Journal of Clinical Monitoring and Computing最新文献

Both tissue hypoperfusion and elevated surgical stress during surgery are involved in the pathogenesis of postoperative acute kidney injury (AKI). Although intraoperative hypotension, which evokes renal hypoperfusion, has been reported to be associated with the development of postoperative AKI, there is no consensus on the association between surgical stress responses (e.g., hypertension and inflammation) and postoperative AKI. Given that intraoperative values of nociceptive response (NR) index are reportedly associated with surgical stress responses, the present study was performed to assess associations between intraoperative NR index and postoperative AKI in patients undergoing non-cardiac surgery. In this single-institutional retrospective cohort study, data of the highest and lowest values of NR index during surgery were obtained in consecutive adult patients undergoing non-cardiac surgery under general anesthesia from February 2022 to August 2023. Data on highest and lowest mean blood pressure (MBP) during surgery were also obtained. In 5,765 patients enrolled, multivariate regression analysis revealed that the development of early postoperative AKI was significantly associated with highest NR during surgery ≥ 0.920, lowest MBP during surgery < 54 mmHg, age ≥ 48 years, male sex, ASA-PS ≥ III, emergency, and duration of surgery ≥ 226 min. In addition to intraoperative hypotension, a higher level of intraoperative NR index is likely associated with higher incidence of early postoperative AKI in adult patients undergoing non-cardiac surgery under general anesthesia.

Purpose: Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension.

Methods: A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies.

Results: 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were - 0.21 (95% CI:-0.33, -0.09) - p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and - 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies.

Conclusions: While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.

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.

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.

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.

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.