Cardiac Mechanical Activity Research Articles

Contactless Electrocardiogram Monitoring With Millimeter Wave Radar

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.

OHCA in Bosnia and Herzegovina: Before and During the COVID-19 Pandemic.

Out-of-hospital cardiac arrest (OHCA) refers to the cessation of mechanical cardiac activity outside healthcare facilities which requires prompt intervention and intensive resuscitative efforts. The COVID-19 pandemic has caused significant disruptions to OHCA systems-of-care, adversely affecting every component of the chain of survival. The objective of this study was to examine the potential impacts of the COVID-19 pandemic on OHCA events, to draw comparisons between the period before and during the COVID-19 pandemic. This cross-sectional study encompassed data pertaining to all OHCA incidents attended to by the Emergency Medical Service of Canton Sarajevo, covering the period from January 2017 to December 2022, before and during the COVID-19 pandemic. During observed period, a total of 1418 [796 (56.1%) before and 622 (43.9%) during COVID-19 pandemic] OHCA events have occurred in Canton Sarajevo of which 297 (20.9 %) [180 (12.7%) before and 117 (8.2%) during COVID-19 pandemic] obtained ROSC. After a 30-day period following the ROSC) it was observed that the predominant outcome, accounting for 181 (12.7%) [106 (7.4%) before and 75 (5.2%) during COVID-19 pandemic] of cases, was a complete recovery. An examination before and during COVID-19 pandemic revealed a decline in OHCA during the year 2021 and 2022 when COVID-19 pandemic was at its highest in the country Being younger, quicker EMT response time and individuals with the initial rhythm of VF or VT were significantly associated with obtaining ROSC (p<0.05). Only 48 (3.3%) of 1418 OHCA events were assisted by bystanders There was no report of AED usage. In conclusion, our investigation highlights the impact of the COVID-19 pandemic on OHCA events in Canton Sarajevo, revealing a decrease in OHCA incidence and a reduction in cases achieving ROSC. Notably, EMT response time was shorter during the pandemic.

Outcomes of Cardiopulmonary Resuscitation and Predictors of Its Outcomes in the Emergency Department in King Saud Medical City, Saudi Arabia.

Cardiac arrest is a medical emergency marked by the cessation of cardiac mechanical activity and insufficient blood flow. CPR (cardiopulmonary resuscitation) is a life-saving method that involves restoring the essential functions of two vital organs: the heart and lungs. This study was conducted to identify the outcome of CPR in cardiac arrest patients presented to the emergency department (ED) and to identify predictors of CPR outcomes. This was a retrospective, descriptive study. All in-hospital cardiac arrest patients who underwent CPR in the King Saud Medical City (KSMC) ED between January 2017 and January 2020 were analyzed, with a sample size of 351 patients. Overall return of spontaneous circulation (ROSC) and survival to discharge (STD) were achieved in 106 (30.2%) and 40 (11.39%) patients, respectively. When assessing the predictors of ROSC, the analyses showed that patient age, pre-arrest intubation, the method used to deliver oxygen, and CPR duration were all statistically significant predictors for ROSC. Similarly, when assessing predictors associated with STD, the analyses showed that patient age, pre-arrest intubation, the method used to deliver oxygen, and CPR duration were positively associated with STD. Comparing the study's findings to those of similar studies, it shows a CPR outcome rate within the range of similar studies. It also highlights that CPR outcomes are highly associated with CPR duration (a maximum of 30 minutes), younger age, and endotracheal intubation.

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases.

Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously. The purpose of this study was to compare the time domain, frequency domain and nonlinear HRV indices obtained from electrocardiograms, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) in healthy volunteers and patients with valvular heart diseases. An analysis of the time domain, frequency domain and nonlinear heart rate variability was conducted on electrocardiograms and gyrocardiograms registered from 29 healthy male volunteers and 30 patients with valvular heart diseases admitted to the Columbia University Medical Center (New York City, NY, USA). The results of the HRV analysis show a strong linear correlation with the HRV indices calculated from the ECG, SCG and GCG signals and prove the feasibility and reliability of HRV analysis despite the influence of VHDs on the SCG and GCG waveforms.

Role of ECMO in E-CPR

Extracorporeal cardiopulmonary resuscitation (ECPR) is the implantation of venoarterial extracorporeal membrane oxygenation (VA-ECMO) in a patient who experienced a sudden and unexpected pulseless condition attributable to cessation of cardiac mechanical activity. The aim of ECPR is to provide adequate perfusion to the end organs when the potentially “reversible” conditions were managed. ECPRs are mostly done in tertiary care center in India. There is little scope for out of hospital arrest as poor quality of CPR and response time exists with lack of awareness about ECPR, which hinders its wider usage even in IHCA. Emergency department doctors need to be involved to counsel, recognize candidates activate, and initiate ECPR.

Abstract 9856: Gender and Racial Disparities in Reported Cardiac Arrest Death in United States, 1999 to 2019

Introduction: Cardiac arrest (CA) is the cessation of cardiac mechanical activity confirmed by the absence of signs of circulation. Data suggest that race and gender could impact the survival rate of CA (1,2). We aim to describe the temporal trend of the age-adjusted mortality rate of gender and race reported as any-mention cause of cardiac arrest death from 1990 to 2019. Methods: The United States statistics mortality data from the CDC WONDER database from 1999 to 2019 were used. The diagnosis of CA was stablished using the ICD-10 codes: I46.0, I46.1, I46.9 and I49.0. Mortality rate was calculated for all ages. Age-adjusted mortality rates per 100000 were calculated using 95% confidence intervals. Results: The study included 7435677 subjects with any-mention of CA death. Most deaths occurred in older individuals (90.7 % of the death reported in those 55 years or older). Cardiac Arrest deaths decreased from 138.1 in 1999 to 91.5 in 2019. Temporal trend depicted a gradual decline in the age-adjusted mortality rate per year (IRR 0.97 95 % CI 0.95 to 0.99). There was a progressive decrease mortality rate across both male (167.6 in 1999 to 110 in 2019 IRR 0.97 95% CI 0.96 to 0.98) and female (117.5 1999 to 76.5 in 2019 IRR 0.97 95% CI 0.96 to 0.98). The age-adjusted mortality rate was significantly higher in males compared to females (126.7 vs 91.3 IRR 1.39 95 % CI 1.27 to 1.52). There was a significant decrease in the mortality rate through 1999 to 2019 seen in all the races. Conclusions: There was an association in the CA mortality rate that persisted over the years favoring male over females. The Black race had the highest mortality rate among the races. American Native had the lowest mortality rate. The phenomenon underlying the race difference is not fully understood (3). Quality-improvement efforts have been associated with improvement of survival (4,5). The present data demonstrates a gradual decline of CA-related mortality associated with significant high gender and racial disparities.

Échographie préhospitalière et urgences cardiologiques

Technological advances over the past two decades have paved the way for the prehospital use of ultrasound. This practice was first developed in traumatology and then in a multitude of other indications, including cardiology. The development of pulmonary ultrasound is certainly the most visible illustration of this. Firstly, because it is an extra-cardiac examination that provides the answer to a cardiac question. Secondly because from a theoretical point of view this ultrasound indication was a bad indication for the use of ultrasound due to the air contained in the thorax. Thirdly, because this indication has become a ‘standard of care’ when caring for a patient with dyspnea – a practice that has become widespread during the COVID epidemic. In patients with heart failure, ultrasound has a high diagnostic power (including for alternative diagnoses) which is all the more precise since the technique is non-invasive, the response is obtained quickly, the examination can be repeated at desire to follow the evolution of the patient. The main other indications for prehospital ultrasound are cardiac arrest to search for a curable cause, identification of residual mechanical cardiac activity, monitoring of cerebral perfusion; chest pain, for both positive and negative diagnoses; shock for the search for an etiology and therapeutic follow-up or even pulmonary embolism or ultrasound for the search for dilation of the right ventricle which is now at the forefront of the recommendation algorithm.

A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification

Objective. Heart sounds can reflect detrimental changes in cardiac mechanical activity that are common pathological characteristics of chronic heart failure (CHF). The ACC/AHA heart failure (HF) stage classification is essential for clinical decision-making and the management of CHF. Herein, a machine learning model that makes use of multi-scale and multi-domain heart sound features was proposed to provide an objective aid for ACC/AHA HF stage classification. Approach. A dataset containing phonocardiogram (PCG) signals from 275 subjects was obtained from two medical institutions and used in this study. Complementary ensemble empirical mode decomposition and tunable-Q wavelet transform were used to construct self-adaptive sub-sequences and multi-level sub-band signals for PCG signals. Time-domain, frequency-domain and nonlinear feature extraction were then applied to the original PCG signal, heart sound sub-sequences and sub-band signals to construct multi-scale and multi-domain heart sound features. The features selected via the least absolute shrinkage and selection operator were fed into a machine learning classifier for ACC/AHA HF stage classification. Finally, mainstream machine learning classifiers, including least-squares support vector machine (LS-SVM), deep belief network (DBN) and random forest (RF), were compared to determine the optimal model. Main results. The results showed that the LS-SVM, which utilized a combination of multi-scale and multi-domain features, achieved better classification performance than the DBN and RF using multi-scale or/and multi-domain features alone or together, with average sensitivity, specificity, and accuracy of 0.821, 0.955 and 0.820 on the testing set, respectively. Significance. PCG signal analysis provides efficient measurement information regarding CHF severity and is a promising noninvasive method for ACC/AHA HF stage classification.

Prevalence, Outcomes, and Risk Factors for Cardiorespiratory Arrest in the Intensive Care Unit: An Observational Study.

BackgroundCardiorespiratory arrest is defined as an abrupt halt in the cardiac mechanical activity that is accompanied by the loss of a detectable pulse, the cessation of breathing, and the loss of consciousness. The aim of this study is to create a clinical–epidemiological profile of patients who experienced cardiorespiratory arrest and were admitted to the intensive care unit to evaluate the associated factors and their impact on the prognosis of these patients.Patients and methodsFrom January to December 2019, the medical records of 135 patients who received cardiopulmonary resuscitation were reviewed for this cross-sectional observational study. The information was collected according to the Utstein model.ResultsA low return of spontaneous circulation of 22.2% was observed, with a predominance of females (53.3%) and older patients (68.9%), multiple comorbidities at admission (68.4%), and asystole as the predominant rhythm. Female sex and age >60 years were statistically significant (p = 0.017), as was the association between sex and comorbidities (p = 0.036), with heart disease being the most prevalent in females (p = 0.036).ConclusionIn this study, even though the resuscitation maneuver time (start of resuscitation following arrest) was very short and the defibrillation was performed promptly, there was a high prevalence of cardiac arrest and low survival rates after cardiopulmonary resuscitation.How to cite this articleMenezes da Silva A, Braga AL, Cruvinel de Souza V. Prevalence, Outcomes, and Risk Factors for Cardiorespiratory Arrest in the Intensive Care Unit: An Observational Study. Indian J Crit Care Med 2022;26(6):704–709.

Studying Cardiac Neural Network Dynamics: Challenges and Opportunities for Scientific Computing.

Neural control of the heart involves continuous modulation of cardiac mechanical and electrical activity to meet the organism’s demand for blood flow. The closed-loop control scheme consists of interconnected neural networks with central and peripheral components working cooperatively with each other. These components have evolved to cooperate control of various aspects of cardiac function, which produce measurable “functional” outputs such as heart rate and blood pressure. In this review, we will outline fundamental studies probing the cardiac neural control hierarchy. We will discuss how computational methods can guide improved experimental design and be used to probe how information is processed while closed-loop control is operational. These experimental designs generate large cardio-neural datasets that require sophisticated strategies for signal processing and time series analysis, while presenting the usual large-scale computational challenges surrounding data sharing and reproducibility. These challenges provide unique opportunities for the development and validation of novel techniques to enhance understanding of mechanisms of cardiac pathologies required for clinical implementation.

Model-Based Analysis of Myocardial Contraction Patterns in Ischemic Heart Disease

ObjectivesThe objective of the current study is to assess the feasibility of using a left ventricle (LV) model in order to reproduce myocardial strains in the case of ischemic heart disease (IHD). Materials and MethodsThe proposed integrated LV model couples a finite element method (FEM) sub-model of the cardiac electrical activity, representing the propagation of the transmembrane potential through the LV, a FEM sub-model of the cardiac mechanical activity, and a lumped-parameter model of the circulatory system that improves the physiological definition of appropriate boundary conditions. Simulations were compared to myocardial strain data obtained from echocardiography, performed on two healthy subjects and two patients diagnosed with chronic IHD. ResultsResults show the model ability to simulate jointly the hemodynamic variables (Systolic and diastolic pressures respectively equal to 110 and 60 mmHg) and myocardial strain curves during each phase of the cardiac cycle. A close match is observed between minimum strains obtained from simulations and clinical data from healthy subjects and IHD patient, with a root mean square error around 2,81% and 2,63% respectively. The modifications of regional LV systolic strains observed from IHD patients with respect to healthy cases were partially explained by a decrease of regional cardiac stress. ConclusionThis paper provides a description of a new coupling algorithm between FEM and lumped-parameter models. Results are promising for the analysis of cardiac strains in the context of IHD.

A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram.

Seismocardiography (SCG) is largely regarded as the state-of-the-art technique for continuous, long-term monitoring of cardiac mechanical activity in wearable applications. SCG signals are acquired via small, lightweight accelerometers fixed on the chest. They provide timings of important cardiac events, such as heart valves openings and closures, thus allowing the estimation of cardiac time intervals of clinical relevance. Forcecardiography (FCG) is a novel technique that records the cardiac-induced vibrations of the chest wall by means of specific force sensors, which proved capable of monitoring respiration, heart sounds and infrasonic cardiac vibrations, simultaneously from a single contact point on the chest. A specific infrasonic component captures the heart walls displacements and looks very similar to the Apexcardiogram. This low-frequency component is not visible in SCG recordings, nor it can be extracted by simple filtering. In this study, a feasible way to extract this information from SCG signals is presented. The proposed approach is based on double integration of SCG. Numerical double integration is usually very prone to large errors, therefore a specific numerical procedure was devised. This procedure yields a new displacement signal (DSCG) that features a low-frequency component (LF-DSCG) very similar to that of the FCG (LF-FCG). Experimental tests were carried out using an FCG sensor and an off-the-shelf accelerometer firmly attached to each other and placed onto the precordial region. Simultaneous recordings were acquired from both sensors, together with an electrocardiogram lead (used as a reference). Quantitative morphological comparison confirmed the high similarity between LF-FCG and LF-DSCG (normalized cross-correlation index >0.9). Statistical analyses suggested that LF-DSCG, although achieving a fair sensitivity in heartbeat detection (about 90%), has not a very high consistency within the cardiac cycle, leading to inaccuracies in inter-beat intervals estimation. Future experiments with high-performance accelerometers and improved processing methods are envisioned to investigate the potential enhancement of the accuracy and reliability of the proposed method.

TRPV1 Contributes to Modulate the Nitric Oxide Pathway and Oxidative Stress in the Isolated and Perfused Rat Heart during Ischemia and Reperfusion.

The transient vanilloid receptor potential type 1 (TRPV1) regulates neuronal and vascular functions mediated by nitric oxide (NO) and by the calcitonin gene-related peptide (CGRP). Here, we study the participation of TRPV1 in the regulation of myocardial injury caused by ischemia-reperfusion and in the control of NO, tetrahydrobiopterin (BH4), the cGMP pathway, CGRP, total antioxidant capacity (TAC), malondialdehyde (MDA) and phosphodiesterase-3 (PDE-3). Isolated hearts of Wistar rats perfused according to the Langendorff technique were used to study the effects of an agonist of TRPV1, capsaicin (CS), an antagonist, capsazepine (CZ), and their combination CZ+CS. The hearts were subjected to three conditions: (1) control, (2) ischemia and (3) ischemia-reperfusion. We determined cardiac mechanical activity and the levels of NO, cGMP, BH4, CGRP, TAC, MDA and PDE-3 in ventricular tissue after administration of CS, CZ and CZ+CS. Western blots were used to study the expressions of eNOS, iNOS and phosphorylated NOS (pNOS). Structural changes were determined by histological evaluation. CS prevented damage caused by ischemia-reperfusion by improving cardiac mechanical activity and elevating the levels of NO, cGMP, BH4, TAC and CGRP. TRPV1 and iNOS expression were increased under ischemic conditions, while eNOS and pNOS were not modified. We conclude that the activation of TRPV1 constitutes a therapeutic possibility to counteract the damage caused by ischemia and reperfusion by regulating the NO pathway through CGRP.

Closed-Loop Multiscale Computational Model of Human Blood Circulation. Applications to Ballistocardiography.

Cardiac mechanical activity leads to periodic changes in the distribution of blood throughout the body, which causes micro-oscillations of the body’s center of mass and can be measured by ballistocardiography (BCG). However, many of the BCG findings are based on parameters whose origins are poorly understood. Here, we generate simulated multidimensional BCG signals based on a more exhaustive and accurate computational model of blood circulation than previous attempts. This model consists in a closed loop 0D-1D multiscale representation of the human blood circulation. The 0D elements include the cardiac chambers, cardiac valves, arterioles, capillaries, venules, and veins, while the 1D elements include 55 systemic and 57 pulmonary arteries. The simulated multidimensional BCG signal is computed based on the distribution of blood in the different compartments and their anatomical position given by whole-body magnetic resonance angiography on a healthy young subject. We use this model to analyze the elements affecting the BCG signal on its different axes, allowing a better interpretation of clinical records. We also evaluate the impact of filtering and healthy aging on the BCG signal. The results offer a better view of the physiological meaning of BCG, as compared to previous models considering mainly the contribution of the aorta and focusing on longitudinal acceleration BCG. The shape of experimental BCG signals can be reproduced, and their amplitudes are in the range of experimental records. The contributions of the cardiac chambers and the pulmonary circulation are non-negligible, especially on the lateral and transversal components of the velocity BCG signal. The shapes and amplitudes of the BCG waveforms are changing with age, and we propose a scaling law to estimate the pulse wave velocity based on the time intervals between the peaks of the acceleration BCG signal. We also suggest new formulas to estimate the stroke volume and its changes based on the BCG signal expressed in terms of acceleration and kinetic energy.

Matrix Metalloproteinase-2 Inhibition in Acute Ischemia-Reperfusion Heart Injury-Cardioprotective Properties of Carvedilol.

Matrix metalloproteinase 2 (MMP-2) is activated in hearts upon ischemia-reperfusion (IR) injury and cleaves sarcomeric proteins. It was shown that carvedilol and nebivolol reduced the activity of different MMPs. Hence, we hypothesized that they could reduce MMPs activation in myocytes, and therefore, protect against cardiac contractile dysfunction related with IR injury. Isolated rat hearts were subjected to either control aerobic perfusion or IR injury: 25 min of aerobic perfusion, followed by 20 min global, no-flow ischemia, and reperfusion for 30 min. The effects of carvedilol, nebivolol, or metoprolol were evaluated in hearts subjected to IR injury. Cardiac mechanical function and MMP-2 activity in the heart homogenates and coronary effluent were assessed along with troponin I content in the former. Only carvedilol improved the recovery of mechanical function at the end of reperfusion compared to IR injury hearts. IR injury induced the activation and release of MMP-2 into the coronary effluent during reperfusion. MMP-2 activity in the coronary effluent increased in the IR injury group and this was prevented by carvedilol. Troponin I levels decreased by 73% in IR hearts and this was abolished by carvedilol. Conclusions: These data suggest that the cardioprotective effect of carvedilol in myocardial IR injury may be mediated by inhibiting MMP-2 activation.

Hepatic Remote Ischemic Preconditioning (RIPC) Protects Heart Damages Induced by Ischemia Reperfusion Injury in Mice.

It has been convincingly demonstrated that remote ischemic preconditioning (RIPC) can make the myocardium resistant to the subsequent ischemia reperfusion injury (IRI), which causes severe damages by mainly generating cell death. However, the cardioprotective effects of the hepatic RIPC, which is the largest metabolic organ against I/R, have not been fully studied. The aim of our research is whether remote liver RIPC may provide cardioprotective effects against the I/R-induced injury. Here, we generated an I/R mice model in four groups to analyze the effect. The control group is the isolated hearts with 140-min perfusion. I/R group added ischemia in 30 min following 90-min reperfusion. The third group (sham) was subjected to the same procedure as the latter group. The animals in the fourth group selected as the treatment group, underwent a hepatic RIPC by three cycles of 5-min occlusion of the portal triad and then followed by induction of I/R in the isolated heart. The level of myocardial infarction and the preventive effects of RIPC were assessed by pathological characteristics, namely, infarct, enzyme releases, pressure, and cardiac mechanical activity. Subjected to I/R, the hepatic RIPC minimized the infarct size (17.7 ± 4.96 vs. 50.06 ± 5, p < 0.001) and improved the left ventricular-developed pressure (from 47.42 ± 6.27 to 91.62 ± 5.22 mmHg) and the mechanical activity. Release of phosphocreatine kinase-myocardial band (73.86 ± 1.95 vs. 25.93 ± 0.66 IUL−1) and lactate dehydrogenase (299.01 ± 10.7 vs. 152.3 ± 16.7 IUL−1) was also decreased in the RIPC-treated group. These results demonstrate the cardioprotective effects of the hepatic remote preconditioning against the injury caused by I/R in the isolated perfused hearts.

Pediatric extracorporeal cardiopulmonary resuscitation: single-center study

Background/aimExtracorporeal cardiopulmonary resuscitation (ECPR) is defined as the venoarterial extracorporeal membrane oxygenation (VA-ECMO) support in a patient who experienced a sudden pulseless condition attributable to cessation of cardiac mechanical activity and circulation. We aimed to evaluate the clinical outcomes of our ECPR experience in a pediatric patient population.Materials and methodsBetween September 2014 and November 2017, 15 children were supported with ECPR following in-hospital cardiac arrest (IHCA) in our hospitals. VA-ECMO setting was established for all patients. Pediatric cerebral performance category (PCPC) scales and long-term neurological prognosis of the survivors were assessed.ResultsThe median age of the study population was 60 (4–156) months. The median weight was 18 (4.8–145) kg, height was 115 (63–172) cm, and body surface area was 0.73 (0.27–2.49) m2. The cause of cardiac arrest was a cardiac and circulatory failure in 12 patients (80%) and noncardiac causes in 20%. Dysrhythmia was present in 46%, septic shock in 13%, bleeding in 6%, low cardiac output syndrome in 13%, and airway disease in 6% of the study population. Median low-flow time was 95 (range 20–320) min. Central VA-ECMO cannulation was placed in only 2 (13.3%) cases. However, the return of spontaneous circulation (ROSC) was obtained in 10 (66.6%) patients, and 5 (50%) of them survived. Overall, 5 patients were discharged from the hospital. Finally, survival following ECPR was 33.3%, and all survivors were neurologically intact at hospital-discharge.ConclusionECPR can be a life-saving therapeutic strategy using a promising technology in the pediatric IHCA population. Early initiation and a well-coordinated, skilled, and dedicated ECMO team are the mainstay for better survival rates.

Circadian Rhythms in Sudden Cardiac Arrest: A Review.

Sudden cardiac arrest (SCA) is a serious public health issue caused by the cessation of cardiac electrical and mechanical activity. Despite advances in pedestrian lifesaving technologies like defibrillators, the SCA mortality rate remains high, and survivors are at risk of suffering ischemic injury to various organs. Understanding the contributing factors for SCA is essential for improving morbidity and mortality. One factor capable of influencing SCA incidence and survival is the time of day at which SCA occurs. This review focused on the effect of time of day on SCA incidence, survival rate, and survival to discharge over the past 30 years and the role of age, sex, and SCA location in modulating the timing of SCA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews criteria guided this review. Four databases (PubMed, Cochrane Libraries, Scopus, and Cumulative Index to Nursing and Allied Health Literature) were queried for research reports or articles addressing time of day and cardiac arrest, which were subsequently screened by the authors for inclusion in this analysis. A total of 48 articles were included in the final analysis. This analysis showed a bimodal SCA distribution with a primary peak in the morning and a secondary peak in the afternoon; these peaks were dependent on age (older persons), sex (more frequent in males), and the location of occurrence (out-of-hospital cardiac arrest vs. in-hospital cardiac rest). Survival following SCA was lowest between midnight and 06:00 a.m. The circadian rhythm likely plays an important role in the time-of-day-dependent pattern that is evident in both the incidence of and survival following SCA. There is a renewed call for nursing research to examine or address circadian rhythm as an element in studies involving older adults and activities affecting cardiovascular or respiratory parameters.

Implantable Fiber Bragg Grating Sensor for Continuous Heart Activity Monitoring: Ex-Vivo and In-Vivo Validation

Continuous and reliable cardiac function monitoring could improve medication adherence in patients at risk of heart failure. This work presents an innovative implantable Fiber Bragg Grating-based soft sensor designed to sense mechanical cardiac activity. The sensor was tested in an isolated beating ovine heart platform, with 3 different hearts operated in wide-ranging conditions. In order to investigate the sensor capability to track the ventricular beats in real-time, two causal algorithms were proposed for detecting the beats from sensor data and to discriminate artifacts. The first based on dynamic thresholds while the second is a hybrid convolutional and recurrent Neural Network. An error of 2.7 ± 0.7 beats per minute was achieved in tracking the heart rate. Finally, we have confirmed the sensor reliability in monitoring the heart activity of healthy adult minipig with an error systematically lower than 1 Bpm.

Abstract 293: Detection of Pseudo-Pulseless Electrical Activity with Electrical Impedance Tomography

Introduction: Pseudo-Pulseless Electrical Activity (p-PEA) is a lifeless form of profound cardiac shock characterized by measurable cardiac mechanical activity without clinically detectable pulses. p-PEA may constitute up to 40% of reported cases of cardiac arrest and its management may be different from ventricular fibrillation or even true-PEA. Currently, diagnosis of p-PEA requires either echocardiography or intravascular catheterization, neither of which are ideal in the prehospital setting. Electrical impedance tomography (EIT) uses skin surface electrodes to generate images based on cross sectional resistance. We investigated the ability of EIT and machine learning (ML) to detect p-PEA. Methods: Female swine (N = 14) under intravenous anesthesia were instrumented with aortic and central venous micromanometer catheters. p-PEA was induced by ventilation with 6% O 2 in 94% N 2 , defined as a systolic aortic pressure less than 40 mmHg. Continuous EIT renderings were obtained from circumferential thoracic and abdominal electrode arrays. A deep learning model was utilized to detect p-PEA using EIT sequences. Twelve pigs were randomly selected as training data and 2 pigs as a test set. EIT images were saved as 30 second clips, resulting in 3033 clips generated. To increase generalizability, random epochs ranging from 30 - 100% of the total clip length were generated, resulting in a model capable of detecting this disease state with limited video fragments. Results: This technique yielded a receiver operator characteristic curve - area under the curve (ROC-AUC) of 0.91 for detection of p-PEA in the testing dataset (Figure 1), with 84% accuracy, 88% sensitivity, and 84% specificity. Conclusions: EIT combined with machine learning may be able to reliably delineate p-PEA in a hypoxic porcine model. This approach may be promising for non-invasive operator-independent p-PEA detection.