Ignite Creation Date:
2024-05-06 @ 6:11 PM
Last Modification Date:
2024-10-26 @ 2:43 PM
Study NCT ID:
NCT05570864
Status:
UNKNOWN
Last Update Posted:
2022-10-07
First Post:
2022-10-04
Brief Title:
Score TO Predict SHOCK - STOP SHOCK
Sponsor:
Premedix Academy
Organization:
Premedix Academy
Study Overview
Official Title:
Artificial Intelligence Based Predictive Scoring System to Identify the Risk of Developing Cardiogenic Shock CS in Patients Suffering From Acute Coronary Syndrome ACS
Status:
UNKNOWN
Status Verified Date:
2022-10
Last Known Status:
ENROLLING_BY_INVITATION
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 patients 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
Study hypothesis
A complex machine learning ML model utilising standard patients 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
Detailed Description:
Background
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 andor 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
Rationale
The AI-based scoring system could aid in identifying high-risk patients prior to the development of cardiogenic shock This would allow taking pre-emptive measures implanting mechanical circulatory support and thus prevent the development of shock leading to improved survival
For this purpose a predictive scoring system STOP SHOCK Score TO Predict SHOCK was developed This scoring system showed better prediction compared to standard models STOP SHOCK was validated on an external cohort of patients with area under the curve AUC of 0844 surpassing other externally validated cardiogenic shock CS models eg ORBI score Furthermore this 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
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 andor 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
Rationale
The AI-based scoring system could aid in identifying high-risk patients prior to the development of cardiogenic shock This would allow taking pre-emptive measures implanting mechanical circulatory support and thus prevent the development of shock leading to improved survival
For this purpose a predictive scoring system STOP SHOCK Score TO Predict SHOCK was developed This scoring system showed better prediction compared to standard models STOP SHOCK was validated on an external cohort of patients with area under the curve AUC of 0844 surpassing other externally validated cardiogenic shock CS models eg ORBI score Furthermore this 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None