Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
50000 participants
OBSERVATIONAL
2022-10-31
2023-12-31
Brief Summary
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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.
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Detailed Description
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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.
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 0.844 surpassing other externally validated cardiogenic shock (CS) models (e.g. 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.
Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Study group (with CS)
A population of patients acquired from multiple national registries aged \> 18 years, admitted for ACS without CS and proceeding to coronary angiography who developed cardiogenic shock (CS).
No interventions assigned to this group
Control group (without CS)
A population of patients acquired from multiple national registries aged \> 18 years, admitted for ACS without CS and proceeding to coronary angiography who did not develop cardiogenic shock (CS).
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* 41000 - 41092: various versions of Acute myocardial infarction
* 41189: Other acute and subacute forms of ischemic heart disease, other
* 4139: Other and unspecified angina pectoris
2. Patients with at least one ICD9 diagnosis code:
* 78551: Cardiogenic shock
* 78550: Shock, unspecified
3. Patients with at least one ICD9 procedures codes:
* 0066: Percutaneous transluminal coronary angioplasty \[PTCA\]
* 3604: Intracoronary artery thrombolytic infusion
* 3606: Insertion of non-drug-eluting coronary artery stent(s)
* 3607: Insertion of drug-eluting coronary artery stent(s)
* 3609: Other removal of coronary artery obstruction
* 8855: Coronary arteriography using a single catheter
* 8856: Coronary arteriography using two catheters
* 8857: Other and unspecified coronary arteriography
* 3722: Left heart cardiac catheterization
* 3723: Combined right and left heart cardiac catheterization
1. Patients with at least one ICD9 diagnosis code:
* 41000 - 41092: various versions of Acute myocardial infarction
* 41189: Other acute and subacute forms of ischemic heart disease, other
* 4139: Other and unspecified angina pectoris
2. Patients with at least one of the ICD9 procedures codes:
* 0066: Percutaneous transluminal coronary angioplasty \[PTCA\]
* 3604: Intracoronary artery thrombolytic infusion
* 3606: Insertion of non-drug-eluting coronary artery stent(s)
* 3607: Insertion of drug-eluting coronary artery stent(s)
* 3609: Other removal of coronary artery obstruction
* 8855: Coronary arteriography using a single catheter
* 8856: Coronary arteriography using two catheters
* 8857: Other and unspecified coronary arteriography
* 3722: Left heart cardiac catheterization
* 3723: Combined right and left heart cardiac catheterization
Exclusion Criteria
* 78551: Cardiogenic shock
* 78550: Shock, unspecified
18 Years
ALL
No
Sponsors
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Premedix Academy
OTHER
Responsible Party
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Principal Investigators
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Allan Böhm, MD, PhD, MBA, FESC, FJCS
Role: PRINCIPAL_INVESTIGATOR
Premedix Academy, Medena 18, 81102 Bratislava, Slovakia
Locations
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Premedix Academy
Bratislava, , Slovakia
Countries
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Related Links
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STOP SHOCK website
Other Identifiers
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0012022
Identifier Type: -
Identifier Source: org_study_id
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