Abstract 221: Machine Learning Algorithms For The Prognostication Of Return Of Spontaneous Circulation
Bibliographic record
Abstract
Introduction: Several factors are known to influence return of spontaneous circulation (ROSC) following out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Machine learning (ML) methods are capable of analyzing large datasets to elucidate the clinical and prognostic value of specific variables. In cardiac arrest, ML may help identify predictors of ROSC in OCHA and ICHA. Purpose: The present systematic review summarizes the literature on ML algorithms used to predict ROSC in OHCA and IHCA. Methods: PubMed, EMBASE, Web of Science, and Cochrane were searched to identify articles. Studies on human subjects with OHCA or IHCA which used ML methods to predict ROSC were included. Results: A total of 4,094 studies were identified through a literature search. Ten were included in the final analysis with a total sample size of 240,798 patients (240,798 (93%) male). Studies used logistic regression (n=7), random forest models (n=2), or deep learning (n=1). Sociodemographic variables beyond age (n=8) and sex (n=5) were not used. Variables used for predicting ROSC included age (n=8), type of rhythm (n=7), witnessed arrest (n=5), and time to emergency services arrival (n=5). Most models were built using data from OHCA patients (n=7), with only three focused on IHCA. Studies utilized a variety of parameters for reporting the prognostic value of their models, including predictive accuracy (69%-99%), area under the receiver operating characteristic curve (0.71-0.83), sensitivity (50.2%-76.0%), and specificity (61.0%-92.9%). Conclusion: Despite the prevalence of cardiac arrest and advances in AI, only ten studies examined ML in the prognostication of ROSC. Logistic regression was predominantly used in the included studies, and there is a paucity of data on the efficacy of deep learning models. Furthermore, heterogeneity in the predictive efficacy of studied ML models merits additional work and the need for large-scale trials comparing ML to conventional methods and clinician judgment. Critically, there is also a need for greater data inclusivity in model development, understanding that marginalized populations are less likely to receive CPR.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".