PS210. Effect of Desvenlafaxine on Altered Heart Rate Variability in Persistent Depressive Disorder
Bibliographic record
Abstract
Abstract Objective: Lower heart rate variability (HRV), a measure of autonomic function, has been noted in depression and linked with cardiovascular disease risk. Evidence suggests that some antidepressants, such as tricyclics and SNRIs, adversely impact HRV (Terhardt et al., 2013) while SSRIs appear to have no influence on HRV (Kemp et al., 2010). Desvenlafaxine is a newer SNRI used to treat depression, but there are no published studies on its benefits in chronic depression, or its influence on measure of HRV. The objectives of this pilot investigation were to investigate these issues. Methods: Twenty-three patients with persistent depressive disorder, who were enrolled in an open-label 8-week trial of monotherapy with desvenlafaxine, were included in this study. HRV was measured at baseline and post-treatment. Differences in HRV parameters were assessed pre-post treatment, as well as between those who responded to treatment (n=15) vs. non-responders (n=8). Results: Across the sample, HRV measures of time-and frequency-domains significantly decreased over the course of treatment, including SDNN (p<.01), RMSSD (p=.05), RR triangular index (p=.03), as well as total Power (p<.01) and LF/HF Power (p=.03). Significant increases in frequency-domain measures such as HF power (p=.04) were also noted. Further, comparisons between responders and non-responders revealed significant differences in time-domain measures (TINN, p=.04), and overall estimates of HRV (RR triangular index) (p=.02) post-treatment. Conclusions: The results suggest that treatment response to desvenlafaxine is associated with normalization of sympathovagal balance (HRV). Limitations of this study include small sample size and absence of a control group. Additional investigations are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".