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Development of Algorithms to Predict Hemodynamic Instability

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT01262508
Recruitment Status : Completed
First Posted : December 17, 2010
Last Update Posted : February 19, 2016
Information provided by (Responsible Party):
Klinik für Kardiologie, Pneumologie und Angiologie, Heinrich-Heine University, Duesseldorf

Brief Summary:
Hemodynamic monitoring in hospitalized patients is crucial since in clinical practice unexpected deterioration of cardiovascular function remains a serious problem and an important cause of death. Novel perspectives in reflex testing of the autonomic nervous system might be useful to protect some patients from cardiovascular events by detecting cardiovascular deteriorations. In addition, standard pulse oximetry in low acuity settings is nowadays predominately used to monitor peripheral oxygen saturation. Of note, there is evidence that additional analyses of pulse wave characteristics might be a valuable source of information to generate additional insights into the cardiorespiratory status of the patient. Herein, we aim to develop novel algorithms in order to protect in-hospital patients from cardiovascular events in consequence of hemodynamic instability in the future.

Condition or disease
Sudden Cardiac Death

Detailed Description:

70 datasets from hospitalized patients will be acquired in order to characterize the functional status of the autonomic nervous system as well as hemodynamics during baseline and during standard procedures including physical exercise testing and head-up tilt table testing.

Autonomic reflex testing:

  • Heart Rate Characteristics
  • Heart Rate Variability
  • Heart Rate Turbulence
  • Blood Pressure Variability
  • Baroreflex Sensitivity
  • Hyperoxic Chemoreflex Sensitivity

Hemodynamic Monitoring:

  • Heart Rate Trends
  • Blood Pressure Trends
  • Pulse Wave Characteristics
  • Cardiac Output
  • Peripheral Vascular Resistance
  • Context information

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Study Type : Observational
Actual Enrollment : 60 participants
Observational Model: Case-Only
Time Perspective: Prospective
Official Title: Development of Algorithms to Detect and Predict Hemodynamic Instability in Patients at Risk
Study Start Date : September 2010
Actual Primary Completion Date : April 2015
Actual Study Completion Date : April 2015

Risk Population
Patients being suspected to be at risk of hemodynamic instability due to medical history

Primary Outcome Measures :
  1. Autonomic Dysfunction [ Time Frame: 24 hours ]
    Dysfunction of the autonomic nervous system as assessed by autonomic reflex testing

Secondary Outcome Measures :
  1. Hemodynamic Deterioration [ Time Frame: 24 hours ]
    Acute hemodynamic changes (Blood pressure changes > 10 mm Hg, heart rate changes > 5 bpm both within 30 seconds) of a patient as assessed by hemodynamic monitoring

Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 85 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Probability Sample
Study Population
Patients being at risk of sudden cardiac death

Inclusion Criteria:

  • hospitalization
  • Age > 17 years

Exclusion Criteria:

  • documented diseases of the central nervous system
  • impairment of mental health
  • age > 85 years

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT01262508

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Duesseldorf, NRW, Germany, 40225
Sponsors and Collaborators
Klinik für Kardiologie, Pneumologie und Angiologie
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Study Chair: Christian Meyer, MD University of Duesseldorf
Study Director: Malte Kelm, MD, PhD University of Duesseldorf
Additional Information:
Publications automatically indexed to this study by Identifier (NCT Number):
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Responsible Party: Klinik für Kardiologie, Pneumologie und Angiologie, Klinik für Kardiologie, Pneumologie und Angiologie Christian Meyer, MD, Heinrich-Heine University, Duesseldorf Identifier: NCT01262508    
Other Study ID Numbers: Autonomics - Prediction
First Posted: December 17, 2010    Key Record Dates
Last Update Posted: February 19, 2016
Last Verified: February 2016
Keywords provided by Klinik für Kardiologie, Pneumologie und Angiologie, Heinrich-Heine University, Duesseldorf:
autonomic nervous system
Additional relevant MeSH terms:
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Death, Sudden, Cardiac
Pathologic Processes
Heart Arrest
Heart Diseases
Cardiovascular Diseases
Death, Sudden