Preoperative Heart Rate Variability Predicts Postinduction Hypotension in Patients with Cervical Myelopathy
Bibliographic record
Abstract
Background: Autonomic dysfunction, commonly seen in patients with cervical myelopathy, may lead to a decrease in blood pressure intraoperatively. Objective: The aim of our study is to determine if changes in Heart rate variability (HRV) could predict hypotension after induction of anesthesia in patients with cervical myelopathy undergoing spine surgery. Methods and Material: In this prospective observational study, 47 patients with cervical myelopathy were included. Five-minute resting ECG (5 lead) was recorded preoperatively and HRV of very low frequency (VLF), low frequency (LF), and high frequency (HF) spectra were calculated using frequency domain analysis. Incidence of hypotension (MAP <80 mmHg, lasting >5 min) and the number of interventions (40 mcg of phenylephrine or 5 mg of ephedrine) required to treat the hypotension during the period from induction to surgical incision were recorded. HRV indices were compared between the hypotension group and the stable group. Results: The incidence of hypotension after induction was 74.4% (35/47) and the median (IQR) interventions needed to treat hypotension was 2 (0.5-6). Patients who experienced hypotension had lower HF power and higher LF-HF ratios. A LF/HF >2.5 indicated postinduction hypotension likely. There was a correlation between increasing LF-HF ratio and the number of interventions that needs to maintain the MAP above 80 mmHg. Conclusion: HF power was lower and LF-HF ratio was higher in patients with cervical myelopathy who developed postinduction hypotension. Hence, preoperative HRV analysis can be useful to identify patients with cervical myelopathy who are at risk of post-induction hypotension.
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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".