Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality approaching 50% despite the use of percutaneous mechanical circulatory support devices (pMCS). Identifying high-risk patients prior to the development of CS could allow pre-emptive use of pMCS possibly preventing CS. For this purpose, we derived and externally validated a machine learning score to predict in-hospital CS in patients with ACS with c-statistics: 0.844 (95% confidence interval, 0.841-0.847). STOPSCHOCK score is available as a web or smartphone application.

The aim of this study is to prospectively validate the STOPSHOCK score on a large cohort of ACS patients in a real- world clinical environment.

Prospective Validation of the STOPSHOCK Score - Artificial Intelligence Based Predictive Scoring System to Identify the Risk of Developing Cardiogenic Shock (CS) in Patients Suffering From Acute Coronary Syndrome (ACS)

Cardiogenic Shock

Cardiogenic Shock Acute

Cardiogenic Shock Post Myocardial Infarction

Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI)

Cardiogenic shock is a serious life-threatening condition affecting almost 10% of patients suffering from acute coronary syndrome (ACS). When untreated, it can rapidly progress to collapse of circulation and sudden death. Despite recent improvements in diagnostic and treatment options, mortality remains incredibly high, reaching nearly 50%. Currently available mechanical circulatory support devices can replace the function of the heart and/or lungs, thereby essentially eliminating the primary cause. However, cardiogenic shock is not only an isolated decrease in cardiac function but a rapidly progressing multiorgan dysfunction accompanied by severe cellular and metabolic abnormalities. The window for successful treatment is relatively narrow, and when missed, even the elimination of the underlying primary cause is not enough to reverse this vicious circle. The ability to identify high-risk patients prior to the development of shock would allow to take pre-emptive measures, such as the implantation of mechanical circulatory support, and thus prevent the development of shock leading to improved survival. For this purpose, Premedix Academy has developed and validated a predictive scoring system STOP SHOCK (Score TO Predict SHOCK). This scoring system showed better prediction compared to standard models and was accepted to the Late- Breaking Science section at the European Society of Cardiology (ESC) Congress 2024. STOP SHOCK was validated on an external cohort of 5123 ACS patients with area under the receiver operating characteristic curve (ROC AUC) of 0.844 (95% confidence interval: 0.841-0.8470) surpassing other externally validated cardiogenic shock (CS) models (e.g. ORBI score). Furthermore, our model is based on variables that are readily available at the first contact with patients and thus STOPSHOCK can be utilized in emergency room (ER) or ambulance even before catheterization. Novelty of our project is also in the concept of continuous training, improvement, and validation to ensure validity and clinical applicability in the future as well. Current medical models are developed, verified, and published. Once the model enters medical practice, research teams will either validate it or replace it with their own model based on a new cohort of patients. However, experience from other fields shows that as soon as machine learning models are deployed, their performance degrades. In order to preserve and even further improve the model, continuous performance monitoring and training/retraining are vital. A small prospective validation study on a cohort of 103 consecutive higher-risk ACS patients, enrolled in intensive cardiac care units in 8 centers from USA, Europe, and Asia demonstrated very good performance with ROC AUC of 0.97 and was presented at the 2023 American Heart Association Annual Meeting. The STOPSHOCK score is currently available as a smartphone application and as an online calculator: https://stopshock.org.

The primary objective of this study is to prospectively validate the STOPSHOCK score on a large cohort of ACS patients. The methods and results of this project follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

The primary outcome is the ability of the STOPSHOCK score to predict the development of cardiogenic shock in patients admitted with acute coronary syndrome. This will be assessed using the area under the receiver operating characteristic curve (ROC AUC), comparing the predicted risk score to the actual occurrence of cardiogenic shock during hospitalization.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

The proportion of patients who develop cardiogenic shock during hospitalization and were correctly identified as high-risk by the STOPSHOCK score at admission.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

The proportion of patients who do not develop cardiogenic shock and were correctly identified as low-risk by the STOPSHOCK score.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

he proportion of patients identified as high-risk by the STOPSHOCK score who actually develop cardiogenic shock during hospitalization.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

The proportion of patients identified as low-risk by the STOPSHOCK score who do not develop cardiogenic shock.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

The harmonic mean of precision and sensitivity, providing a balanced metric that accounts for both false positives and false negatives.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

Proportion of all predictions (true positives and true negatives) that are correctly classified by the STOPSHOCK score.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

Metric summarizing the trade-off between precision and recall for the STOPSHOCK score, especially relevant given the low incidence of cardiogenic shock.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

Balanced performance metric accounting for true and false positives and negatives, especially useful in imbalanced datasets such as cardiogenic shock prediction.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

Metric calculated as Sensitivity + Specificity - 1, representing the optimal threshold for distinguishing between high- and low-risk patients.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

Mean squared difference between predicted probabilities and actual outcomes of cardiogenic shock, assessing the calibration and accuracy of the STOPSHOCK score.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

Metrics used to assess how well the STOPSHOCK score's predicted probabilities align with observed cardiogenic shock outcomes; slope = 1 and intercept = 0 indicate perfect calibration.

STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.

This cohort includes adult patients (age >18 years) admitted to the coronary care unit (CCU) or intensive care unit (ICU) with a diagnosis of acute coronary syndrome (ACS), including STEMI, NSTEMI, and unstable angina. Patients are enrolled at the time of admission before the development of cardiogenic shock. The STOPSHOCK score is calculated using clinical variables available at first contact. Patients are followed during hospitalization to determine whether cardiogenic shock develops. No intervention is applied.

Prospective Validation of the STOPSHOCK Score - Artificial Intelligence Based Predictive Scoring System to Identify the Risk of Developing Cardiogenic Shock (CS) in Patients Suffering From Acute Coronary Syndrome (ACS)

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Placebo

Accepting

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