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Published on: June 12, 2021
Prehospital Prediction of Cardiogenic Shock Among Patients With ST-Segment-Elevation Myocardial Infarction: The EARLY
Cathevine Yang1,2, Terry Lee3, Andrew Kochan1,4
1Division of Cardiology, Department of Medicine University of British Columbia Vancouver British Columbia Canada.
Insights
A new EARLY SHOCK score predicts cardiogenic shock in ST-elevation myocardial infarction patients. This tool aids prehospital identification, enabling rapid shock team activation and improving outcomes.
Area of Science:
- Cardiology
- Emergency Medicine
- Clinical Prediction Models
Background:
- Cardiogenic shock (CS) complicates up to 10% of ST-segment-elevation myocardial infarction (STEMI) cases.
- High mortality and morbidity rates are associated with CS in STEMI patients.
- Effective prehospital risk stratification for CS is crucial.
Purpose of the Study:
- To develop and validate a clinical scoring system for predicting in-hospital CS in STEMI patients.
- To create a tool easily applicable in the prehospital setting.
- To facilitate early identification and management of CS.
Main Methods:
- Retrospective cohort study using prospective data.
- Logistic regression analysis to identify predictors of in-hospital CS.
- Internal validation of the prediction model (C statistic=0.87) and conversion to a risk score.
Main Results:
- 2736 STEMI patients undergoing primary percutaneous coronary intervention were analyzed.
- 15.2% of patients developed in-hospital CS.
- Eight predictors formed the EARLY SHOCK score: EMS Heart Rate and Systolic Blood Pressure, Age, Renal Replacement, Infarction Location, Diabetes, Heart Failure, and Cardiac Arrest.
Conclusions:
- The EARLY SHOCK score effectively predicts CS in STEMI patients.
- The score is designed for easy prehospital application to enable early shock team activation.
- External validation is pending for wider clinical implementation.
Background:
Cardiogenic 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.
Methods:
The 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.
Results:
From 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.
Conclusions:
The 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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