Accuracy of the Initial Rhythm to Predict a Short No-Flow Time in Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
OBJECTIVES: The no-flow time (NFT) can help establish prognosis in out-of-hospital cardiac arrest (OHCA) patients. It is often used as a selection criterion for extracorporeal resuscitation. In patients with an unwitnessed OHCA for whom the NFT is unknown, the initial rhythm has been proposed to identify those more likely to have had a short NFT. Our objective was to determine the predictive accuracy of an initial shockable rhythm for an NFT of 5 minutes or less (NFT ≤ 5). DESIGN: Retrospective analysis of prospectively collected data. SETTING: Prehospital OHCA in eight U.S. and three Canadian sites. PATIENTS: A total of 28,139 adult patients with a witnessed nontraumatic OHCA were included, of whom 11,228 (39.9%) experienced an emergency medical service-witnessed OHCA (NFT = 0), 695 (2.7%) had a bystander-witnessed OHCA, and an NFT less than or equal to 5, and 16,216 (57.6%) with a bystander-witnessed OHCA and an NFT greater than 5. INTERVENTIONS: Sensitivity, specificity, and likelihood ratios of an initial shockable rhythm to identify patients with an NFT less than or equal to 5 minutes. MEASUREMENTS AND MAIN RESULTS: The sensitivity of an initial shockable rhythm to identify patients with an NFT less than or equal to 5 was poor (25% [95% CI, 25-26]), but specificity was moderate (70% [95% CI, 69-71]). The positive and likelihood ratios were inverted (negative accuracy) (positive likelihood ratio, 0.76 [95% CI, 0.74-0.79]; negative likelihood ratio, 1.12 [95% CI, 1.10-1.12]). Including only patients with a bystander-witnessed OHCA improved the sensitivity to 48% (95% CI, 45-52), the positive likelihood ratio to 1.45 (95% CI, 1.33-1.58), and the negative likelihood ratio to 0.77 (95% CI, 0.72-0.83), while slightly lowering the specificity to 67% (95% CI, 66-67). CONCLUSIONS: Our analysis demonstrated that the presence of a shockable rhythm at the time of initial assessment was poorly sensitive and only moderately specific for OHCA patients with a short NFT. The initial rhythm, therefore, should not be used as a surrogate for NFT in clinical decision-making.
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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 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".