An observational study in Cardiogenic Shock, Mechanical Circulatory Support and Cardiogenic Shock Post Myocardial Infarction, sponsored by Medical University of Vienna. Recruiting at 1 site in Austria. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-06-01.
Sponsored by Medical University of Vienna · Observational
The main goal of this observational, study is to develop a clinical decision support tool utilizing Impella 5.5 pump parameters to predict native heart recovery and prevent adverse events, by leveraging data science and real-world clinical data of cardiogenic shock patients.
Therefore, secondary objectives are essential to consolidating a retrospective longitudinal analysis of Impella 5.5 pump data alongside ICU digital health record datasets to:
The clinical management of patients experiencing severe cardiogenic shock requires precise, real-time monitoring to optimize hemodynamic support and guide therapeutic transitions. The Impella 5.5 micro-axial flow pump provides left ventricular unloading, generating automated internal continuous parameters that reflect moving cardiac states. This study establishes a retrospective, longitudinal framework that integrates these high-frequency device metrics with corresponding clinical data housed within intensive care unit (ICU) digital health records (DHR). By synthesizing these disparate data streams, this research aims to build an advanced analytical framework to support clinical decisions in the cardiogenic shock landscape.
Signal Validation and Data Preprocessing:
The initial phase of the study validates the physiological fidelity of the continuous data stream. High-frequency digital logs generated by the pump console-specifically the optical placement signal-will undergo time-series alignment with standard physiological waveforms recorded in the ICU, using indwelling arterial line pressure data as the reference standard. This signal validation ensures that the longitudinal parameter data accurately capture mechanical positioning and true left ventricular dynamics prior to entering the downstream modeling pipeline.
Analytical Framework and Modeling Strategy:
Following data integration and signal validation, the consolidated dataset will be leveraged to develop predictive models aimed at distinguishing patient trajectories and forecasting complications. The computational pipeline is divided into two primary analytical pathways:
Endpoint Classification:
An artificial neural network will be developed to evaluate patient trajectories toward distinct clinical endpoints: native heart recovery, escalation to heart replacement therapy, or death. The modeling pipeline incorporates a rigorous framework to ensure generalizability and guard against overfitting. The complete dataset will be partitioned into an 80% development subset and a 20% independent testing subset. The development subset will undergo 5-fold cross-validation to drive comprehensive model architecture optimization, systematically testing structural variations to identify the highest-performing network configuration.
Adverse Event Forecasting:
Separate statistical and machine learning architectures will be constructed to evaluate risk patterns and clinical scenarios associated with severe on-device complications, specifically clinical hemolysis, new-onset arrhythmias, and hemocompatibility-related adverse events (HRAEs). These models focus on identifying early-warning clusters within the high-frequency pump log data to identify sub-clinical changes before manifest physiological degradation occurs.
Through these combined pathways, this observational study seeks to lay the foundational algorithmic groundwork for a real-time clinical decision support tool utilizing objective, automated device analytics to improve safety and personalization in mechanical circulatory support.
277 studies on the registry are indexed under Shock, Cardiogenic; 127 are open to participants now.
This study's planned enrollment of 100 is below the median of 200 across 142 observational studies indexed under Shock, Cardiogenic.
Browse Shock, Cardiogenic studies →Medical University of Vienna is the lead sponsor of 1,076 studies on the registry; 177 are open to participants now.
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The study population comprises adult patients admitted to the intensive care unit (ICU) presenting with severe cardiogenic shock who required temporary mechanical circulatory support between January 2019 and March 2026, treated at the Medical University of Vienna, Austria. Eligible individuals are those who undergo clinical management utilizing the Impella 5.5 micro-axial flow pump as part of their standard of care. Only patients with available high-resolution pump data (downloaded from the clinical console) and ICU digital health record datasets extracted from ICCA (IntelliSpace Critical Care and Anesthesia, Philips Medical Systems Development and Manufacturing Centre, Hamburg, Germany) will be included for analysis.
Exclusion Criteria:
Patients supported with the Impella 5.5 device (J\&J MedTech) achieving native heart recovery
Device: Micro-axial flow pump support
Patients supported with the Impella 5.5 device (J\&J MedTech) transitioning to heart replacement therapy (durable mechanical circulatory support or heart transplantation
Device: Micro-axial flow pump support
Patients supported with the Impella 5.5 device (J\&J MedTech) suffering from all-cause mortality during mechanical circulatory support.
Device: Micro-axial flow pump support
Temporary circulatory support using the Impella 5.5 micro-axial flow pump. The device is surgically placed (typically via the axillary artery) across the aortic valve into the left ventricle to provide active forward flow, unloading the left ventricle and maintaining systemic perfusion during cardiogenic shock. Management of the device includes the collection and analysis of continuous device-derived hemodynamic data and associated clinical parameters throughout the duration of support.
Predictive Performance and Optimization of the Clinician Decision Support Tool
The predictive performance of the developed clinical decision support tool will be evaluated by its ability to classify patient trajectories toward native heart recovery versus adverse outcomes. The model will undergo comprehensive model architecture optimization to identify the structural configuration that yields optimal performance. Model training, validation, and testing will follow a standard data split: 80% of the dataset will be utilized for training and internal validation using 5-fold cross-validation, and the remaining 20% will be held out as a definitive testing dataset. Final classification performance will be quantified using specific metrics derived from Receiver Operating Characteristic (ROC) curve analysis, including: Area Under the Curve (AUC), Sensitivity, Specificity
Time frame: From the time of Impella 5.5 insertion up to device explant (estimated average of 5 to 14 days).
Bias Between the Impella 5.5 Placement Signal and ICU Arterial Line Waveforms
The systematic difference (bias) between the automated Impella 5.5 optical placement signal and the gold-standard ICU indwelling arterial line pressure waveforms will be quantified using Bland-Altman analysis to evaluate signal alignment. Unit of Measure: Millimeters of mercury (mmHg)
Time frame: Continuously through the duration of active Impella 5.5 device support (from device insertion up to explant, estimated average of 5 to 14 days).
Correlation Coefficient Between the Impella 5.5 Placement Signal and ICU Arterial Line Waveforms
The strength and direction of the linear relationship between the continuous time-series data of the Impella 5.5 optical placement signal and the ICU arterial line waveforms will be evaluated. Unit of Measure: Correlation coefficient (r) on a scale from -1.0 to 1.0.
Time frame: Continuously through the duration of active Impella 5.5 device support (from device insertion up to explant, estimated average of 5 to 14 days).
Concordance Rate of Directional Trends Between the Impella 5.5 Placement Signal and ICU Arterial Line Waveforms
The trending ability of the placement signal will be assessed via concordance plots. The concordance rate represents the percentage of data points where the directional change (increase or decrease) matches between both signal streams over time, excluding zones of clinical noise. Unit of Measure: Percentage (%) of concordant data pairs.
Time frame: Continuously through the duration of active Impella 5.5 device support (from device insertion up to explant, estimated average of 5 to 14 days).
Percentage of Participants Exhibiting Specific Device-Support Clinical Endpoints
The final clinical trajectory of the cohort on device support will be categorized into one of three mutually exclusive outcomes: 1. Native heart recovery (successful device weaning and explant) 2. Escalation to heart replacement therapy (durable LVAD implantation or heart transplantation) 3. All-cause mortality while on active device support. Unit of Measure: Percentage (%) of participants within each categorical outcome category.
Time frame: Through the duration of hospital stay (estimated average of 30 days).
Percentage of Participants Experiencing Device-Related Hemolysis
The incidence of clinically significant hemolysis on device support, defined by standard laboratory criteria (e.g., plasma free hemoglobin greater than 40 mg/dL or a doubling of lactate dehydrogenase alongside clinical signs). Unit of Measure: Percentage (%) of participants.
Time frame: From device insertion up to 30 days post-explant or hospital discharge, whichever occurs first.
Percentage of Participants Experiencing New-Onset Clinically Significant Arrhythmias
The incidence of new-onset atrial or ventricular arrhythmias occurring during device support that require immediate pharmacological, electrical, or device-setting intervention. Unit of Measure: Percentage (%) of participants.
Time frame: From device insertion up to 30 days post-explant or hospital discharge, whichever occurs first.
Number of Hemocompatibility-Related Adverse Events (HRAEs) Per Participant
The rate of hemocompatibility-related adverse events, including major bleeding episodes (requiring transfusion or reoperation) and thromboembolic events (e.g., ischemic stroke, peripheral arterial embolization) occurring during device support. Unit of Measure: Number of events per participant.
Time frame: From device insertion up to 30 days post-explant or hospital discharge, whichever occurs first.
Plan to share: No — IPD will not be shared because the dataset contains highly sensitive, high-frequency physiological data linked with intensive care health records. Further, IPD is currently not planned for public sharing as the dataset is being actively used to develop a proprietary clinical decision support tool and model architecture optimization.
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