Variations In Blood Volume Research Articles

Respiratory influence on cerebral blood flow and blood volume - A 4D flow MRI study.

Variations in cerebral blood flow and blood volume interact with intracranial pressure and cerebrospinal fluid dynamics, all of which play a crucial role in brain homeostasis. A key physiological modulator is respiration, but its impact on cerebral blood flow and volume has not been thoroughly investigated. Here we used 4D flow MRI in a population-based sample of 65 participants (mean age = 75 ± 1) to quantify these effects. Two gating approaches were considered, one using respiratory-phase and the other using respiratory-time (i.e. raw time in the cycle). For both gating methods, the arterial inflow was significantly larger during exhalation compared to inhalation, whereas the venous outflow was significantly larger during inhalation compared to exhalation. The cerebral blood volume variation per respiratory cycle was 0.83 [0.62, 1.13] ml for respiratory-phase gating and 0.78 [0.59, 1.02] ml for respiratory-time gating. For comparison, the volume variation of the cardiac cycle was 1.01 [0.80, 1.30] ml. Taken together, our results clearly demonstrate respiratory influences on cerebral blood flow. The corresponding vascular volume variations appear to be of the same order of magnitude as those of the cardiac cycle, highlighting respiration as an important modulator of cerebral blood flow and blood volume.

Optimal Dosage of Platelet-Rich Plasma Injections in Patients With Osteoarthritis of the Knee: A Scoping Review.

Knee osteoarthritis (KOA) is a healthcare burden affecting over 595 million people worldwide. Recently, intra-articular platelet-rich plasma (PRP) injections from the patient's blood have shown promise in slowing KOA progression due to platelets' regenerative properties. This study aimed to evaluate the optimal dosing and schedule for PRP therapy in managing mild to moderate KOA. A systematic search was conducted across Embase, Ovid Medline, Web of Science, Cochrane Central, and CINAHL using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify articles published from August 2015 to March 2024. Keywords included "platelet rich plasma," "knee osteoarthritis," and "administration schedule." Inclusion criteria were studies on human patients utilizing PRP as monotherapy in experimental trials, while review articles, editorials, case reports, and meta-analyses were excluded. Three reviewers independently extracted and described patient interventions and outcomes, focusing on Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Visual Analog Scale (VAS), and imaging changes. Thirty-nine publications with PRP monotherapy protocols were found, with fourteen meeting the inclusion criteria. Twelve studies were randomized clinical trials, and two were longitudinal cohort studies, totaling 1704 patients with a mean follow-up of 7.51 ± 4.82 months. The most common PRP protocol was 4.357 ± 1.419 mL infusions, with three doses every four weeks and a single dose being frequent. Platelet values varied, with seven including a mean platelet count, three reporting that the platelet concentration in each dose had to be at least 150,000/μL, and four did not include platelet concentration. There was notable variation in PRP acquisition protocols, blood volume, and centrifugation processes across studies. Therapeutic benefits were represented by WOMAC and VAS scores rather than imaging changes. PRP injections appear to be safe and effective for symptomatic relief of knee pain associated with mild to moderate osteoarthritis (OA). The average infusion volume was 4 mL, administered at three doses four weeks apart. Given that platelet-derived growth factors promote the proliferation of chondrocytes and mesenchymal stem cells, leading to the stimulation of articular cartilage remodeling, further studies are warranted to assess the optimal platelet count necessary for the long-term effects of PRP in knee cartilage healing and sustained symptomatic improvement.

Physiological control for left ventricular assist devices based on deep reinforcement learning.

The improvement of controllers of left ventricular assist device (LVAD) technology supporting heart failure (HF) patients has enormous impact, given the high prevalence and mortality of HF in the population. The use of reinforcement learning for control applications in LVAD remains minimally explored. This work introduces a preload-based deep reinforcement learning control for LVAD based on the proximal policy optimization algorithm. The deep reinforcement learning control is built upon data derived from a deterministic high-fidelity cardiorespiratory simulator exposed to variations of total blood volume, heart rate, systemic vascular resistance, pulmonary vascular resistance, right ventricular end-systolic elastance, and left ventricular end-systolic elastance, to replicate realistic inter- and intra-patient variability of patients with a severe HF supported by LVAD. The deep reinforcement learning control obtained in this work is trained to avoid ventricular suction and allow aortic valve opening by using left ventricular pressure signals: end-diastolic pressure, maximum pressure in the left ventricle (LV), and maximum pressure in the aorta. The results show controller obtained in this work, compared to the constant speed LVAD alternative, assures a more stable end-diastolic volume (EDV), with a standard deviation of 5 mL and 9 mL, respectively, and a higher degree of aortic flow, with an average flow of 1.1 L/min and 0.9 L/min, respectively. This work implements a deep reinforcement learning controller in a high-fidelity cardiorespiratory simulator, resulting in increases of flow through the aortic valve and increases of EDV stability, when compared to a constant speed LVAD strategy.

The Effect of K3EDTA Blood Volume Variation on Complete Blood Count Results

This study aims to determine the effect of varying K3EDTA blood volumes on complete blood count (CBC) results. Venous blood collection was performed using a closed system method. Blood was collected into K3EDTA vacutainer tubes with varying volumes of 1 mL, 2 mL, and 3 mL for each respondent, with a total of 10 respondents. The CBC parameters examined were WBC, RBC, Hb, Hct, MCV, MCH, MCHC, and PLT, using a quality-controlled hematology analyzer. Normally distributed data were analyzed using ANOVA, while non-normally distributed data were analyzed using the Friedman test. The results showed p-values > 0.05 for WBC, RBC, Hb, Hct, MCV, MCH, and MCHC, whereas the PLT test results showed a p-value < 0.05. The conclusion of this study is that there is an effect of varying K3EDTA blood volumes on PLT test results, but no effect on WBC, RBC, Hb, Hct, MCV, MCH, and MCHC test results.

Understanding the physiological transmission mechanisms of photoplethysmography signals: a comprehensive review.

Objective. The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications.Approach. Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints.Significance. The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals.

Predicting Role of GFAP and UCH-L1 biomarkers in Spontaneous Subarachnoid Hemorrhage: a preliminary study to evaluate in the short-term their correlation with severity of bleeding and prognosis

BackgroundSpontaneous non-traumatic subarachnoid hemorrhage (sSAH) is a severe brain vascular accident. Glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) can be theoretically assayed to predict a patient’s progression, picturing different aspects of clinical failure after sSAH. The study aims to: a) explore the correlation between sSAH blood volume and biomarkers variation; b) evaluate if these can be predictive of the neurogenic response after sSAH and be prognostic of patient outcome; c) establish eventual threshold levels of biomarkers to define patients’ clinical outcome. MethodsBlood volumetry at CT scan upon admission, GFAP and UCH-L1 were collected at 24 h, at 72 h, and after 7 days from hemorrhage. Trends and cut-off serum sampling were determined. Clinical outcome was assessed with mRS scale at 14 days. ResultsA strong correlation between GFAP and UCH-L1 and blood diffusion volume in all explored serum intervals related to unfavorable outcome. GFAP and UCH-L1 were very early predictors of unfavorable outcomes at 24 h from sSAH (p = 0.002 and 0.011 respectively). Threshold levels of UCH-L1 apparently revealed a very early, early and late predictor of unfavorable outcomes. ConclusionGFAP and UCH-L1 represent a potential tool for prompt monitoring and customization of therapies in neurosurgical patients.

Deep learning-based photoplethysmography biometric authentication for continuous user verification

Biometric authentication methods have gained prominence as secure and convenient alternatives to traditional passwords and PINs. In this paper, we propose a novel approach for biometric authentication using photoplethysmography (PPG) signals and deep learning techniques. PPG is a non-invasive method that measures variations in blood volume within microvascular tissue beds, and it is typically used for monitoring heart rate and oxygen saturation. Our research leverages the unique characteristics of PPG signals to develop a robust and continuous user verification system. The primary goal of our study is to explore the feasibility and effectiveness of PPG-based biometric authentication, enabling a seamless and secure means of confirming the identity of individuals. We use a diverse dataset of PPG signals from various individuals, ensuring that it encompasses differences in skin tone, age, and other variables that can influence PPG signal characteristics. The collected data undergoes careful preprocessing, including noise removal, baseline correction, and heartbeat segmentation. For the core of our authentication system, we design and train a multiscale feature fusion deep learning (MFFD) model. This model, utilizing a Convolutional Neural Network (CNN) architecture, takes as input the relevant features extracted from PPG signals and learns to differentiate between individuals based on their unique PPG patterns. In this study, the input is constructed by gradually incorporating various features, beginning with a single PPG signal. In this study, the CNN model was trained independently, followed by the implementation of score fusion techniques. Our evaluation demonstrates the effectiveness of the PPG-based biometric authentication system, achieving high accuracy while addressing key security concerns. We consider false acceptance rate (FAR) and false rejection rate (FRR) to assess the system's performance. The model achieves the Accuracy of 99.5 % on BIDMC, 98.6 % on MIMIC, 99.2 % on CapnoBase dataset.

Photoplethysmogram-based heart rate and blood pressure estimation with hypertension classification

A photoplethysmogram (PPG) is an optically-derived signal that records the variation in blood volume within the microvasculature. Certain cardiovascular diseases (CVDs) are symptomatic of damaged blood vessels and problems in blood flow, including hypertension. While software implementations for heart rate and blood pressure estimation exist, point-of-care systems demand hardware-based implementations for real-time estimations to be useful for CVD detection. In this study, digital field programmable gate array (FPGA) based systems are developed for heart rate and blood pressure estimation from PPG signals by means of linear regression. In addition to the blood pressure estimation system, we present a prototype hypertension level detection system that achieves 92.42% accuracy while consuming 0.364 W of power. The Mean Absolute Error (MAE) ± Standard Deviation (SD) for heart rate estimation is 3.17 ± 2.79 beat per minute. The corresponding results for systolic and diastolic blood-pressure estimation are 4.75 ± 2.78 and 3.34 ± 2.60, respectively. The prototype can be further extended to wearable devices and medical equipment in the future.

Kiosk 5R-TC-05 - Pulmonary Blood Volume Variation in Patients with Precapillary Pulmonary Hypertension

Kiosk 5R-TC-05 - Pulmonary Blood Volume Variation in Patients with Precapillary Pulmonary Hypertension

A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG).

A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.

Functional ultrasound imaging of stroke in awake rats

Anesthesia is a major confounding factor in preclinical stroke research as stroke rarely occurs in sedated patients. Moreover, anesthesia affects both brain functions and the stroke outcome acting as neurotoxic or protective agents. So far, no approaches were well suited to induce stroke while imaging hemodynamics along with simultaneous large-scale recording of brain functions in awake animals. For this reason, the first critical hours following the stroke insult and associated functional alteration remain poorly understood. Here, we present a strategy to investigate both stroke hemodynamics and stroke-induced functional alterations without the confounding effect of anesthesia, i.e., under awake condition. Functional ultrasound (fUS) imaging was used to continuously monitor variations in cerebral blood volume (CBV) in +65 brain regions/hemispheres for up to 3 hr after stroke onset. The focal cortical ischemia was induced using a chemo-thrombotic agent suited for permanent middle cerebral artery occlusion in awake rats and followed by ipsi- and contralesional whiskers stimulation to investigate on the dynamic of the thalamocortical functions. Early (0–3 hr) and delayed (day 5) fUS recording enabled to characterize the features of the ischemia (location, CBV loss), spreading depolarizations (occurrence, amplitude) and functional alteration of the somatosensory thalamocortical circuits. Post-stroke thalamocortical functions were affected at both early and later time points (0–3 hr and 5 days) after stroke. Overall, our procedure facilitates early, continuous, and chronic assessments of hemodynamics and cerebral functions. When integrated with stroke studies or other pathological analyses, this approach seeks to enhance our comprehension of physiopathologies towards the development of pertinent therapeutic interventions.

Functional ultrasound imaging of stroke in awake rats.

Anesthesia is a major confounding factor in preclinical stroke research as stroke rarely occurs in sedated patients. Moreover, anesthesia affects both brain functions and the stroke outcome acting as neurotoxic or protective agents. So far, no approaches were well suited to induce stroke while imaging hemodynamics along with simultaneous large-scale recording of brain functions in awake animals. For this reason, the first critical hours following the stroke insult and associated functional alteration remain poorly understood. Here, we present a strategy to investigate both stroke hemodynamics and stroke-induced functional alterations without the confounding effect of anesthesia, i.e., under awake condition. Functional ultrasound (fUS) imaging was used to continuously monitor variations in cerebral blood volume (CBV) in +65 brain regions/hemispheres for up to 3 hr after stroke onset. The focal cortical ischemia was induced using a chemo-thrombotic agent suited for permanent middle cerebral artery occlusion in awake rats and followed by ipsi- and contralesional whiskers stimulation to investigate on the dynamic of the thalamocortical functions. Early (0-3 hr) and delayed (day 5) fUS recording enabled to characterize the features of the ischemia (location, CBV loss), spreading depolarizations (occurrence, amplitude) and functional alteration of the somatosensory thalamocortical circuits. Post-stroke thalamocortical functions were affected at both early and later time points (0-3 hr and 5 days) after stroke. Overall, our procedure facilitates early, continuous, and chronic assessments of hemodynamics and cerebral functions. When integrated with stroke studies or other pathological analyses, this approach seeks to enhance our comprehension of physiopathologies towards the development of pertinent therapeutic interventions.

Muscle stimulation for peripheral venous oxygen saturation estimation using photoplethysmography: a proof-of-concept

Abstract The body’s ability to balance oxygen supply and demand can be compromised in conditions such as shock, sepsis, and heart failure. Thus, measuring venous oxygen saturation (SvO2) simultaneously with the well-established peripheral arterial oxygen saturation can help in the clinical management of these conditions. Some authors have suggested a non-invasive SvO2 estimation method that acquires venous blood volume variations generated through the calf muscle pump using photoplethysmography (PPG): the Venous Muscle Pump Test (VMPT). However, the technique presents significant variability in the rhythm and speed of the foot dorsal flexions needed for the VMPT and cannot be performed on unconscious subjects and those with reduced mobility. This study proposes using functional electrical stimulation (FES) to stimulate the calf muscle and generate rhythmic and reproducible muscle contractions. A human proof-of-concept study was conducted with three healthy young male participants. The PPG signals achieved through the VMPT with conventionally active and FES-induced movements were compared. We found that FES-induced movement produced reproducible venous blood volume variations comparable to the ones induced by the active movement. However, it also leads to lower venous refilling time and lower muscle power. Although further individualized tuning of the stimulation parameters is needed to achieve more conclusive results, FES-induced movement proves to be a promising alternative to the conventional VMPT technique to measure venous oxygen saturation and assess venous insufficiency in specific clinical situations.

Longitudinal Assessment of Cerebral Blood Volume Variation in Human Neonates Using Ultrafast Power Doppler and Diverging Waves

Longitudinal assessment of brain perfusion is a critical parameter for neurodevelopmental outcome of neonates undergoing cardiopulmonary bypass procedure. In this study, we aim to measure the variations of cerebral blood volume (CBV) in human neonates during cardiac surgery, using Ultrafast Power Doppler and freehand scanning. To be clinically relevant, this method must satisfy three criteria: being able to image a wide field of view in the brain, show significant longitudinal CBV variations, and present reproducible results. To address the first point, we performed for the first time transfontanellar Ultrafast Power Doppler using a hand-held phased-array transducer with diverging waves. This increased the field of view more than threefold compared to previous studies using linear transducers and plane waves. We were able to image vessels in the cortical areas as well as the deep grey matter and temporal lobes. Second, we measured the longitudinal variations of CBV on human neonates undergoing cardiopulmonary bypass. When compared to a pre-operative baseline acquisition, the CBV exhibited significant variation during bypass: on average, +20±3% in the mid-sagittal full sector (p<0.0001), -11±3% in the cortical regions (p<0.01) and -10±4% in the basal ganglia (p<0.01). Third, a trained operator performing identical scans was able to reproduce CBV estimates with a variability of 4% to 7.5% depending on the regions considered. We also investigated whether vessel segmentation could further improve reproducibility, but found that it actually introduced greater variability in the results. Overall, this study demonstrates the clinical translation of ultrafast power Doppler with diverging-waves and freehand scanning.

Brain-wide continuous functional ultrasound imaging for real-time monitoring of hemodynamics during ischemic stroke

Ischemic stroke occurs abruptly causing sudden neurologic deficits, and therefore, very little is known about hemodynamic perturbations in the brain immediately after stroke onset. Here, functional ultrasound imaging was used to monitor variations in relative cerebral blood volume (rCBV) compared to baseline. rCBV levels were analyzed brain-wide and continuously at high spatiotemporal resolution (100 μm, 2 Hz) until 70mins after stroke onset in rats. We compared two stroke models, with either a permanent occlusion of the middle cerebral artery (MCAo) or a tandem occlusion of both the common carotid and middle cerebral arteries (CCAo + MCAo). We observed a typical hemodynamic pattern, including a quick drop of the rCBV after MCAo, followed by spontaneous reperfusion of several brain regions located in the vicinity of the ischemic core. The severity and location of the ischemia were variable within groups. On average, the severity of the ischemia was in good agreement with the lesion volume (24 hrs after stroke) for MCAo group, while larger for the CCAo + MCAo model. For both groups, we observed that infarcts extended to initially non-ischemic regions located rostrally to the ischemic core. These regions strongly colocalize with the origin of transient hemodynamic events associated with spreading depolarizations.

Non-invasive waveform analysis for emergency triage via simulated hemorrhage: An experimental study using novel dynamic lower body negative pressure model

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) model among healthy volunteers. We used a dynamic LBNP protocol as opposed to the traditional model, where LBNP is applied in a predictable step-wise, progressively descending manner. This dynamic LBNP version assists in circumventing the problem posed in terms of time dependency, as in real-life pre-hospital settings intravascular blood volume may fluctuate due to volume resuscitation. A supervised DL-based framework for ternary classification was realized by segmenting the underlying noninvasive signal and labeling segments with corresponding LBNP target levels. The proposed DL model with two inputs was trained with respective time–frequency representations extracted on waveform segments to classify each of them into blood volume loss: Class 1 (mild); Class 2 (moderate); or Class 3 (severe). At the outset, the latent space derived at the end of the DL model via late fusion among both inputs assists in enhanced classification performance. When evaluated in a 3-fold cross-validation setup with stratified subjects, the experimental findings demonstrated PPG to be a potential surrogate for variations in blood volume with average classification performance, AUROC: 0.8861, AUPRC: 0.8141, F1-score:72.16%, Sensitivity:79.06%, and Specificity:89.21%. Our proposed DL algorithm on PPG signal demonstrates the possibility to capture the complex interplay in physiological responses related to both bleeding and fluid resuscitation using this challenging LBNP setup.

An Investigation on Conductive Intracardiac Communication Dynamic Channel Gain During the Cardiac Cycle for Leadless Pacemakers

In galvanic coupling conductive intracardiac communication(GCCIC) of the leadless pacemakers, the electrical signal transmitted directly through the myocardium and blood is inevitably affected by the cardiac cycle. Established studies focused more on the effect of the myocardium. However, our preliminary in-vitro experiments suggested that blood volume variations also significantly impacted signal transmission. In this article, we analyzed the blood volume variations during the cardiac cycle and designed an in-vitro experimental platform containing a simulated heart beating system and an automatic channel characteristic acquisition system, which controlled two peristaltic pumps to realize the periodic blood volume variations and the continuous acquisition of channel gain. Through the in-vitro porcine heart experiment, the effect of frequency and blood volume variations during the cardiac cycle on two channel gains was analyzed. Considering the impact of high-frequency signal leakage, the channel gain variations of the low frequency are the main concern. The results showed that the channel gain was positively correlated with frequency; it changed periodically with blood volume variations in the cardiac cycle, and the trends were different due to the different signal paths of the two channels. For the Right Ventricle-Right Atrium channel, the gain varied from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-67$</tex-math></inline-formula> dB to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-53$</tex-math></inline-formula> dB and is inversely correlated with blood volume. The gain fluctuation range was smaller for the Right Ventricle-Left Ventricle channel, about 2 dB. This study shows that the gain of intracardiac communication channels, especially the RV-RA channel, is influenced by blood volume variations during the cardiac cycle.

Impact of cardiopulmonary bypass on cerebrovascular autoregulation assessed by ultrafast ultrasound imaging.

Newborns with congenital heart disease undergoing cardiac surgery are at risk of neurodevelopmental impairment with limited understanding of the impact of intra-operative cardiopulmonary bypass (CPB), deep hypothermia and selective cerebral perfusion on the brain. We hypothesized that a novel ultrasound technique, ultrafast power Doppler (UPD), can assess variations of cerebral blood volume (CBV) in neonates undergoing cardiac surgery requiring CPB. UPD was performed before, during and after surgery in newborns with hypoplastic left heart syndrome undergoing a Norwood operation. We found that global CBV was not significantly different between patients and controls (P=0.98) and between pre- and post-surgery (P=0.62). UPD was able to monitor changes in CBV throughout surgery, revealing regional differences in CBV during hypothermia during which CBV correlated with CPB flow rate (R2 =0.52, P=0.021). Brain injury on post-operative magnetic resonance imaging was observed in patients with higher maximum variation in CBV. Our findings suggest that UPD can quantify global and regional brain perfusion variation during neonatal cardiac surgery with this first intra-operative application demonstrating an association between CBV and CPB flow rate, suggesting loss of autoregulation. Therefore, the measurement of CBV by UPD could enable optimization of cerebral perfusion during cardiac surgery in neonates. KEY POINTS: The impact of cardiopulmonary bypass (CPB) on the neonatal brain undergoing cardiac surgery is poorly understood. Ultrafast power Doppler (UPD) quantifies cerebral blood volume (CBV), a surrogate of brain perfusion. CBV varies throughout CPB surgery and is associated with variation of the bypass pump flow rate during deep hypothermia. Association between CBV and bypass pump flow rate suggests loss of cerebrovascular autoregulatory processes. Quantitative monitoring of cerebral perfusion by UPD could provide a direct parameter to optimize CPB flow rate.

A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods

Photoplethysmography is a method to visualize the variation in blood volume within tissues with light. The signal obtained has been used for the monitoring of patients, interpretation for diagnosis or for extracting other physiological variables (e.g., pulse rate and blood oxygen saturation). However, the photoplethysmography signal can be perturbed by external and physiological factors. Implementing methods to evaluate the quality of the signal allows one to avoid misinterpretation while maintaining the performance of its applications. This paper provides an overview on signal quality index algorithms applied to photoplethysmography. We try to provide a clear view on the role of a quality index and its design. Then, we discuss the challenges arising in the quality assessment of imaging photoplethysmography.

312.9: Mathematical Modelling to Determine Segment Perfusion Parameters for Living Donor Liver Transplantation

Introduction: Adult-to-Adult Living Donor Liver Transplantation (A2ALL-1) Cohort Study focused on hepatic blood flow and effect of portal modulation in smaller and left lobe grafts demonstrating a significantly (p = 0.03) higher graft dysfunction in modulated recipients versus unmodulated. The study concluded the need for prespecified portal flow and hepatic venous outflow modulation protocols to better define outcomes of volumetric interventions. We developed a mathematical model of the liver to determine hemodynamic parameters across segments following simulated liver resections and middle hepatic vein interventions in both the remnant donor liver and recipient grafts to predict functional graft outcomes prior to reperfusion. Methods: Arterial/portal/hepatic vein and terminal portal triad diameters from 12 deceased donor livers and biopsies were used in a custom lumped parameter model developed in MATLAB {Fig(a)}. Blood flow is modeled using the Hagen-Poiseuille equation, and the continuity equation is solved to obtain volume flow rate of the whole liver at every point throughout the network {Fig(b)}. Simulated right hepatectomy in the plane of resection shown in Fig(a) is used to derive variations in blood flow and volume through the modeled left liver lobe {Fig(c)}.Results: Schematic illustrations of the modeling components of liver segments demonstrate volume flow rate encoded in pseudocolor mapping in the donor liver before and after right hepatectomy {Fig(b&c)}. Localized increase in blood flow through 2nd and 3rd generations of vessel division is especially apparent in the donor’s simulated remnant left lobe {Fig(c)}. Volume and flow rate alterations in hepatic venous outflow following ligation/reconstruction of middle hepatic veins in the remnant left lobe is evident. Perfusion parameters in the donor graft are also captured to simulate graft perfusion prior to reperfusion in the recipient. Conclusion: Surgical intervention on the middle hepatic vein (MHV) is critical for donor remnant liver outflow and optimal perfusion in the donor graft. Mathematical simulations to predict hemodynamic inflow – outflow parameters in living donor liver transplantation could help guide MHV ligation and reconstruction as well as predict graft perfusion parameters prior to reperfusion.