Peri‐mortem arrhythmias in the non‐cardiac intensive care unit
Bibliographic record
Abstract
BACKGROUND: Cardiovascular failure is recognized as a common final pathway at the end of life but there is a paucity of data describing terminal arrhythmias. AIM: We aimed to describe arrhythmias recorded peri-mortem in critically ill patients. STUDY DESIGN: We enrolled intensive care unit patients admitted to two tertiary Canadian medico-surgical centres. Participants wore a continuous electrocardiogram (ECG) monitor for 14 days, until discharge, removal or death. We recorded all significant occurrences of arrhythmias in the final hour of life. RESULTS: Among 39 patients wearing an ECG monitor at the time of death, 22 (56%) developed at least 1 terminal arrhythmia as adjudicated by an arrhythmia physician: 23% (n = 9) had ventricular fibrillation/polymorphic ventricular tachycardia, 18% (n = 7) had sinoatrial pauses, 15% (n = 6) had atrial fibrillation and 13% (n = 5) had high-degree atrioventricular block. Five participants (13%) developed multiple arrythmias. CONCLUSIONS: Arrhythmias are common in dying critically ill patients. There is a roughly even distribution between ventricular arrhythmias, atrial fibrillation, sinus node dysfunction and atrioventricular block. RELEVANCE TO CLINICAL PRACTICE: The results of this study may be most useful for critically ill patients who are organ donation candidates. The appearance of arrhythmias may serve as a marker of change in clinical status for organ donation teams to plan mobilization efforts. In participants who are sedated or intubated, arrhythmias could be a surrogate marker for respiratory or neurologic changes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".