The Long-Term Cardiovascular Risks of Duloxetine Use in Older Adults: A Retrospective Medical Record-Based Adverse Drug Reaction Assessment
Bibliographic record
Abstract
Background: Duloxetine, a Serotonin–Norepinephrine Reuptake Inhibitor (SNRI), is frequently used to treat diabetic peripheral neuropathy, depression, and fibromyalgia. However, its long-term cardiovascular implications in older individuals remain underexplored, particularly in those with pre-existing cardiovascular diseases. This medical record assessment aimed to evaluate the potential cardiovascular risks of duloxetine use in older persons after prolonged use. Methods: We evaluated adverse drug reactions (ADRs) using six medical records from elderly individuals (aged 70–79) with cardiovascular comorbidities who received duloxetine (≥60 mg daily) for anxiety, depression, and chronic pain. ADRs were assessed using the Naranjo ADR Probability Scale, the Modified Hartwig and Siegel Severity Scale, and the Karch and Lasagna Algorithm. Clinical outcomes were assessed before and after duloxetine dose reduction or withdrawal. Results: All the patients had cardiovascular-related ADRs, such as peripheral cyanosis, vasoconstriction, atrial fibrillation, and hypertensive episodes. Five of the six patients experienced mild cognitive impairment [Montreal Cognitive Assessment (MoCA) scores of 11–24/30]. A positive dechallenge (symptom resolution) was observed in all medical records after decreasing or discontinuing duloxetine. It is interesting to note that four medical records demonstrated significant improvement in cyanosis, blood pressure, and anxiety after decreasing or discontinuing duloxetine use. There was no rechallenge in this study. The causality was considered probable (Naranjo Scale), and ADRs were categorized as moderately severe (Hartwig and Siegel Scale) in all the medical records. However, with adequate monitoring, the ADRs were considered preventable (Schumock and Thornton Scale). Conclusions: Long-term duloxetine use could cause significant cardiovascular problems in older individuals, particularly those who already have cardiovascular difficulties. Regular monitoring of cardiovascular function and early steps such as dose adjustment or drug withdrawal of duloxetine may reduce the prognosis of ADRs. More studies are required to create safer treatment strategies for managing depression and anxiety in older people with cardiovascular issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".