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Enrolling by Invitation

NCT Number: NCT07090382

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 (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.

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Key information

Conditions

Cardiogenic Shock Acute Coronary Syndrome Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI) Cardiogenic Shock Acute Cardiogenic Shock Post Myocardial Infarction Cardiovascular Diseases Heart Diseases Infarction Ischemia Myocardial Infarction Myocardial Ischemia Necrosis PCI Pathologic Processes Pathological Conditions, Signs and Symptoms Shock Shock, Cardiogenic Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Premedix Academy

Bratislava, 81102, Slovakia

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients aged >18 years.
  • Admitted for acute coronary syndrome in CCU

Exclusion criteria

  • Patients aged < 18 years.
  • Patients in CSWG-SCAI C, D or E CS the before the admission to CCU.

Treatment and study plan

Primary outcomes

  1. Discriminatory Power of the STOPSHOCK Score for Predicting Cardiogenic Shock

    Time frame: Up to hospital discharge (average of 14 days)

    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.

Secondary outcomes

  1. Sensitivity (Recall) of the STOPSHOCK Score

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  2. Specificity of the STOPSHOCK Score

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  3. Positive Predictive Value (Precision)

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  4. Negative Predictive Value

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  5. F1 Score of the STOPSHOCK Score

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  6. Accuracy of the STOPSHOCK Score

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  7. Area Under the Precision-Recall Curve (PR AUC)

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  8. Matthews Correlation Coefficient

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  9. Youden's J Statistic

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  10. Brier Score

    Time frame: Up to hospital discharge (average of 14 days)

    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.

  11. Calibration Slope and Intercept

    Time frame: Up to hospital discharge (average of 14 days)

    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.

Sponsors and collaborators

Lead sponsor

Premedix Academy

Other

Registry information

Acronym: STOPSCHOCK

Important dates

Study start
2025
Primary completion
2025
Study completion
2026
First posted
Jul 29, 2025
Registry last updated
Jul 29, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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