ArticleJournal of the American Heart Association2025
Prehospital Prediction of Cardiogenic Shock Among Patients With ST-Segment-Elevation Myocardial Infarction: The EARLY SHOCK Score.
Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.European journal of clinical investigation · 2026Observational
- Impact of stress hyperglycemia ratio on incidence of in-hospital cardiogenic shock in patients with ST-elevation myocardial infarction: a prospective, multicenter study.Frontiers in endocrinology · 2025Observational
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12 authors.
Funding
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Abstract
backgroundCardiogenic shock (CS) develops in up to 10% of patients with ST-segment-elevation myocardial infarction and is associated with high mortality and morbidity rates. The objective of the current study was to generate a clinical scoring system that can be easily applied in the prehospital setting to predict the development of in-hospital CS among patients undergoing primary percutaneous coronary intervention for ST-segment-elevation myocardial infarction.
methodsThe authors conducted a retrospective cohort study using prospective data from a dual hub-and-spoke health system. Logistic regression was used to assess the relationship between prespecified clinical predictors and the occurrence of in-hospital CS. Internal validation was conducted to assess the C statistic and calibration curve of the prediction model. The prediction model was converted to a risk score by scaling of the regression coefficients.
resultsFrom April 1, 2012, to December 31, 2020, there were 2736 consecutive patients with ST-segment-elevation myocardial infarction undergoing primary percutaneous coronary intervention. Of these, 415 (15.2%) developed CS. Eight strong predictors were independently associated with CS by multivariable analysis and used to develop a prediction model. The model achieved a C statistic of 0.87. The EARLY SHOCK risk scoring algorithm incorporates Emergency Medical Services Heart Rate and Systolic Blood Pressure, Age, Renal Replacement, Location of Infarction, Sugar (diabetes), Heart Failure, and Cardiac Arrest.
conclusionsThe authors identified 8 clinical variables that strongly predict CS among patients with ST-segment-elevation myocardial infarction undergoing primary percutaneous coronary intervention. This has been developed into the EARLY SHOCK score, which can be easily applied in the prehospital setting to rapidly identify CS and enable shock team activation. External validation for the scoring system is pending for broader application.
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