Autonomic Nervous System Response to Epidural Analgesia in Laboring Patients by Wavelet Transform of Heart Rate and Blood Pressure Variability
Bibliographic record
Abstract
BACKGROUND: Epidurals are effective in relieving labor pain but result in a sympathectomy that may compromise maternal hemodynamic stability and fetal perfusion. Decreases in blood pressure and heart rate can be corrected, but markers of autonomic activity would be useful to predict and prevent such changes. The goal of this study was to find markers describing the changes in autonomic nervous system activity with epidural anesthesia in laboring patients. METHODS: The authors analyzed heart rate variability and blood pressure variability in 13 laboring patients using wavelet transform, a time-frequency analysis that accommodates rapid changes in autonomic activity. Heart rate and blood pressure variability were obtained 5 min before and 10 min after injection of 20 ml bupivacaine, 0.125%, and 50 microg fentanyl in the epidural space. RESULTS: Blood pressure and heart rate were not affected by epidural analgesia. However, high-frequency power of heart rate variability increased after epidural (increase in parasympathetic drive). The ratio of low-frequency:high-frequency power of heart rate variability decreased. High- and low-frequency power of blood pressure variability decreased (decrease in sympathetic outflow). CONCLUSIONS: Indices of parasympathetic and sympathetic activity after neuraxial blockade in laboring patients can be obtained by analysis of both heart rate variability and blood pressure variability. The analysis by wavelet transform can discern changes in autonomic activity when values of blood pressure and heart rate do not vary significantly. Whether this technique could be used to predict and prevent hemodynamic compromise after neuraxial blockade merits further studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".