Journal of Clinical Monitoring and Computing最新文献

Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. Each group underwent two equal sequences of pH lowering with pre-defined targets by continuous infusion of lactic acid. The inputs to ANNs were pH, ΔPaCO₂ (variation of the arterial partial pressure of CO₂), PaO₂, and blood temperature which were sampled from the animal model. The output was the delta minute volume (ΔV_M), (the change of minute volume as compared to the minute volume the animal had at the beginning of the experiment). The ANN performance was analyzed using mean squared error (MSE), linear regression, and the Bland-Altman (B-A) method. The animal experiment provided the necessary data to train the ANN. The best architecture of ANN had 17 intermediate neurons; the best performance of the finally trained ANN had a linear regression with R² of 0.99, an MSE of 0.001 [L/min], a B-A analysis with bias ± standard deviation of 0.006 ± 0.039 [L/min]. ANNs can accurately estimate ΔV_M using the same information that arrives at the respiratory centers. This performance makes them a promising component for the future development of closed-loop artificial ventilators.

The review article "Haemodynamic Monitoring During Noncardiac Surgery" offers valuable insights but lacks evidence linking specific haemodynamic strategies to improved outcomes. There's a need for standardized protocols, ongoing clinician education, and further validation of new technologies. Additionally, balancing the use of invasive versus noninvasive methods and addressing cost-effectiveness and sustainability are essential. Continued research and adaptive practices are crucial for optimizing perioperative care.

Mechanomyography is currently the accepted laboratory reference standard for quantitative neuromuscular blockade monitoring. Mechanomyographs are not commercially available. Previously, a mechanomyograph was built by our laboratory and used in several clinical studies. It was subsequently redesigned to improve its usability and functionality and to accommodate a wider range of hand sizes and shapes using an iterative design process. Each version of the redesigned device was initially tested for usability and functionality in the lab with the investigators as subjects without electrical stimulation. The redesigned devices were then assessed on patients undergoing elective surgery under general anesthesia without neuromuscular blocking drugs. Since the patients were not paralyzed, the expected train-of-four ratio was 1.0. The device accuracy and precision were represented by the train-of-four ratio mean and standard deviation. If issues with the device's useability or functionality were discovered, changes were made, and the redesign processes repeated. The final mechanomyograph design was used to collect 2,362 train-of-four ratios from 21 patients. The mean and standard deviation of the train-of-four ratios were 0.99 ± 0.030. Additionally, the final mechanomyograph design was easier to use and adjust than the original design and fit a wider range of hand sizes. The final design also reduced the frequency of adjustments and the time needed for adjustments, facilitating data collection during a surgical procedure.

Post-induction hypotension (MAP < 65 mmHg) occurs frequently and is usually caused by the cardiovascular adverse effects of the anaesthetic induction drugs used. We hypothesize that a clinically significant difference in the incidence and severity of hypotension will be found when different doses of propofol and remifentanil are used for induction of anaesthesia.

Methods: This is a secondary analysis of a randomised controlled trial wherein four groups (A-D) of patients received one out of four different combinations of propofol and remifentanil, titrated to a predicted equipotency in probability of tolerance to laryngoscopy (PTOL) according to the Bouillon interaction model. In group A, a high dose of propofol and a low dose of remifentanil was administered, and across the groups this ratio was gradually changed until it was reversed in group D. Mean and systolic arterial blood pressure (MAP, SAP) were compared at four time points (T_baseline, T_post-bolus, T_3min, T_nadir) within and between groups Heart rate, bispectral index (BIS) and the incidence of hypotension were compared.

Results: Data from 76 patients was used. At T_post-bolus a statistically significant lower MAP and SAP was found in group A versus D (p = 0.011 and p = 0.002). A significant higher heart rate was found at T_3min and T_nadir between groups A and B when compared to groups C and D (p = < 0.001 and p = 0.002). A significant difference in BIS value was found over all groups at T_3min and T_nadir (both p < 0.001). All other outcomes did not differ significantly between groups.

Conclusion: Induction of anaesthesia with different predicted equipotent combinations of propofol and remifentanil did result in statistically different but clinically irrelevant differences in haemodynamic endpoints during induction of anaesthesia. Our study could not identify preferable drug combinations that decrease the risk for hypotension after induction, although they all yield a similar predicted PTOL.

Technological advances allow continuous vital sign monitoring at the general ward, but traditional vital signs alone may not predict serious adverse events (SAE). This study investigated continuous heart rate variability (HRV) monitoring's predictive value for SAEs in acute medical and major surgical patients. Data was collected from four prospective observational studies and two randomized controlled trials using a single-lead ECG. The primary outcome was any SAE, secondary outcomes included all-cause mortality and specific non-fatal SAE groups, all within 30 days. Subgroup analyses of medical and surgical patients were performed. The primary analysis compared the last 24 h preceding an SAE with the last 24 h of measurements in patients without an SAE. The area under a receiver operating characteristics curve (AUROC) quantified predictive performance, interpretated as low prognostic ability (0.5-0.7), moderate prognostic ability (0.7-0.9), or high prognostic ability (> 0.9). Of 1402 assessed patients, 923 were analysed, with 297 (32%) experiencing at least one SAE. The best performing threshold had an AUROC of 0.67 (95% confidence interval (CI) 0.63-0.71) for predicting cardiovascular SAEs. In the surgical subgroup, the best performing threshold had an AUROC of 0.70 (95% CI 0.60-0.81) for neurologic SAE prediction. In the medical subgroup, thresholds for all-cause mortality, cardiovascular, infectious, and neurologic SAEs had moderate prognostic ability, and the best performing threshold had an AUROC of 0.85 (95% CI 0.76-0.95) for predicting neurologic SAEs. Predicting SAEs based on the accumulated time below thresholds for individual continuously measured HRV parameters demonstrated overall low prognostic ability in high-risk hospitalized patients. Certain HRV thresholds had moderate prognostic ability for prediction of specific SAEs in the medical subgroup.

Background: Postoperative sore throat (POST) is a common complication following endotracheal tube removal, and effective preventive strategies remain elusive. This trial aimed to determine whether actively regulating intraoperative cuff pressure below the tracheal capillary perfusion pressure threshold could effectively reduce POST incidence in patients undergoing gynecological laparoscopic procedures.

Methods: This single-center, randomized controlled superiority trial allocated 60 patients scheduled for elective gynecological laparoscopic procedures into two groups: one designated for cuff pressure measurement and adjustment (CPMA) group, and a control group where only cuff pressure measurement was conducted without any subsequent adjustments. The primary outcome was POST incidence at rest within 24 h post-extubation. Secondary outcomes included cough, hoarseness, postoperative nausea and vomiting (PONV) incidence, and post-extubation pain severity.

Results: The incidence of sore throat at rest within 24 h after extubation in the CPMA group was lower than in the control group, meeting the criteria for statistically significant superiority based on a one-sided test (3.3% vs. 26.7%, P < 0.025). No statistically significant differences were observed in cough, hoarseness, or pain scores within 24 h post-extubation between the two groups. However, the CPMA group had a higher incidence of PONV compared to the control group. Additionally, the control group reported higher sore throat severity scores within 24 h post-extubation.

Conclusions: Continuous monitoring and maintenance of tracheal tube cuff pressure at 18 mmHg were superior to merely monitoring without adjustment, effectively reducing the incidence of POST during quiet within 24 h after tracheal tube removal in gynecological laparoscopic surgery patients.

Trial registration: The study was registered at www.chictr.org.cn (ChiCTR2200064792) on 18/10/2022.

To determine how percutaneous tracheostomy (PT) impacts on respiratory system compliance (C_rs) and end-expiratory lung volume (EELV) during volume control ventilation and to test whether a recruitment maneuver (RM) at the end of PT may reverse lung derecruitment. This is a single center, prospective, applied physiology study. 25 patients with acute brain injury who underwent PT were studied. Patients were ventilated in volume control ventilation. Electrical impedance tomography (EIT) monitoring and respiratory mechanics measurements were performed in three steps: (a) baseline, (b) after PT, and (c) after a standardized RM (10 sighs of 30 cmH₂O lasting 3 s each within 1 min). End-expiratory lung impedance (EELI) was used as a surrogate of EELV. PT determined a significant EELI loss (mean reduction of 432 arbitrary units p = 0.049) leading to a reduction in C_rs (55 ± 13 vs. 62 ± 13 mL/cmH₂O; p < 0.001) as compared to baseline. RM was able to revert EELI loss and restore C_rs (68 ± 15 vs. 55 ± 13 mL/cmH₂O; p < 0.001). In a subgroup of patients (N = 8, 31%), we observed a gradual but progressive increase in EELI. In this subgroup, patients did not experience a decrease of C_rs after PT as compared to patients without dynamic inflation. Dynamic inflation did not cause hemodynamic impairment nor raising of intracranial pressure. We propose a novel and explorative hyperinflation risk index (HRI) formula. Volume control ventilation did not prevent the PT-induced lung derecruitment. RM could restore the baseline lung volume and mechanics. Dynamic inflation is common during PT, it can be monitored real-time by EIT and anticipated by HRI. The presence of dynamic inflation during PT may prevent lung derecruitment.

Purpose: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms.

Methods: An open-source database of non-cardiac surgery patients ( https://vitadb.net/dataset ) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC).

Results: Data from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5 min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915-0.918) for the original data and 0.833 (95% CI, 0.830-0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension.

Conclusions: A deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring.

The potential use of TEG/ROTEM® in evaluating the bleeding risk for rare coagulation disorders needs to be assessed, considering the common mismatch among laboratory tests and the clinical manifestations. As a result, there is currently no published data on the use of viscoelastic tests to assess coagulation in FVII deficient patients undergoing elective neurosurgery. We describe the case of a patient affected by severe FVII deficiency who underwent microvascular decompression (MVD) craniotomy for hemifacial spasm (HFS). The ROTEM® did not show a significant coagulopathy according to the normal ranges, before and after the preoperative administration of the recombinant activated FVII, but a substantial reduction in EXTEM and FIBTEM Clotting Times was noted. The values of coagulation in standard tests, on the contrary, were indicative of a coagulopathy, which was corrected by the administration of replacement therapy. Whether this difference between ROTEM® and standard tests is due to the inadequacy of thromboelastographic normal ranges in this setting, or to the absence of clinically significant coagulopathy, has yet to be clarified. Neurosurgery is a typical high bleeding risk surgery; additional data is required to clarify the potential role for thromboelastographic tests in the perioperative evaluation of the FVII deficient neurosurgical patients.

Purpose: This study tests if the pressure variation in the HFNC-system may allow for monitoring of respiratory rate and the pressure difference during breathing may be a marker of respiratory effort.

Methods: A HFNC system (Fisher & Paykel Optiflow Thrive 950) was modified by adding a GE Healthcare D-Lite spirometry sensor attached to a respiratory module and a pressure transducer. Participants were instructed to breathe regularly, quickly and slowly during 4 different conditions (HFNC flow 30 l/min and 70 l/min and with an open and closed mouth). Respiratory rate was counted based on pressure variation shown on the monitor graphs and compared with the count by observation of the participant. The pressure difference between inspiration and expiration was tested for correlation with the respiratory rate, as a surrogate marker for respiratory effort.

Results: Twenty five participants were included in this study. False detection of apnea in pressure-based measurements occurred in 10% and 11% of the measurements with open mouth position at 30 l/min and 70 l/min HFNC-flow, respectively, but not with a closed mouth. The 95% Limits of Agreement were - 1.85;1.91, -13.72;9,88, -2.25;2.47, -30.32;19.93 for the conditions of 30 l/min -closed mouth, 30 l/min - open mouth, 70 l/min - closed mouth and 70 l/min - open mouth, respectively. There was a correlation between pressure difference and respiratory effort, except for the condition of 30 l/min with open mouth.

Conclusions: The pressure variation in the HFNC system allows for respiratory rate and effort monitoring, but requires further development to increase precision.

Trial registration: ClinicalTrials.gov (NCT05991843).