Cardiovascular Response Patterns to Sympathetic Stimulation by Central Hypovolemia

Cardiovascular responses to leg-press exercises during head-down tilt

Introduction

Physical exercise and gravitational load affect the activity of the cardiovascular system. How these factors interact with one another is still poorly understood. Here we investigate how the cardiovascular system responds to leg-press exercise during head-down tilt, a posture that reduces orthostatic stress, limits gravitational pooling, and increases central blood volume.

Methods

Seventeen healthy participants performed leg-press exercise during head-down tilt at different combinations of resistive force, contraction frequency, and exercise duration (30 and 60 s), leading to different exercise power. Systolic (sBP), diastolic (dBP), mean arterial pressure (MAP), pulse pressure (PP) and heart rate (HR) were measured continuously. Cardiovascular responses were evaluated by comparing the values of these signals during exercise recovery to baseline. Mixed models were used to evaluate the effect of exercise power and of individual exercise parameter on the cardiovascular responses.

Results

Immediately after the exercise, we observed a clear undershoot in sBP (Δ = -7.78 ± 1.19 mmHg), dBP (Δ = -10.37 ± 0.84 mmHg), and MAP (Δ = -8.85 ± 0.85 mmHg), an overshoot in PP (Δ = 7.93 ± 1.13 mmHg), and elevated values of HR (Δ = 33.5 ± 0.94 bpm) compared to baseline (p < 0.0001). However, all parameters returned to similar baseline values 2 min following the exercise (p > 0.05). The responses of dBP, MAP and HR were significantly modulated by exercise power (correlation coefficients: rdBP = -0.34, rMAP = -0.25, rHR = 0.52, p < 0.001). All signals' responses were modulated by contraction frequency (p < 0.05), increasing the undershoot in sBP (Δ = -1.87 ± 0.98 mmHg), dBP (Δ = -4.85 ± 1.01 and Δ = -3.45 ± 0.98 mmHg for low and high resistive force respectively) and MAP (Δ = -3.31 ± 0.75 mmHg), and increasing the overshoot in PP (Δ = 2.57 ± 1.06 mmHg) as well as the value of HR (Δ = 16.8 ± 2.04 and Δ = 10.8 ± 2.01 bpm for low and high resistive force respectively). Resistive force affected only dBP (Δ = -4.96 ± 1.41 mmHg, p < 0.0001), MAP (Δ = -2.97 ± 1.07 mmHg, p < 0.05) and HR (Δ = 6.81 ± 2.81 bpm, p < 0.0001; Δ = 15.72 ± 2.86 bpm, p < 0.0001; Δ = 15.72 ± 2.86 bpm, p < 0.05, depending on the values of resistive force and contraction frequency), and exercise duration affected only HR (Δ = 9.64 ± 2.01 bpm, p < 0.0001).

Conclusion

Leg exercises caused only immediate cardiovascular responses, potentially due to facilitated venous return by the head-down tilt position. The modulation of dBP, MAP and HR responses by exercise power and that of all signals by contraction frequency may help optimizing exercise prescription in conditions of limited orthostatic stress.

Pulmonary blood volume assessment from a standard cardiac rubidium-82 imaging protocol: impact of adenosine-induced hyperemia

BACKGROUND

This study aimed to assess the feasibility of estimating the pulmonary blood volume noninvasively using standard Rubidium-82 myocardial perfusion imaging (MPI) and characterize the changes during adenosine-induced hyperemia.

METHODS

This study comprised 33 healthy volunteers (15 female, median age = 23 years), of which 25 underwent serial rest/adenosine stress Rubidium-82 MPI sessions. Mean bolus transit times (MBTT) were obtained by calculating the time delay from the Rubidium-82 bolus arrival in the pulmonary trunk to the arrival in the left myocardial atrium. Using the MBTT, in combination with stroke volume (SV) and heart rate (HR), we estimated pulmonary blood volume (PBV = (SV × HR) × MBTT). We report the empirically measured MBTT, HR, SV, and PBV, all stratified by sex [male (M) vs female (F)] as mean (SD). In addition, we report grouped repeatability measures using the within-subject repeatability coefficient.

RESULTS

Mean bolus transit times was shortened during adenosine stressing with sex-specific differences [(seconds); Rest: Female (F) = 12.4 (1.5), Male (M) = 14.8 (2.8); stress: F = 8.8 (1.7), M = 11.2 (3.0), all P ≤ 0.01]. HR and SV increased during stress MPI, with a concomitant increase in the PBV [mL]; Rest: F = 544 (98), M = 926 (105); Stress: F = 914 (182), M = 1458 (338), all P < 0.001. The following test-retest repeatability measures were observed for MBTT (Rest = 17.2%, Stress = 17.9%), HR (Rest = 9.1%, Stress = 7.5%), SV (Rest = 8.9%, Stress = 5.6%), and for PBV measures (Rest = 20.7%, Stress = 19.5%) CONCLUSION: Pulmonary blood volume can be extracted by cardiac rubidium-82 MPI with excellent test-retest reliability, both at rest and during adenosine-induced hyperemia.

Central Hypovolemia Detection During Environmental Stress-A Role for Artificial Intelligence?

The first step to exercise is preceded by the required assumption of the upright body position, which itself involves physical activity. The gravitational displacement of blood from the chest to the lower parts of the body elicits a fall in central blood volume (CBV), which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. The reduction in CBV and stroke volume (SV) in response to postural stress, post-exercise, or to blood loss results in reduced left ventricular filling, which may manifest as orthostatic intolerance. When termination of exercise removes the leg muscle pump function, CBV is no longer maintained. The resulting imbalance between a reduced cardiac output (CO) and a still enhanced peripheral vascular conductance may provoke post-exercise hypotension (PEH). Instruments that quantify CBV are not readily available and to express which magnitude of the CBV in a healthy subject should remains difficult. In the physiological laboratory, the CBV can be modified by making use of postural stressors, such as lower body "negative" or sub-atmospheric pressure (LBNP) or passive head-up tilt (HUT), while quantifying relevant biomedical parameters of blood flow and oxygenation. Several approaches, such as wearable sensors and advanced machine-learning techniques, have been followed in an attempt to improve methodologies for better prediction of outcomes and to guide treatment in civil patients and on the battlefield. In the recent decade, efforts have been made to develop algorithms and apply artificial intelligence (AI) in the field of hemodynamic monitoring. Advances in quantifying and monitoring CBV during environmental stress from exercise to hemorrhage and understanding the analogy between postural stress and central hypovolemia during anesthesia offer great relevance for healthy subjects and clinical populations.

Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis

Assessment of the volume status by blood pressure (BP) monitoring is difficult, since baroreflex control of BP makes it insensitive to blood loss up to about one liter. We hypothesized that a machine learning model recognizes the progression of central hypovolemia toward presyncope by extracting information of the noninvasive blood pressure waveform parametrized through principal component analysis. This was tested in healthy volunteers exposed to simulated hemorrhage by lower body negative pressure (LBNP).

Fifty‐six healthy volunteers were subjected to progressive central hypovolemia. A support vector machine was trained on the blood pressure waveform. Three classes of progressive stages of hypovolemia were defined. The model was optimized for the number of principal components and regularization parameter for penalizing misclassification (cost): C. Model performance was expressed as accuracy, mean squared error (MSE), and kappa statistic (inter‐rater agreement).

Forty‐six subjects developed presyncope of which 41 showed an increase in model classification severity from baseline to presyncope. In five of the remaining nine subjects (1 was excluded) it stagnated. Classification of samples during baseline and end‐stage LBNP had the highest accuracy (95% and 50%, respectively). Baseline and first stage of LBNP demonstrated the lowest MSE (0.01 respectively 0.32). Model MSE and accuracy did not improve for C values exceeding 0.01. Adding more than five principal components did not further improve accuracy or MSE. Increment in kappa halted after 10 principal components had been added. Automated feature extraction of the blood pressure waveform allows modeling of progressive hypovolemia with a support vector machine. The model distinguishes classes between baseline and presyncope.

Support Vector Machine Based Monitoring of Cardio-Cerebrovascular Reserve during Simulated Hemorrhage

Introduction: In the initial phase of hypovolemic shock, mean blood pressure (BP) is maintained by sympathetically mediated vasoconstriction rendering BP monitoring insensitive to detect blood loss early. Late detection can result in reduced tissue oxygenation and eventually cellular death. We hypothesized that a machine learning algorithm that interprets currently used and new hemodynamic parameters could facilitate in the detection of impending hypovolemic shock.

Method: In 42 (27 female) young [mean (sd): 24 (4) years], healthy subjects central blood volume (CBV) was progressively reduced by application of −50 mmHg lower body negative pressure until the onset of pre-syncope. A support vector machine was trained to classify samples into normovolemia (class 0), initial phase of CBV reduction (class 1) or advanced CBV reduction (class 2). Nine models making use of different features were computed to compare sensitivity and specificity of different non-invasive hemodynamic derived signals. Model features included: volumetric hemodynamic parameters (stroke volume and cardiac output), BP curve dynamics, near-infrared spectroscopy determined cortical brain oxygenation, end-tidal carbon dioxide pressure, thoracic bio-impedance, and middle cerebral artery transcranial Doppler (TCD) blood flow velocity. Model performance was tested by quantifying the predictions with three methods: sensitivity and specificity, absolute error, and quantification of the log odds ratio of class 2 vs. class 0 probability estimates.

Results: The combination with maximal sensitivity and specificity for classes 1 and 2 was found for the model comprising volumetric features (class 1: 0.73–0.98 and class 2: 0.56–0.96). Overall lowest model error was found for the models comprising TCD curve hemodynamics. Using probability estimates the best combination of sensitivity for class 1 (0.67) and specificity (0.87) was found for the model that contained the TCD cerebral blood flow velocity derived pulse height. The highest combination for class 2 was found for the model with the volumetric features (0.72 and 0.91).

Conclusion: The most sensitive models for the detection of advanced CBV reduction comprised data that describe features from volumetric parameters and from cerebral blood flow velocity hemodynamics. In a validated model of hemorrhage in humans these parameters provide the best indication of the progression of central hypovolemia.