Sensor technologies to detect out-of-hospital cardiac arrest: A systematic review of diagnostic test performance
Bibliographic record
Abstract
Aim: Cardiac arrest (CA) is the cessation of circulation to vital organs that can only be reversed with rapid and appropriate interventions. Sensor technologies for early detection and activation of the emergency medical system could enable rapid response to CA and increase the probability of survival. We conducted a systematic review to summarize the literature surrounding the performance of sensor technologies in detecting OHCA. Methods: We searched the academic and grey literature using keywords related to cardiac arrest, sensor technologies, and recognition/detection. We included English articles published up until June 6, 2022, including investigations and patent filings that reported the sensitivity and specificity of sensor technologies to detect cardiac arrest on human or animal subjects. (Prospero# CRD42021267797). Results: We screened 1666 articles and included four publications examining sensor technologies. One tested the performance of a physical sensor on human participants in simulated CA, one tested performance on audio recordings of patients in cardiac arrest, and two utilized a hybrid design for testing including human participants and ECG databases. Three of the devices were wearable and one was an audio detection algorithm utilizing household smart technologies. Real-world testing was limited in all studies. Sensitivity and specificity for the sensors ranged from 97.2 to 100% and 90.3 to 99.9%, respectively. All included studies had a medium/high risk of bias, with 2/4 having a high risk of bias. Conclusions: Sensor technologies show promise for cardiac arrest detection. However, current evidence is sparse and of high risk of bias. Small sample sizes and databases with low external validity limit the generalizability of findings.
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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 | 0.019 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".