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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 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 1046

Last updated 2025-07-29

No results posted yet for this study

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

Sponsors & Collaborators

  • Premedix Academy

    lead OTHER

Principal Investigators

  • Allan Böhm, MD, PhD, MSc, MBA, FESC, FJCS · Premedix Academy

  • Branislav Bezák, MD, PhD · Premedix Academy

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-01
Primary Completion
2025-12-31
Completion
2026-04-30

Countries

  • Slovakia

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07090382 on ClinicalTrials.gov