Pulseless Electric Activity
Bibliographic record
Abstract
S udden cardiac arrest (SCA) remains an important public health challenge.Despite a dramatic decrease in the ageadjusted risk of SCA, the cumulative number of fatal SCAs in the United States remains large.Estimates range from <170 000 to >450 000 fatal SCAs per year; a figure in the range of 300 000 to 370 000 per year is likely the best current estimate.1 SCA appears to account for ≈50% of all cardiovascular deaths, 2 and it is estimated that 50% of the SCAs are the first clinical expression of previously undiagnosed heart disease.2,3 Most out-of-hospital cardiac arrests (80%) occur in private homes or other living facilities.4 Electric mechanisms associated with SCA are broadly classified into tachyarrhythmic and nontachyarrhythmic categories, the latter including pulseless electric activity (PEA; formerly referred to as electromechanical dissociation), asystole, extreme bradycardia, and other mechanisms often associated with noncardiac factors (Table ).The first approaches to the problem of SCA focused on ventricular fibrillation (VF) and pulseless ventricular tachycardia (VT).An early impact on the prevention and treatment of VF and VT was realized in patients with acute coronary syndromes >50 years ago, 5 followed by the development of strategies for responding to out-of-hospital cardiac arrest, implantable cardioverter-defibrillators, and defibrillation by lay responders.Data from the Seattle emergency rescue system 6 and elsewhere 7-9 have identified progressive reductions in the number of responses to SCA over 2 to 3 decades.This change was due primarily to a reduction in the number of ventricular tachyarrhythmic events identified by emergency medical services responders.In the Seattle data, the incidences of PEA and asystole had not changed over the 3 decades of observation and therefore have emerged as proportionately more frequent mechanisms than VT/VF.Whether this also reflects the emergence of greater absolute numbers of PEA and asystole, as suggested in other studies, 7,9 possibly as a result of broader deployment of emergency rescue systems with longer average response times, or evolving changes in patient substrate remains to be determined.As preventive and therapeutic interventions for VT/VF were developing, PEA and asystole did not receive a great deal of attention.Currently, however, PEA should receive greater attention on the basis of the combination of its increasing proportion of the SCA spectrum, its much lower survival rate than that after VT/VF arrests, emerging suggestions that survival may be improved, and uncertainty whether there is a proportional versus an absolute increase in incidence.The definitions, prognostics, and potential for improvements in therapeutic opportunities for PEA, in contrast to asystole, are of sufficient interest and complexity that this condition now warrants a strong investigative focus within the spectrum of SCA mechanisms and management challenges.Expanding mechanism-related concepts offers the hope for both better stratification and development of more effective therapeutic interventions.Because of the increasing prevalence of and limited scientific information on PEA, the National Heart, Lung, and Blood Institute sponsored a workshop on PEA that convened on June 6, 2012, in Bethesda, MD. 10 It was designed to explore current knowledge and future directions for research in the prediction, prevention, and management of PEA, as well as probable mechanistic pathways that might translate to clinical care.The working group participants had expertise in basic, clinical, and epidemiological aspects of SCA generally and PEA in particular.This article summarizes the deliberations, focusing
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".