Journal of Clinical Monitoring and Computing最新文献

Tanaka et al. suggested that the increase in high-frequency variability index (HFVI) after peripheral nerve block (PNB) may result from autonomic effects mediated by the stellate ganglion block when an interscalene brachial plexus block (ISB) is performed. To investigate this, we analyzed patients receiving PNB with (ISB group; n = 8) or without ISB (non-ISB group; n = 22). HFVI increased significantly in both groups to a similar extent. These findings suggest that ISB's direct autonomic influence on HFVI is minimal. Additionally, laterality effects could not be assessed due to limited cases. We conclude that HFVI elevation is more likely due to PNB's analgesic effects rather than ISB's autonomic influence. Further studies are needed to confirm these findings.

Continuous vital signs monitoring with wearable systems may improve early recognition of patient deterioration on hospital wards. The objective of this study was to determine whether the wearable Checkpoint Cardio's CPC12S, can accurately measure heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), blood pressure (BP) and temperature continuously. In an observational multicenter method comparison study of 70 high-risk surgical patients admitted to high-dependency wards; HR, RR, SpO2, BP and temperature were simultaneously measured with the CPC12S system and with ICU-grade monitoring systems in four European hospitals. Outcome measures were bias and 95% limits of agreement (LoA). Clinical accuracy was assessed with Clarke Error Grid analyses for HR and RR. A total of 3,212 h of vital signs data (on average 26 h per patient) were analyzed. For HR, bias (95% LoA) of the pooled analysis was 0.0 (-3.5 to 3.4), for RR 1.5 (-3.7 to 7.5) and for SpO2 0.4 (-3.1 to 4.0). The CPC12S system overestimated BP, with a bias of 8.9 and wide LoA (-23.3 to 41.2). Temperature was underestimated with a bias of -0.6 and LoA of -1.7 to 0.6. Clarke Error Grid analyses showed that adequate treatment decisions regarding changes in HR and RR would have been made in 99.2% and 92.0% of cases respectively. The CPC12S system showed high accuracy for measurements of HR. The accuracy of RR, SpO2 were slightly overestimated and core temperature underestimated, with LoA outside the predefined clinical acceptable range. The accuracy of BP was unacceptably low.

The aim of this study was to compare the supramaximal currents obtained simultaneously by acceleromyography (AMG) TOF-Watch^® SX and electromyography (EMG) AF-201P in pediatric patients. Patients aged 2-11 years who underwent elective surgery under general anesthesia were enrolled. AMG and EMG monitors were placed on opposite arms of the patient to stimulate the ulnar nerve and monitor the adductor pollicis muscles following the induction of general anesthesia. After the monitors were calibrated, rocuronium 0.6 mg/kg was administered. Endotracheal intubation was performed when the EMG showed a train-of-four (TOF) count of 0. The primary outcome was the supramaximal current set by each device's calibration. The secondary outcomes were the time from the administration of rocuronium to a TOF count of 0, the baseline and final TOF ratios, and the conditions surrounding intubation. The paired t test and the Wilcoxon signed-rank test were used to analyze parametric and nonparametric data. A P value < 0.05 indicated statistical significance. Clinical data from 25 pediatric patients were analyzed. The supramaximal current was significantly lower with EMG than with AMG (27.4 ± 6.1 vs. 54.2 ± 8.4 mA, p <.001). The time to a TOF count of 0 was significantly shorter with EMG than with AMG (58 [24-170] vs. 75 [26-236] seconds, p <.001). Both the baseline and final TOF ratios were significantly lower with EMG than with AMG. The ease of laryngoscopy was rated as excellent for all patients. The vocal cords were abducted in 76% of patients, and no movement to insertion of the endotracheal tube was confirmed in 96% of patients. We found that the supramaximal current was significantly lower with EMG than with AMG.

This study explores the feasibility of continuous pulse wave velocity (PWV) monitoring during general anaesthesia (GA), particularly in response to blood pressure fluctuations. Our aim is to evaluate whether dynamic PWV can provide new insight to detect cardiovascular risks. From December 2022 to February 2023, continuous carotid and femoral Doppler monitoring was performed on patients scheduled for surgery with GA, to collect PWV data at awakening (PWV_AW) and during GA (PWV_GA). The study investigated PWV's response to MAP fluctuations using the α-angle, a dynamic stiffness parameter. We evaluated PWV and α-angle efficacy in discriminating between low (CVR-) and high (CVR+) cardiovascular risk patients. Among 43 patients, 41 (95%) had successful PWV measurements. PWV_AW was significantly higher than PWV_GA (8.1 vs. 7.4 m.s^-1, p < 0.0001). This difference vanished after matching MAP levels. A strong correlation was found between PWV_AW and PWV_GA (r = 0.88, and r = 0.97 at the same MAP levels). PWV_GA, α-angle and their product (α x PWV_GA) were significantly higher in CVR + patients (8.1 vs. 6.9 m.s^-1, p < 0.01; 2.6 vs. 1.3 degrees, p < 0.001; 21.8 vs. 8.1 degrees.m.s^-1, p < 0.001, respectively), with AUC values indicating good predictive capabilities for cardiovascular risk (PWV_GA: AUC [95%CI] = 0.80 [0.65-0.95]; α-angle: 0.83 [0.69-0.96]; product: 0.86 [0.74-0.97]). Measurement of PWV under GA using carotid and femoral Doppler is a feasible method to continuously assess arterial stiffness under general anaesthesia. Further studies are required to validate the α-angle parameter in different physiological conditions.

Despite advances made toward the automation of medical treatments, existing work has predominantly focused on the automation of individual medical treatments while overlooking interferences between them. In our prior work, we developed an automation architecture to administer interfering medical treatments with safety, which (i) monitors internal physiological state of a patient using an extended Kalman filter, (ii) mediates medical treatments to bound the estimated internal state within a prescribed safe regime based on control barrier functions, and (iii) treats the patient to a prespecified treatment target through a number of intermediate targets derived from reachability analysis. The goal of this work was to investigate the scalability of this automation architecture in more complex and challenging medical treatment scenarios with a number of conflicts. Using a critical care resuscitation scenario including fluid resuscitation and intravenous sedative-vasopressor infusion, we examined if our automation architecture can achieve treatment goals while ascertaining the safety of internal state in a large number of diverse in silico patients. The results suggested that (i) the extended Kalman filter could continuously monitor a patient's internal physiological state, (ii) the control barrier functions could mediate interfering medical treatments and protect patients against unsafe internal physiological state, and (iii) the reachability analysis could treat a patient as closely as possible to a treatment target while ensuring the safety of the patient's internal physiological state, all despite complex and entangled conflicts between them. Our automation architecture may provide a viable means to autonomously de-conflict interfering medical treatments.

Wearables and Internet of Things (IoT) technologies are increasingly incorporated into healthcare, including perioperative settings. These devices offer continuous non-invasive monitoring of vital signs, patient position, and mobilization. Nonetheless, there is currently little information about tolerance and acceptance of wearables in postoperative patients. We therefore assessed opinions and user experience in postoperative patients who used three popular fitness trackers during their entire hospital stay. Specifically, we evaluate the Apple Watch 7, Garmin Fenix 6 Pro, and Withings ScanWatch. We used an investigator-designed patient questionnaire with 11 questions to quantify patient experience and opinions regarding inpatient and home monitoring. Secondarily, we evaluated compliance and the incidence of associated adverse events during daily patient visits. Data were analyzed using descriptive statistics and non-parametric tests. The majority of the answers to the questions (82.1%) were rated positively defined as Likert-Scale Scores 4 or 5 by the 33 analyzed patients, ranging between 72.7 and 97.0% agreement rate. Specific questions related to data sharing for research and overall user experience received high agreement rates (97.0 and 84.8%, respectively). Women reported slightly higher satisfaction with device comfort, as compared to men (LS-Score 4.8 vs. 4.0). No significant differences were found based on the device model or length of hospitalization. The use of wearable devices in healthcare is rated positively by postoperative inpatients, paving the way for future implementation of these devices in healthcare. However, besides validating the measurement accuracy and demonstrating clinical benefits, several regulatory hurdles must be overcome before implementing wearables in routine clinical care.

The administration of propofol for sedation or general anesthesia presents challenges due to the complex relationship between patient factors and real-time physiological responses. This study explores the application of deep reinforcement learning (DRL) to automate propofol dosing, aiming to maintain multiple physiological parameters including bispectral index (BIS), heart rate (HR), respiratory rate (RR), and mean arterial pressure (MAP) within safe and desired ranges. A multi-variable pharmacokinetic-pharmacodynamic (PK/PD) simulation environment was developed to model the effects of propofol on the physiological parameters. An adjustable reward system was designed for multi-target anesthetic infusion. The DRL agent was trained using Twin Delayed Deep Deterministic Policy Gradient (TD3), interacting with the simulation environment and receiving rewards for maintaining physiological parameters close to their targets and above safety thresholds. The performance of the TD3 agent was compared to other DRL algorithms and traditional control methods. The TD3 algorithm demonstrated superior performance in achieving precise and safe control of multiple physiological parameters during propofol administration, outperforming other DRL algorithms and traditional control methods. The application of DRL, particularly TD3, offers a promising approach for automating propofol dosing, ensuring better management of physiological parameters and enhancing the safety and effectiveness of sedation and general anesthesia.