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)

NCT ID: NCT07090382

Last Updated: 2025-07-29

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Total Enrollment: 1046 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-06-01
• Study Completion Date: 2026-04-30

Brief Summary

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.

Detailed Description

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.

Conditions

Cardiogenic Shock; Cardiogenic Shock Acute; Cardiogenic Shock Post Myocardial Infarction; Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI); PCI

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

ACS Patients Admitted to CCU

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.

No interventions assigned to this group

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Premedix Academy

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Allan Böhm, MD, PhD, MSc, MBA, FESC, FJCS

Role: PRINCIPAL_INVESTIGATOR

Premedix Academy

Branislav Bezák, MD, PhD

Role: STUDY_DIRECTOR

Premedix Academy

Locations

Premedix Academy

Bratislava, , Slovakia

Countries

Slovakia

References

Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.

Reference Type: BACKGROUND

PMID: 25560730 (View on PubMed)

Böhm A, Jajcay N, Spartalis M, et al. Abstract 14290: Prospective Clinical Validation of the STOPSHOCK Smartphone Application - Artificial Intelligence Model for Prediction of Cardiogenic Shock in Patients With Acute Coronary Syndrome. Circulation 2023; 148.

Reference Type: BACKGROUND

Tran V, Pham H, Yang B-S, Nguyen T. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing 2012; 32: 320-30.

Reference Type: BACKGROUND

Grohmann J, Nicholson P, Iglesias J, Kounev S, Lugones D. Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning; 2019.

Reference Type: BACKGROUND

Bohm A, Segev A, Jajcay N, Krychtiuk KA, Tavazzi G, Spartalis M, Kollarova M, Berta I, Jankova J, Guerra F, Pogran E, Remak A, Jarakovic M, Sebenova Jerigova V, Petrikova K, Matetzky S, Skurk C, Huber K, Bezak B. Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome. Eur Heart J Digit Health. 2025 Jan 6;6(2):240-251. doi: 10.1093/ehjdh/ztaf002. eCollection 2025 Mar.

Reference Type: BACKGROUND

PMID: 40110217 (View on PubMed)

Bagai J, Brilakis ES. Update in the Management of Acute Coronary Syndrome Patients with Cardiogenic Shock. Curr Cardiol Rep. 2019 Mar 4;21(4):17. doi: 10.1007/s11886-019-1102-3.

Reference Type: BACKGROUND

PMID: 30828750 (View on PubMed)

De Luca L, Olivari Z, Farina A, Gonzini L, Lucci D, Di Chiara A, Casella G, Chiarella F, Boccanelli A, Di Pasquale G, De Servi S, Bovenzi FM, Gulizia MM, Savonitto S. Temporal trends in the epidemiology, management, and outcome of patients with cardiogenic shock complicating acute coronary syndromes. Eur J Heart Fail. 2015 Nov;17(11):1124-32. doi: 10.1002/ejhf.339. Epub 2015 Sep 4.

Reference Type: BACKGROUND

PMID: 26339723 (View on PubMed)

Thiele H, Zeymer U. Cardiogenic shock in patients with acute coronary syndromes. In: Tubaro M, Vranckx P, Price S, Vrints C, eds. The ESC Textbook of Intensive and Acute Cardiovascular Care: Oxford University Press; 2015: 0.

Reference Type: BACKGROUND

Other Identifiers

012025

Identifier Type: -

Identifier Source: org_study_id