Cardiogenic ShockSTOPSCHOCKNCT07090382
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)
An observational study of Cardiogenic Shock and Cardiogenic Shock Acute, run by Premedix Academy at 1 site in Slovakia.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiogenic Shock, Cardiogenic Shock Acute, Cardiogenic Shock Post Myocardial Infarction
- How many people
- 1,046 participants
- Where
- 1 siteSlovakia
- Study dates
- 1 Jun 2025to 30 Apr 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: Patients aged > 18 years, admitted for ACS.
Who may be able to join
- Patients aged >18 years.
- Admitted for acute coronary syndrome in CCU
Who cannot join
- Patients aged < 18 years.
- Patients in CSWG-SCAI C, D or E CS the before the admission to CCU.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
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.
Read the detailed description
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. For this purpose, Premedix Academy has developed and validated a predictive scoring system STOP SHOCK (Score TO Predict SHOCK). This scoring system showed better prediction compared to standard models and was accepted to the Late- Breaking Science section at the European Society of Cardiology (ESC) Congress 2024. STOP SHOCK was validated on an external cohort of 5123 ACS patients with area under the receiver operating characteristic curve (ROC AUC) of 0.844 (95% confidence interval: 0.841-0.8470) surpassing other externally validated cardiogenic shock (CS) models (e.g. ORBI score). Furthermore, our 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. Novelty of our project is also in the concept of continuous training, improvement, and validation to ensure validity and clinical applicability in the future as well. Current medical models are developed, verified, and published. Once the model enters medical practice, research teams will either validate it or replace it with their own model based on a new cohort of patients. However, experience from other fields shows that as soon as machine learning models are deployed, their performance degrades. In order to preserve and even further improve the model, continuous performance monitoring and training/retraining are vital. A small prospective validation study on a cohort of 103 consecutive higher-risk ACS patients, enrolled in intensive cardiac care units in 8 centers from USA, Europe, and Asia demonstrated very good performance with ROC AUC of 0.97 and was presented at the 2023 American Heart Association Annual Meeting. The STOPSHOCK score is currently available as a smartphone application and as an online calculator: https://stopshock.org.
The primary objective of this study is to prospectively validate the STOPSHOCK score on a large cohort of ACS patients. The methods and results of this project follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.
- Conditions
- Cardiogenic Shock, Cardiogenic Shock Acute, Cardiogenic Shock Post Myocardial Infarction, Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI), PCI
- Keywords
- Cardiogenic shock, Acute coronary syndrome, Risk prediction, Clinical decision support, Emergency medicine, Coronary care unit, Shock prevention, Prognostic score, Cardiovascular disease, Predictive modeling, Early warning system, Intensive care, Mortality risk, Prospective validation
- Medical subject headings
- Shock, Cardiogenic, Acute Coronary Syndrome, Cardiovascular Diseases
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 |
|---|---|---|
| ACS Patients Admitted to CCU | This cohort includes adult patients (age >18 years) admitted to the coronary care unit (CCU) or intensive care unit (ICU) with a diagnosis of acute coronary syndrome (ACS), including STEMI, NSTEMI, and unstable angina. Patients are enrolled at the time of admission before the development of cardiogenic shock. The STOPSHOCK score is calculated using clinical variables available at first contact. Patients are followed during hospitalization to determine whether cardiogenic shock develops. No intervention is applied. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Discriminatory Power of the STOPSHOCK Score for Predicting Cardiogenic Shock
The primary outcome is the ability of the STOPSHOCK score to predict the development of cardiogenic shock in patients admitted with acute coronary syndrome. This will be assessed using the area under the receiver operating characteristic curve (ROC AUC), comparing the predicted risk score to the actual occurrence of cardiogenic shock during hospitalization.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Secondary outcomes (11)
Sensitivity (Recall) of the STOPSHOCK Score
The proportion of patients who develop cardiogenic shock during hospitalization and were correctly identified as high-risk by the STOPSHOCK score at admission.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Specificity of the STOPSHOCK Score
The proportion of patients who do not develop cardiogenic shock and were correctly identified as low-risk by the STOPSHOCK score.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Positive Predictive Value (Precision)
he proportion of patients identified as high-risk by the STOPSHOCK score who actually develop cardiogenic shock during hospitalization.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Negative Predictive Value
The proportion of patients identified as low-risk by the STOPSHOCK score who do not develop cardiogenic shock.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
F1 Score of the STOPSHOCK Score
The harmonic mean of precision and sensitivity, providing a balanced metric that accounts for both false positives and false negatives.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Accuracy of the STOPSHOCK Score
Proportion of all predictions (true positives and true negatives) that are correctly classified by the STOPSHOCK score.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Area Under the Precision-Recall Curve (PR AUC)
Metric summarizing the trade-off between precision and recall for the STOPSHOCK score, especially relevant given the low incidence of cardiogenic shock.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Matthews Correlation Coefficient
Balanced performance metric accounting for true and false positives and negatives, especially useful in imbalanced datasets such as cardiogenic shock prediction.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Youden's J Statistic
Metric calculated as Sensitivity + Specificity - 1, representing the optimal threshold for distinguishing between high- and low-risk patients.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Brier Score
Mean squared difference between predicted probabilities and actual outcomes of cardiogenic shock, assessing the calibration and accuracy of the STOPSHOCK score.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Calibration Slope and Intercept
Metrics used to assess how well the STOPSHOCK score's predicted probabilities align with observed cardiogenic shock outcomes; slope = 1 and intercept = 0 indicate perfect calibration.
STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
Measured: Up to hospital discharge (average of 14 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2025
- Study start1 Jun 2025
- Primary completion31 Dec 2025 (estimated)
- Study completion30 Apr 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- 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
- Prospective
- Enrolment
- 1,046 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Premedix Academy
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Allan Böhm, MD, PhD, MSc, MBA, FESC, FJCS, Principal investigator, Premedix AcademyBranislav Bezák, MD, PhD, Study director, Premedix Academy
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07090382
- Sponsor's protocol ID
- 012025
- Acronym
- STOPSCHOCK
- Status last verified
- Jul 2025
- First submitted
- 18 Jul 2025
- Last updated
- 29 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.
PubMed 25560730Böhm A, Jajcay N, Spartalis M, et al. Abstract 14290: Prospective Clinical Validation of the STOPSHOCK Smartphone Application - Artificial Intelligence Model for Prediction of Cardiogenic Shock in Patients With Acute Coronary Syndrome. Circulation 2023; 148.
Tran V, Pham H, Yang B-S, Nguyen T. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing 2012; 32: 320-30.
Grohmann J, Nicholson P, Iglesias J, Kounev S, Lugones D. Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning; 2019.
Bohm A, Segev A, Jajcay N, Krychtiuk KA, Tavazzi G, Spartalis M, Kollarova M, Berta I, Jankova J, Guerra F, Pogran E, Remak A, Jarakovic M, Sebenova Jerigova V, Petrikova K, Matetzky S, Skurk C, Huber K, Bezak B. Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome. Eur Heart J Digit Health. 2025 Jan 6;6(2):240-251. doi: 10.1093/ehjdh/ztaf002. eCollection 2025 Mar.
PubMed 40110217Bagai J, Brilakis ES. Update in the Management of Acute Coronary Syndrome Patients with Cardiogenic Shock. Curr Cardiol Rep. 2019 Mar 4;21(4):17. doi: 10.1007/s11886-019-1102-3.
PubMed 30828750De Luca L, Olivari Z, Farina A, Gonzini L, Lucci D, Di Chiara A, Casella G, Chiarella F, Boccanelli A, Di Pasquale G, De Servi S, Bovenzi FM, Gulizia MM, Savonitto S. Temporal trends in the epidemiology, management, and outcome of patients with cardiogenic shock complicating acute coronary syndromes. Eur J Heart Fail. 2015 Nov;17(11):1124-32. doi: 10.1002/ejhf.339. Epub 2015 Sep 4.
PubMed 26339723Thiele H, Zeymer U. Cardiogenic shock in patients with acute coronary syndromes. In: Tubaro M, Vranckx P, Price S, Vrints C, eds. The ESC Textbook of Intensive and Acute Cardiovascular Care: Oxford University Press; 2015: 0.
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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)
- ClinicalTrials.gov ID
- NCT07090382
- Acronym / sponsor ID
- STOPSCHOCK / 012025
- Conditions
- Cardiogenic Shock, Cardiogenic Shock Acute, Cardiogenic Shock Post Myocardial Infarction, Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI), PCI
- Keywords
- Cardiogenic shock, Acute coronary syndrome, Risk prediction, Clinical decision support, Emergency medicine, Coronary care unit, Shock prevention, Prognostic score
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 1,046 participants (estimated)
- Lead sponsor
- Premedix AcademyUniversity, hospital or other
- Study start
- 1 Jun 2025 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 30 Apr 2026 (estimated)
- First posted
- 29 Jul 2025 (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.
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.
Detailed description
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. For this purpose, Premedix Academy has developed and validated a predictive scoring system STOP SHOCK (Score TO Predict SHOCK). This scoring system showed better prediction compared to standard models and was accepted to the Late- Breaking Science section at the European Society of Cardiology (ESC) Congress 2024. STOP SHOCK was validated on an external cohort of 5123 ACS patients with area under the receiver operating characteristic curve (ROC AUC) of 0.844 (95% confidence interval: 0.841-0.8470) surpassing other externally validated cardiogenic shock (CS) models (e.g. ORBI score). Furthermore, our 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. Novelty of our project is also in the concept of continuous training, improvement, and validation to ensure validity and clinical applicability in the future as well. Current medical models are developed, verified, and published. Once the model enters medical practice, research teams will either validate it or replace it with their own model based on a new cohort of patients. However, experience from other fields shows that as soon as machine learning models are deployed, their performance degrades. In order to preserve and even further improve the model, continuous performance monitoring and training/retraining are vital. A small prospective validation study on a cohort of 103 consecutive higher-risk ACS patients, enrolled in intensive cardiac care units in 8 centers from USA, Europe, and Asia demonstrated very good performance with ROC AUC of 0.97 and was presented at the 2023 American Heart Association Annual Meeting. The STOPSHOCK score is currently available as a smartphone application and as an online calculator: https://stopshock.org.
The primary objective of this study is to prospectively validate the STOPSHOCK score on a large cohort of ACS patients. The methods and results of this project follow the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| ACS Patients Admitted to CCU | – | –This cohort includes adult patients (age >18 years) admitted to the coronary care unit (CCU) or intensive care unit (ICU) with a diagnosis of acute coronary syndrome (ACS), including STEMI, NSTEMI, and unstable angina. Patients are enrolled at the time of admission before the development of cardiogenic shock. The STOPSHOCK score is calculated using clinical variables available at first contact. Patients are followed during hospitalization to determine whether cardiogenic shock develops. No intervention is applied. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged > 18 years, admitted for ACS.
Key inclusion criteria (2 listed)
- Patients aged >18 years.
- Admitted for acute coronary syndrome in CCU
Key exclusion criteria (2 listed)
- Patients aged < 18 years.
- Patients in CSWG-SCAI C, D or E CS the before the admission to CCU.
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
Discriminatory Power of the STOPSHOCK Score for Predicting Cardiogenic Shock
Time frame: Up to hospital discharge (average of 14 days)
The primary outcome is the ability of the STOPSHOCK score to predict the development of cardiogenic shock in patients admitted with acute coronary syndrome. This will be assessed using the area under the receiver operating characteristic curve (ROC AUC), comparing the predicted risk score to the actual occurrence of cardiogenic shock during hospitalization. STOPSHOCK Score (0-100%): This score estimates the risk of developing cardiogenic shock during hospitalization. The higher the percentage, the higher the predicted risk. Higher scores indicate a worse outcome.
11 secondary outcomes
Sensitivity (Recall) of the STOPSHOCK Score
Time frame: Up to hospital discharge (average of 14 days)
Specificity of the STOPSHOCK Score
Time frame: Up to hospital discharge (average of 14 days)
Positive Predictive Value (Precision)
Time frame: Up to hospital discharge (average of 14 days)
Negative Predictive Value
Time frame: Up to hospital discharge (average of 14 days)
F1 Score of the STOPSHOCK Score
Time frame: Up to hospital discharge (average of 14 days)
Accuracy of the STOPSHOCK Score
Time frame: Up to hospital discharge (average of 14 days)
Area Under the Precision-Recall Curve (PR AUC)
Time frame: Up to hospital discharge (average of 14 days)
Matthews Correlation Coefficient
Time frame: Up to hospital discharge (average of 14 days)
Youden's J Statistic
Time frame: Up to hospital discharge (average of 14 days)
Brier Score
Time frame: Up to hospital discharge (average of 14 days)
Calibration Slope and Intercept
Time frame: Up to hospital discharge (average of 14 days)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Jul 2025.
- Background: PubMed 25560730Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015 Jan 6;162(1):W1-73. doi: 10.7326/M14-0698.
- BackgroundBöhm A, Jajcay N, Spartalis M, et al. Abstract 14290: Prospective Clinical Validation of the STOPSHOCK Smartphone Application - Artificial Intelligence Model for Prediction of Cardiogenic Shock in Patients With Acute Coronary Syndrome. Circulation 2023; 148.
- BackgroundTran V, Pham H, Yang B-S, Nguyen T. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine. Mechanical Systems and Signal Processing 2012; 32: 320-30.
- BackgroundGrohmann J, Nicholson P, Iglesias J, Kounev S, Lugones D. Monitorless: Predicting Performance Degradation in Cloud Applications with Machine Learning; 2019.
- Background: PubMed 40110217Bohm A, Segev A, Jajcay N, Krychtiuk KA, Tavazzi G, Spartalis M, Kollarova M, Berta I, Jankova J, Guerra F, Pogran E, Remak A, Jarakovic M, Sebenova Jerigova V, Petrikova K, Matetzky S, Skurk C, Huber K, Bezak B. Machine learning-based scoring system to predict cardiogenic shock in acute coronary syndrome. Eur Heart J Digit Health. 2025 Jan 6;6(2):240-251. doi: 10.1093/ehjdh/ztaf002. eCollection 2025 Mar.
- Background: PubMed 30828750Bagai J, Brilakis ES. Update in the Management of Acute Coronary Syndrome Patients with Cardiogenic Shock. Curr Cardiol Rep. 2019 Mar 4;21(4):17. doi: 10.1007/s11886-019-1102-3.
- Background: PubMed 26339723De Luca L, Olivari Z, Farina A, Gonzini L, Lucci D, Di Chiara A, Casella G, Chiarella F, Boccanelli A, Di Pasquale G, De Servi S, Bovenzi FM, Gulizia MM, Savonitto S. Temporal trends in the epidemiology, management, and outcome of patients with cardiogenic shock complicating acute coronary syndromes. Eur J Heart Fail. 2015 Nov;17(11):1124-32. doi: 10.1002/ejhf.339. Epub 2015 Sep 4.
- BackgroundThiele H, Zeymer U. Cardiogenic shock in patients with acute coronary syndromes. In: Tubaro M, Vranckx P, Price S, Vrints C, eds. The ESC Textbook of Intensive and Acute Cardiovascular Care: Oxford University Press; 2015: 0.
- Individual participant dataSharing planned: No
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Cardiogenic Shock
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.
Allan Böhm, MD, PhD, MSc, MBA, FESC, FJCS principal investigator
Premedix Academy
Branislav Bezák, MD, PhD study director
Premedix Academy
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 Cardiogenic Shock Acute, 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 2 inclusion and 2 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: enrolling by invitation, as of the sponsor's last update on 29 Jul 2025. 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?
This study is still under way. Results are not available in the source record yet.
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?
Discriminatory Power of the STOPSHOCK Score for Predicting Cardiogenic Shock (time frame: Up to hospital discharge (average of 14 days)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07090382, which also lists 8 linked publications.
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 29 Jul 2025.