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)

NCT07090382 | Enrolling By Invitation

• 1 other identifier: 012025
• Study Type: observational
• Target: 1,046
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Keywords

Cardiogenic shock; Acute coronary syndrome; Risk prediction; Clinical decision support; Emergency medicine; Coronary care unit; Shock prevention; Prognostic score; Cardiovascular disease; Predictive modeling; Early warning system; Intensive care; Mortality risk; Prospective validation

Outcome Measures

Primary Outcomes (1)

• Discriminatory Power of the STOPSHOCK Score for Predicting Cardiogenic Shock

  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.

  Up to hospital discharge (average of 14 days)

Secondary Outcomes (11)

• Sensitivity (Recall) of the STOPSHOCK Score

  Up to hospital discharge (average of 14 days)

• Specificity of the STOPSHOCK Score

  Up to hospital discharge (average of 14 days)

• Positive Predictive Value (Precision)

  Up to hospital discharge (average of 14 days)

• Negative Predictive Value

  Up to hospital discharge (average of 14 days)

• F1 Score of the STOPSHOCK Score

  Up to hospital discharge (average of 14 days)

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients aged \> 18 years, admitted for ACS.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Premedix Academy: lead

Study Sites (1)

Premedix Academy

Bratislava, 81102, Slovakia

Location

Related Publications (8)

• 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.

  PMID: 25560730 BACKGROUND

• 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.

  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.

  BACKGROUND

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

  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.

  PMID: 40110217 BACKGROUND

• 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.

  PMID: 30828750 BACKGROUND

• 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.

  PMID: 26339723 BACKGROUND

• 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.

  BACKGROUND

MeSH Terms

Conditions

Shock, Cardiogenic; Acute Coronary Syndrome; Cardiovascular Diseases

Condition Hierarchy (Ancestors)

Myocardial Infarction; Myocardial Ischemia; Heart Diseases; Vascular Diseases; Infarction; Ischemia; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Necrosis; Shock

Study Officials

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

  Premedix Academy

  PRINCIPAL INVESTIGATOR

• Branislav Bezák, MD, PhD

  Premedix Academy

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 18, 2025
• First Posted: July 29, 2025
• Study Start: June 1, 2025
• Primary Completion: December 31, 2025
• Study Completion: April 30, 2026
• Last Updated: July 29, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

SL (1)