Abstract 14054: Pre-Hospital Predictors of Cardiogenic Shock Among ST-Segment Elevation Myocardial Infarction Patients With and Without Cardiac Arrest: Implications for Shock Teams
Bibliographic record
Abstract
Introduction: Cardiogenic shock (CS) develops in up to 8.6% of patients with STEMI and is associated with poor outcomes. Early identification of patients at risk of CS is paramount to enable timely mobilization of shock teams to improve outcomes. Hypothesis: We hypothesized that pre-hospital clinical parameters can predict development of CS in STEMI patients undergoing primary PCI. Using these predictors, we developed a risk score to rapidly identify patients at risk of developing CS. Methods: We performed a retrospective cohort study using prospective data from a centralized STEMI registry of a healthcare system serving 1.25 million people. Patients presenting with STEMI with intent to receive primary PCI between 2012 to 2020 were included. Logistic regression was used to assess the relationship between predictors and the occurrence of CS at any point from first medical contact to hospital discharge. The prediction model was converted to a risk score by scaling of the regression coefficients. The accuracy of the risk score was determined using C-statistic. Results: Among 2736 consecutive STEMI patients, 15.2% (n=415) developed CS. Overall, 10.9% of patients had prehospital cardiac arrest, which was more likely in those with CS (46.5% vs. 4.5%, p<0.001). Patients with CS were more likely to have prolonged first medical contact-to-device time per national guidelines (74.7% vs. 53.3%, p<0.001). Regression analysis identified 8 parameters that predict CS as shown in Table 1. Two models, with and without cardiac arrest, were developed. Both models show C-statistics of 0.80 and above. Conclusions: Among STEMI patients with intent to undergo primary PCI, we identified 8 clinical parameters that strongly predict CS. This has been developed into a scoring system which can be easily used by emergency service providers to rapidly identify patients with CS and enable timely shock team activation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".