Mapping of Cardiac Power in Healthy Humans and Testing of a New Blood Pressure Sensor - a Pilot Study
Mapping of Cardiac Power in Healthy Humans and Testing of a New Blood Pressure Sensor - a Pilot Study
Brief Summary:
The study will record hemodynamic data from 20 healthy volunteers at rest and during moderate bicycle exercise in the recumbent and half sitting position for the following purposes:
Sub-study 1 Testing the overall accuracy and the dependence on changes in posture and exercise of a new non-invasive blood pressure sensor against simultaneous invasive measurements.
Sub-study 2 Exploration of the effect of exercise and position on cardiac energy delivery to the circulation. The interplay between heart and vasculature (Ventriculo-arterial coupling) will be characterized based on simultaneous blood pressure and ultrasound blood flow measurements.
Sub-study 3 Evaluation and possible improvement of an individualized mechanistic model predicting the hemodynamic response to exercise based on hemodynamic profile at rest.
Sub-study 4 Testing of a machine learning based system for evaluation of dynamic autoregulation of renal blood flow from simultaneous continuous blood pressure and ultrasound blood flow measurements.
Sub-study 1 A new non-invasive blood pressure sensor designed to give continuous blood pressure signals from ambulatory persons has been developed at NTNU (Norwegian University of Technology and Science). In this sub study we will test the ability of this new device to correctly represent the invasive blood pressure measurements and if its accuracy is affected by changes in posture and activity.
The new sensor is an armband carried around the distal forearm. We will evaluate the degree of correspondence with invasive blood pressure measurements from the probands' other arm. The study's primary goal is to compare paired, beat-to-beat numeric values for systolic, diastolic, and mean arterial pressures during each study period, As a secondary study goal the accordance of the arterial curve waveforms, in these periods will also be compared. Overall performance and data from each separate study period will be reported. The effect of changes in posture and activity on the convergence between invasive and non-invasive measurements will be assessed by comparison of four repeated recordings in recumbent and half sitting positions, namely: at rest and during bicycle exercise of 50, 100 and 150 Watts.
Sub-study 2 Changes in posture and exercise level are both known to affect cardiac output and blood pressure, and thus the energy transfer from the heart to the circulation. The amount of energy can be assessed by multiplying invasive blood pressure recordings with synchronized transthoracic ultrasound measurement of blood flow through the aortic valve. The study's primary goal is to investigate the effect of exercise and position on cardiac energy delivery to the circulation in normal subjects. From the synchronized measurements of blood pressure and blood flow two separate parameters describing energy transfer are calculated, Total Cardiac Power, and Cardiac Power Output, both given in Watts. The former includes the energy associated with oscillations in flow and pressure, the latter does not. Oscillatory power is calculated as the difference: Total Cardiac Power - Cardiac Power Output.
Energy transfer from the heart to the vasculature depends not only on the heart's contractility but also on systemic vascular resistance and aortic elastance. Cardio-vascular interaction will be investigated as a secondary study goal. Two methods will be used, both based on the combination of cardiac ultrasound and invasive arterial pressure. The first of these, Single beat Ventriculo-Arterial Coupling calculates Ae/Ees (Aortic elastance/Left ventricular elastance) that requires cardiac catheterization, from a combination of several less invasive variables. Despite complicated calculation this method has become increasingly popular in clinical research over the last years. Newly Oscillatory Power Fraction, i.e. Oscillatory Power/Total Cardiac Power was proposed as an alternative. This parameter is much easier calculated but little data confirm its reliability as a measure of cardio-vascular interaction.
Repeated synchronized ultrasound and arterial pressure measurements during the same postures and bicycle workloads as described in Sub study 1 will be compared to assess the effects of exercise level and posture on energy transfer and, cardio-vascular interaction in healthy persons.
Sub-study 3 A simple mechanistic model has been established predicting individual hemodynamic responses to exercise based on demographic data and the hemodynamic profile at rest.
The main idea of this sub-study is to test this model by comparing the individually predicted hemodynamic values for each specific combination of posture and exercise applied in sub-studies 1 and 2 to the actual individual hemodynamic responses recorded in the study subjects. The overall result will be analyzed for determination of the model's predictive power.
As a second goal, a detailed analysis will be performed for detection of possible improvements of the model.
Sub-study 4 Impaired Dynamic Autoregulation of Renal Blood Flow may be an early warning signal in intensive care patients developing Acute Kidney Injury. However, assessment of the kidney's autoregulation mechanisms Myogenic Response (MR) and Tubulo-Glomerular Feedback (TGF) depends on simultaneous continuous recordings of blood pressure and flow signals from renal arteries for several minutes. This is challenging as the kidneys move with respiration, and we consequently developed a system for machine learning assisted ultrasound recording of flow velocities in renal arteries. A high-end ordinary ultrasound scanner will be used for a four minutes recording in recumbent and resting probands. Synchronized blood pressure curves are recorded from the indwelling arterial cannula.
Assessment of MR and TGF function will be performed by off-line transfer function frequency analyses with blood pressure as input and flow velocities as output. Different machine learning methods and different approaches to the transfer function analysis will be tested to determine their ability to detect dynamic autoregulation of renal blood flow in normal subjects, and which method(s) perform(s) the best.
Inclusion Criteria:
Exclusion Criteria: