Cardiogenic ShockNCT05570864
Score TO Predict SHOCK - STOP SHOCK
An observational study of Cardiogenic Shock and Acute Coronary Syndrome, run by Premedix Academy at 1 site in Slovakia.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiogenic Shock, Acute Coronary Syndrome, Acute Myocardial Infarction
- How many people
- 50,000 participants
- Where
- 1 siteSlovakia
- Study dates
- Oct 2022to Dec 2023
- How long will I take part?
- 30 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study will include a combined population of patients who suffered acute myocardial infarction recorded in national registries from several countries. An estimated 30 to 50000 new patients will be added from multiple registries to to improve the existing primary STOP SHOCK model. The incidence of cardiogenic shock in this group of patients is estimated to be 10% which would constitute the shock patient group, the rest of the patients will be included in the control group.
Who may be able to join
- 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
- Patients with at least one ICD9 diagnosis code:
- 78551: Cardiogenic shock
Show all 33Show fewer
- 78550: Shock, unspecified
- 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
- 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
- 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
Who cannot join
- 1. Patients without ICD9 diagnosis codes:
- 78551: Cardiogenic shock
- 78550: Shock, unspecified
Trial locations and contacts
1 site in 1 country.
About this study
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)
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:
Read more
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.
Read the 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 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.
- Keywords
- cardiogenic shock, acute coronary syndrome, artificial intelligence, scoring system, machine learning, acute myocardial infarction
- Medical subject headings
- Shock, Cardiogenic, Acute Coronary Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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). | |
| 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). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Development of cardiogenic shock
Development of cardiogenic shock (CS) in patients suffering from acute coronary syndrome (ACS)
Measured: Up to 72 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Oct 2022
- Study startOct 2022 (estimated)
- Primary completionNov 2023 (estimated)
- Study completionDec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiogenic ShockStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 50,000 participants (estimated)
- Target duration
- 30 Days
Sponsor and investigators
- Lead sponsor
- Premedix Academy
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Allan Böhm, MD, PhD, MBA, FESC, FJCS, Principal investigator, Premedix Academy, Medena 18, 81102 Bratislava, Slovakia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05570864
- Sponsor's protocol ID
- 0012022
- Status last verified
- Oct 2022
- First submitted
- 4 Oct 2022
- Last updated
- 7 Oct 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Oct 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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)
- ClinicalTrials.gov ID
- NCT05570864
- Acronym / sponsor ID
- 0012022
- Keywords
- cardiogenic shock, acute coronary syndrome, artificial intelligence, scoring system, machine learning, acute myocardial infarction
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 50,000 participants (estimated)
- Lead sponsor
- Premedix AcademyUniversity, hospital or other
- Study start
- Oct 2022 (estimated)
- Primary completion
- Nov 2023 (estimated)
- Study completion
- Dec 2023 (estimated)
- First posted
- 7 Oct 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 30 Days
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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). |
| 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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study will include a combined population of patients who suffered acute myocardial infarction recorded in national registries from several countries. An estimated 30 to 50000 new patients will be added from multiple registries to to improve the existing primary STOP SHOCK model. The incidence of cardiogenic shock in this group of patients is estimated to be 10% which would constitute the shock patient group, the rest of the patients will be included in the control group.
Key inclusion criteria (33 listed)
- Patients with at least one ICD9 diagnosis code:
- Patients with at least one ICD9 diagnosis code:
- Patients with at least one ICD9 procedures codes:
- Patients with at least one ICD9 diagnosis code:
- Patients with at least one of the ICD9 procedures codes:
Key exclusion criteria (3 listed)
- 78551: Cardiogenic shock
- 78550: Shock, unspecified
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Development of cardiogenic shock
Time frame: Up to 72 hours
Development of cardiogenic shock (CS) in patients suffering from acute coronary syndrome (ACS)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Oct 2022.
- STOP SHOCK websiteLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Allan Böhm, MD, PhD, MBA, FESC, FJCS principal investigator
Premedix Academy, Medena 18, 81102 Bratislava, Slovakia
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiogenic Shock and Acute Coronary Syndrome, run by Premedix Academy at 1 site in Slovakia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 33 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Oct 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Slovakia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of cardiogenic shock (time frame: Up to 72 hours). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Oct 2022.