Scoping review of the association between postsurgical pain and heart rate variability parameters
Bibliographic record
Abstract
Surgical interventions can elicit neuroendocrine and sympathovagal responses, leading to cardiac autonomic imbalance. Cardiac complications account for approximately 30% of postoperative complications. Altered heart rate variability (HRV) was initially described in the 1970s as a predictor of acute coronary syndromes and has more recently been shown to be an independent predictor of postoperative morbidity and mortality after noncardiac surgery. In general, HRV reflects autonomic balance, and altered HRV measures have been associated with anesthetic use, chronic pain conditions, and experimental pain. Despite the well-documented relationship between altered HRV and postsurgical outcomes and various pain conditions, there has not been a review of available evidence describing the association between postsurgical pain and HRV. We examined the relationship between postsurgical pain and HRV. MEDLINE and EMBASE databases were searched until December 2020 and included all studies with primary data. Two reviewers independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Review of Interventions. A total of 8 studies and 1002 participants were included. Studies examined the association of postsurgical pain and HRV or analgesia nociception index derived from HRV. There was a statistically significant association between HRV measures and postsurgical pain in 6 of 8 studies. Heterogeneity of studies precluded meta-analyses. No studies reported cardiovascular outcomes. There is a potential association between postsurgical pain and HRV or analgesia nociception index, although results are likely impacted by confounding variables. Future studies are required to better delineate the relationship between postsurgical pain and HRV and impacts on cardiovascular outcomes.
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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 | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".