Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors
Bibliographic record
Abstract
Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03%-0.10% per year. Yet, the absolute number of cases around the world is high because of the sheer size of the population at risk, making SCA/SCD a major global health issue. On the basis of conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies on the association of age, sex, race, socioeconomic status, and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors are reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population are discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction is examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".