Alterations in Respiratory Heart Rate Variability in Brain-Injured Neuro-ICU Patients Compared with Healthy Humans
Bibliographic record
Abstract
Abstract Background Respiratory heart rate variability (RespHRV), the physiological variation in heart rate in phase with breathing, is mainly generated by central brainstem mechanisms. Its characteristics and determinants in brain-injured patients in the neuro-intensive care unit (neuro-ICU) are poorly understood. Objective To characterize RespHRV amplitude and phase in brain-injured patients compared to healthy participants, and to explore clinical variables influencing RespHRV in the neuro-ICU. Methods We analyzed 55 brain-injured patients (traumatic brain injury, aneurysmal subarachnoid hemorrhage, or other causes) and 31 healthy controls. ECG and respiratory signals were recorded and processed to extract cycle-by-cycle RespHRV amplitude and phase. Group differences were assessed using Mann–Whitney and Watson–Williams tests. In an additional analysis, 55 patients’ RespHRV amplitude and phase were modeled using generalized linear mixed-effects models to evaluate the impact of sedation, mechanical ventilation mode, vasoactive and analgesic drugs, and time, including random intercepts and slopes for subjects. Results Compared to controls, brain-injured patients exhibited a significantly lower RespHRV amplitude (1.04 [0.45, 1.96] vs. 6.21 [4.08, 9.34] bpm; p < 0.001) and an inverted RespHRV phase, with peak heart rate occurring during expiration rather than inspiration. Mixed-effects modeling revealed that machine-triggered ventilation and high level of sedation induced a significant reduction in RespHRV amplitude. Conclusions Brain-injured patients demonstrate markedly impaired central generation of RespHRV, with peripheral contributors likely accounting for the remaining variability. Ventilation mode and pharmacological interventions strongly alter RespHRV. Restoration of normal RespHRV patterns may serve as a physiological marker of autonomic and brainstem recovery, warranting further investigation in longitudinal 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".