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Score TO Predict SHOCK - STOP SHOCK

NCT05570864 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 50000

Last updated 2022-10-07

No results posted yet for this study

Summary

The goal of this international multicenter study is to develop a scoring system to identify the risk of developing cardiogenic shock (CS) in patients suffering from acute coronary syndrome (ACS) utilising artificial intelligence.

Study hypothesis:

A complex machine learning (ML) model utilising standard patient's admission data predicts the development of cardiogenic shock in patients suffering from acute myocardial infarction better than standard prediction models.

Study objectives:

The primary objective of this study is to further improve predictive parameters of #STOPSHOCK model for prediction of development of cardiogenic shock in patients suffering from acute myocardial infarction.

The secondary objective of this study is to develop a new predictive model for the development of cardiogenic shock in patients suffering from acute myocardial infarction based on larger combined cohort of patients utilising advanced ML algorithms, continuous model performance monitoring and continual learning.

Conditions

  • Cardiogenic Shock
  • Acute Coronary Syndrome
  • Acute Myocardial Infarction

Sponsors & Collaborators

  • Premedix Academy

    lead OTHER

Principal Investigators

  • Allan Böhm, MD, PhD, MBA, FESC, FJCS · Premedix Academy, Medena 18, 81102 Bratislava, Slovakia

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-10-31
Primary Completion
2023-11-30
Completion
2023-12-31

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 NCT05570864 on ClinicalTrials.gov