoma
7. Otto-Meyer et al. 2023 -- Antidepressant Effects on Glioblastoma Outcomes
8. Kind et al. 2018 -- Neighborhood Atlas and Area Deprivation Index
9. USDA ERS 2020 -- Rural-Urban Commuting Area Codes
10. Von Elm et al. 2007 -- STROBE Statement: Guidelines for Reporting Observational Studies