Perioperative advanced haemodynamic monitoring of patients undergoing multivisceral debulking surgery: an observational pilot study.

BACKGROUND: Patients undergoing high-risk surgery show haemodynamic instability and an increased risk of morbidity. However, most of the available data concentrate on the intraoperative period. This study aims to characterise patients with advanced haemodynamic monitoring throughout the whole perioperative period using electrical cardiometry. METHODS: In a prospective, observational, monocentric pilot study, electrical cardiometry measurements were obtained using an Osypka ICON™ monitor before surgery, during surgery, and repeatedly throughout the hospital stay for 30 patients with primary ovarian cancer undergoing multivisceral cytoreductive surgery. Severe postoperative complications according to the Clavien-Dindo classification were used as a grouping criterion. RESULTS: The relative change from the baseline to the first intraoperative timepoint showed a reduced heart rate (HR, median - 19 [25-quartile - 26%; 75-quartile - 10%]%, p < 0.0001), stroke volume index (SVI, - 9.5 [- 15.3; 3.2]%, p = 0.0038), cardiac index (CI, - 24.5 [- 32; - 13]%, p < 0.0001) and index of contractility (- 17.5 [- 35.3; - 0.8]%, p < 0.0001). Throughout the perioperative course, patients had intraoperatively a reduced HR and CI (both p < 0.0001) and postoperatively an increased HR (p < 0.0001) and CI (p = 0.016), whereas SVI was unchanged. Thoracic fluid volume increased continuously versus preoperative values and did not normalise up to the day of discharge. Patients having postoperative complications showed a lower index of contractility (p = 0.0435) and a higher systolic time ratio (p = 0.0008) over the perioperative course in comparison to patients without complications, whereas the CI (p = 0.3337) was comparable between groups. One patient had to be excluded from data analysis for not receiving the planned surgery. CONCLUSIONS: Substantial decreases in HR, SVI, CI, and index of contractility occurred from the day before surgery to the first intraoperative timepoint. HR and CI were altered throughout the perioperative course. Patients with postoperative complications differed from patients without complications in the markers of cardiac function, a lower index of contractility and a lower SVI. The analyses of trends over the whole perioperative time course by using non-invasive technologies like EC seem to be useful to identify patients with altered haemodynamic parameters and therefore at an increased risk for postoperative complications after major surgery.

GRU Neural Network Improved Bioimpedance Based Stroke Volume Estimation during Ergometry Stress Test.

Cardiovascular diseases (CVDs) are one of the leading members of non-communicable diseases. An early diagnosis is essential for effective treatment, to reduce hospitalization time and health care costs. Nowadays, an exercise stress test on an ergometer is used to identify CVDs. To improve the accuracy of diagnostics, the hemodynamic status and parameters of a person can be investigated. For hemodynamic management, thoracic electrical bioimpedance has recently been used. This technique offers beat-to-beat stroke volume calculation but suffers from an artifact-sensitive signal that makes such measurements difficult during movement. We propose a new method based on a gated recurrent unit (GRU) neural network and the ECG signal to improve the measurement of bioimpedance signals, reduce artifacts and calculate hemodynamic parameters. We conducted a study with 23 subjects. The new approach is compared to ensemble averaging, scaled Fourier linear combiner, adaptive filter, and simple neural networks. The GRU neural network performs better with single artifact events than shallow neural networks (mean error -0.0244, mean square error 0.0181 for normalized stroke volume). The GRU network is superior to other algorithms using time-correlated data for the exercise stress test.

Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia.

Compensated shock and hypovolaemia are frequent conditions that remain clinically undetected and can quickly cause deterioration of perioperative and critically ill patients. Automated, accurate and non-invasive detection methods are needed to avoid such critical situations. In this experimental study, we aimed to create a prediction model for stroke volume index (SVI) decrease based on electrical cardiometry (EC) measurements. Transthoracic echo served as reference for SVI assessment (SVI-TTE). In 30 healthy male volunteers, central hypovolaemia was simulated using a lower body negative pressure (LBNP) chamber. A machine-learning algorithm based on variables of EC was designed. During LBNP, SVI-TTE declined consecutively, whereas the vital signs (arterial pressures and heart rate) remained within normal ranges. Compared to heart rate (AUC: 0.83 (95% CI: 0.73-0.87)) and systolic arterial pressure (AUC: 0.82 (95% CI: 0.74-0.85)), a model integrating EC variables (AUC: 0.91 (0.83-0.94)) showed a superior ability to predict a decrease in SVI-TTE ≥ 20% (p = 0.013 compared to heart rate, and p = 0.002 compared to systolic blood pressure). Simulated central hypovolaemia was related to a substantial decline in SVI-TTE but only minor changes in vital signs. A model of EC variables based on machine-learning algorithms showed high predictive power to detect a relevant decrease in SVI and may provide an automated, non-invasive method to indicate hypovolaemia and compensated shock.

Surrogate based continuous noninvasive blood pressure measurement.

Arterial blood pressure is one of the most often measured vital parameters in clinical practice. State-of-the-art noninvasive ABP measurement technologies have noticeable limitations and are mainly based on uncomfortable techniques of complete or partial arterial occlusion by cuffs. Most commonplace devices provide only intermittent measurements, and continuous systems are bulky and difficult to apply correctly for nonprofessionals. Continuous cuffless ABP measurements are still an unmet clinical need and a topic of ongoing research, with only few commercially available devices. This paper discusses surrogate-based noninvasive blood pressure measurement techniques. It covers measurement methods of continuously and noninvasively inferring BP from surrogate signals without applying external pressures, except for reference or initialization purposes. The BP is estimated by processing signal features, so called surrogates, which are modulated by variations of BP. Discussed techniques include well-known approaches such as pulse transit time and pulse arrival time techniques, pulse wave analysis or combinations thereof. Despite a long research history, these methods have not found widespread use in clinical and ambulatory practice, in part due to technical limitations and the lack of a standardized regulatory framework. This work summarizes findings from an invited workshop of experts in the fields covering clinical expertise, engineering aspects, commercialization and standardization issues. The goal is to provide an application driven outlook, starting with clinical needs, and extending to technical actuality. It provides an outline of recommended research directions and includes a detailed overview of clinical use case scenarios for these technologies, opportunities, and limitations.

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram.

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.

Balanced Adjustable Mirrored Current Source with Common Mode Feedback and Output Measurement for Bioimpedance Applications.

Bioimpedance methods are used in a variety of applications such as impedance tomography, electrodermal activity detection and vascular disease assessment. Recent developments in portable and unobtrusive biosignal acquisition systems facilitate the integration of wearable bioimpedance applications including sleep monitoring, respiration estimation and fluid monitoring. However, the less stable measurement situation in a wearable scenario increases the requirements for the system's accuracy and adaptability. The current source of a bioimpedance system needs to drive large complex loads subject to vast variations over time while maintaining a high level of accuracy. The widely used improved Howland current source suffers from multiple disadvantages when considered for an adaptive bioimpedance system. We propose an optimized mirrored architecture which allows for a simple output current adjustment and current measurement without an additional shunt resistor in the load path. The system implements a common mode feedback system which includes balancing of the mirrored sources. Our design is validated by calculation, SPICE simulation and complex load measurements. We achieved output impedances in excess of 3 MΩ and derived a simplified transconductance function valid for frequencies up to 1 MHz. We conclude that the presented architecture is an important step forward towards accurate wearable bioimpedance acquisition. Employing generalized impedance converters, the output impedance could be further optimized.

Wearable Multimodal Stethoscope Patch for Wireless Biosignal Acquisition and Long-Term Auscultation.

Detecting critical events in postoperative care and improving comfort, costs and availability in sleep assessment are two of many areas in which wearable biosignal acquisition can be a viable tool. Modern sensors as well as patch and textile integration facilitate unobtrusive biosignal acquisition, yet placing sensors at different locations across the body is still prevailing. Actigraphy and the electrocardiogram (ECG) are commonly integrated modalities. The stethoscope however, despite its wide range of applications, has been neglected from these developments. The introduction of digital stethoscopes, recently led to an objectification and increased interest in the field. We present the prototype of a wearable, Bluetooth 5.0 LE enabled multimodal sensor patch combining five modalities: MEMS stethoscope, ambient noise sensing, ECG, impedance pneumography (IP) and 9-axial actigraphy. The system alleviates the need for sensors at different body positions and enables long-term auscultation. Using high sampling rates and online synchronization, multimodal sensor fusion becomes feasible. The patch measures 70 mm x 60 mm and is attached using three 24 mm Ag/AgCl electrodes. High quality cardiac and pulmonary auscultation as well as ECG and IP acquisition are demonstrated. We derived respiration surrogates with linear correlations to a reference exceeding 0.91 and conclude that the system can be utilized in fields requiring unobtrusive yet high quality signal acquisition. Future research will include the integration of additional sensors and further size reduction.