Journal of Clinical Monitoring and Computing最新文献

Purpose: Patients undergoing cardiothoracic surgery are at risk of developing perioperative stroke, but residual effects of anesthesia may hamper timely detection. This study aims to determine if there is an association between intraoperative regional cerebral oxygenation (ScO₂) monitoring using near-infrared spectroscopy (NIRS) and the occurrence of early perioperative stroke within three days after cardiothoracic surgery.

Methods: We performed a single-center retrospective observational cohort study including all consecutive cardiothoracic surgery patients with routinely perioperative ScO₂ monitoring admitted postoperatively to the Intensive Care Unit (ICU) between 2008 and 2017. Patients with a confirmed stroke in the anterior cerebral circulation on brain imaging were included in the analysis. Intraoperative area under the curve (AUC), duration, and total ScO₂ excursions below predefined thresholds (< 50% ScO₂ or > 20% reduction below baseline) were calculated for each hemisphere. Stroke-affected and non-affected hemispheres were compared using logistic regression analyses to investigate a potential association between ScO₂ values and stroke.

Results: Of the 2454 cardiothoracic surgery patients with perioperative ScO₂ monitoring, 39 had a anterior stroke on brain imaging. ScO₂ readings of 44 affected hemispheres were compared to 34 non-affected hemispheres. Only the duration of ScO₂ < 50% or a > 20% drop from baseline were significantly associated with global ischemia (OR 1.30 (0.95%CI; 1.09-2.30)) when comparing affected (72 [5 to 33] min.) versus non-affected (28 [4 to 44] min.) hemispheres.

Conclusion: The duration of ScO₂ values < 50% or a drop > 20% from baseline were associated with the occurrence of early perioperative global cerebral ischemia within three days after cardiothoracic surgery.

An effect-site target-controlled infusion (TCI) would allow a more precise titration of intravenous analgesics effect. The analgesia nociception index (ANI) continuously monitors the analgesia/nociception balance during general anesthesia. This study aims to derive a PKPD model of ketamine antinociceptive effect using the Domino PK parameter set and the ANI response data in awake patients without other drugs affecting the ANI response. Twenty awake adult patients were prospectively studied before general anesthesia. Patients received a single intravenous bolus of ketamine 0.1 mg·kg^- 1, and the subsequent ANI values were recorded. An effect compartment model incorporating the Domino PK parameter set was used to characterize the time lag between ketamine plasma concentrations and the ANI response. The model was parameterized with a single parameter Ke0. An Emax pharmacodynamic model was used to fit the ANI response data. Model parameters were estimated with NONMEM^® 7.5. The minimum objective function value guided the model construction. After the ketamine administration, basal ANI values increased from 38.5 ± 4.95 to a maximum of 53.5 ± 4.95 with an observed time-to-peak effect of 1.83 ± 0.74 min. Modeling analysis revealed hysteresis between predicted plasma concentrations from the Domino model and observed ANI data. Hysteresis was characterized, incorporating an estimated Keo of 0.238 (CI95% 0.20-0.28) min-1 to the described PK parameters set. The developed PKPD model, using Domino's PK parameters and the ANI response data, adequately characterized the temporal profile of ketamine's antinociceptive effect. The current estimated model parameters can be used to perform an effect-site TCI of ketamine for analgesic purposes.

The early detection of clinical deterioration could be the next significant step in enhancing patient safety in general hospital wards. Most patients do not deteriorate suddenly; instead, their vital signs are often abnormal or trending towards an abnormal range hours before severe adverse events requiring rescue intervention and/or ICU transfer. To date, at least 10 large clinical studies have demonstrated a significant reduction in severe adverse events when heart rate, blood pressure, oxygen saturation and/or respiratory rate are continuously monitored on medical and surgical wards. Continuous, silent, and automatic monitoring of vital signs also presents the opportunity to eliminate unnecessary spot-checks for stable patients. This could lead to a reduction in nurse workload, while significantly improving patient comfort, sleep quality, and overall satisfaction. Wireless and wearable sensors are particularly valuable, as they make continuous monitoring feasible even for ambulatory patients, raising questions about the future relevance of "stay-in-bed" solutions like capnography, bed sensors, and video-monitoring systems. While the number of wearable sensors and mobile monitoring solutions is rapidly growing, independent validation studies on their sensitivity and specificity in detecting abnormal vital signs in actual patients, rather than healthy volunteers, remain limited. Additionally, further research is needed to evaluate the cost-effectiveness of using wireless wearables for vital sign monitoring both within hospital wards and at home.

Patient monitoring in the perioperative setting can be challenging, especially when monitoring multiple patients simultaneously or managing dynamic situations that require movement around the operating room. We aimed to evaluate whether avatar-based patient monitoring, which presents vital signs in the form of changing colors, shapes and motion, improves remote vital sign recognition compared to conventional monitoring. We conducted a prospective, single-center, computer-based simulation study to evaluate how anesthesia providers recognize vital signs when using the Philips Visual Patient Avatar at different viewing distances (8 and 16 m) compared to conventional monitoring. The primary outcome was the total number of correctly identified vital signs which were compared for the two distances and the two devices using mixed Poisson regression. We analyzed data from 28 anesthesia providers who participated in 112 simulations. The correct recognition rate using the Visual Patient Avatar compared to conventional monitoring at 8 m was increased by 74% (rate ratio 1.74, 95% CI, 1.42 to 2.14, p < 0.001) and by 51% at 16-meter viewing distance (rate ratio 1.51, 95% CI, 1.23 to 1.87, p < 0.001). We observed scenario-specific superior performance for six vital signs at 8 m. The results provide empirical evidence that avatar-based monitoring can significantly improve the perception of vital signs when using distant vision.

This study investigated the clinical and physiological response to use of the BEACON Caresystem, a bedside open-loop decision support system providing advice to guide clinicians when weaning patients from invasive mechanical ventilation. Multicenter prospective study conducted in five adult intensive care units in the UK. Following screening and assent, intubated patients mechanically ventilated for > 24 h were randomized to intervention or usual care. Intervention consisted of application of the BEACON Caresystem's advice on tidal volume/inspiratory pressure, inspired oxygen, respiratory rate and PEEP. Usual care was defined as local clinical practice. The primary outcome was duration of mechanical ventilation. Secondary outcomes quantified prolonged intubation and survival; adverse events; ventilator settings and physiological state; time spent in ventilator modes; links to other therapy; the frequency of advice utilization and time spent outside normal physiological limits. The study was terminated early with a total of 112 patients included. Fifty-four were randomised to the intervention arm and fifty-eight to usual care. The study was underpowered and no significant differences were seen in duration of mechanical ventilation (p = 0.773), prolonged intubation or survival. Intervention arm patients had lower rates of adverse events (p = 0.016), including fewer hypoxaemic events (p = 0.008) and lower values of PEEP (p = 0.030) and tidal volume (p = 0.042). Values of peak inspiratory pressure and pressure support were reduced but at the boarder of statistical significance (p = 0.104, p = 0.093, respectively). No differences were seen for time in ventilator mode or other therapy. Advice presented by the decision support system was applied at the beside an average of 88% of occasions, with a significantly increased number of changes only in inspired oxygen fraction. No significant differences were seen in time spent outside physiological limits. This study investigated the use of the BEACON Caresystem, an open loop clinical decision support system providing advice on ventilator settings. It was terminated early, with no significant difference shown in duration of mechanical ventilation, the primary outcome. Application of advice indicated potential for fewer adverse events and improved physiological status. (Trial registration ClinicalTrials.gov under NCT03249623. Registered 22nd June 2017).

This study presents a comprehensive comparison of multiple time-series models applied to physiological metric predictions. It aims to explore the effectiveness of both statistical prediction models and pharmacokinetic-pharmacodynamic prediction model and modern deep learning approaches. Specifically, the study focuses on predicting the bispectral index (BIS), a vital metric in anesthesia used to assess the depth of sedation during surgery, using datasets collected from real-life surgeries. The goal is to evaluate and compare model performance considering both univariate and multivariate schemes. Accurate BIS prediction is essential for avoiding under- or over-sedation, which can lead to adverse outcomes. The study investigates a range of models: The traditional mathematical models include the pharmacokinetic-pharmacodynamic model and statistical models such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR). The deep learning models encompass recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), as well as Temporal Convolutional Networks (TCNs) and Transformer models. The analysis focuses on evaluating model performance in predicting the BIS using two distinct datasets of physiological metrics collected from actual surgical procedures. It explores both univariate and multivariate prediction schemes and investigates how different combinations of features and input sequence lengths impact model accuracy. The experimental findings reveal significant performance differences among the models: In univariate prediction scenarios for predicting BIS, the LSTM model demonstrates a 2.88% improvement over the second-best performing model. For multivariate predictions, the LSTM model outperforms others by 6.67% compared to the next best model. Furthermore, the addition of Electromyography (EMG) and Mean Arterial Pressure (MAP) brings significant accuracy improvement when predicting BIS. The study emphasizes the importance of selecting and building appropriate time-series models to achieve accurate predictions in biomedical applications. This research provides insights to guide future efforts in improving vital sign prediction methodologies for clinical and research purposes. Clinically, with improvements in the prediction of physiological parameters, clinicians can be informed of interventions if an anomaly is detected or predicted.

This single-center study explored the efficacy of superficial parasternal intercostal plane block (SPIPB) versus erector spinae plane block (ESPB) in opioid-sparing within Nociception Level (NOL) index-directed anesthesia for elective open-heart surgery. After targeted propensity matching, 19 adult patients given general anesthesia with preincisional SPIPB were compared to 33 with preincisional ESPB. We hypothesized that SPIPB is non-inferior to ESPB in reducing total intraoperative fentanyl consumption, with a non-inferiority margin (δ) set at 0.1 mg. Intraoperative fentanyl dosing targeted a NOL index ≤ 25. Postoperatively, paracetamol 1 g 6-hourly and morphine for numeric rating scale (NRS) ≥ 4 were administered. This study could not demonstrate that SPIPB was inferior to ESPB for total intraoperative fentanyl consumption, as the confidence interval for the median difference of 0.1 mg (95% CI 0.05-0.15) crossed the predefined δ, with the lower bound falling below and the upper bound exceeding δ, p = 0.558. SPIPB led to higher postoperative morphine use at 24 and 48 h: 0 (0-40.6) vs. 59.5 (28.5-96.1) µg kg^-1, p < 0.001 and 22.2 (0-42.6) vs. 63.5 (28.5-96.1) µg kg^-1, p = 0.001. Four times fewer SPIPB patients remained morphine-free at 48 h, p < 0.001, and their time to first morphine dose was three times shorter compared to ESPB patients, p = 0.001. SPIPB led to higher time-weighted average NRS scores at rest, 1 (0-1) vs. 1 (1-2), p = 0.004, and with movement, 2 (1-2) vs. 3 (2-3), p = 0.002, calculated over the 48-h period post-extubation. The SPIPB group had a significantly higher average NOL index, p = 0.003, and greater NOL index variability, p = 0.027. This study could not demonstrate that SPIPB was inferior to ESPB for intraoperative fentanyl consumption. Significant differences were observed in secondary outcomes, with SPIPB leading to higher postoperative morphine use, higher pain scores, and reduced nociception control.

Quantitative neuromuscular monitoring reduces the incidence of residual neuromuscular block, but broad acceptance of monitoring has been elusive despite recommendations for quantitative monitoring for decades. Acceptance of quantitative monitoring may, in part, be related to the quality of the data from monitoring systems. This evaluation explored proper stimulating electrode positioning for electromyographic (EMG) monitoring, the impact of an educational intervention on electrode positioning and anesthesia provider (anesthesiologist, resident, anesthetist) confidence in the monitoring data from the device. In a single-center, observations of EMG electrode placement by anesthesia technicians, in 55 adult elective surgery patients were made by an independent observer. Separately, the anesthesia provider satisfaction with EMG derived data was recorded after reversal of neuromuscular block. An educational intervention then occurred on proper electrode positioning, including prior observations of electrode positioning, and prior anesthesia provider satisfaction with the EMG derived data. After the intervention, stimulating electrode position was observed with an additional 60 patients and anesthesia provider satisfaction with the data was again ascertained. The educational intervention significantly increased the proportion of ideal ulnar nerve groove electrode positioning from 74.5% to 95% (P < 0.003) and ideal wrist crease positioning (distal electrode within 2 cm of crease) from 61.8% to 96.7% (P < 0.001). Anesthesia provider confidence with EMG derived information during anesthesia delivery, increased from 67 to 90% after the education (P = 0.005). There was a significant relationship between correct stimulating electrode placement and anesthesia provider confidence in the EMG derived data on neuromuscular block status. An educational intervention to improve EMG electrode positioning proved meaningful. It increased anesthesia provider confidence in the EMG derived data during anesthesia case management.

Automated EMG devices to detect compound muscle action potentials from the adductor pollicis muscle in response to ulnar nerve stimulation, regardless of hand and thumb position, may serve as a better reference ("gold standard") for clinical assessment of neuromuscular function than traditional mechanomyography (MMG) systems that need custom design and validation in lab settings. This evaluation compared the TetraGraph EMG system against a validated MMG device to investigate the accuracy and repeatability of this quantitative EMG monitor for detecting onset, offset and deep neuromuscular block. Simultaneous muscle action potential recordings from the EMG neuromuscular monitor and muscle contractions from an in-house developed MMG monitor in response to ulnar nerve stimulation were obtained from patients having elective surgery requiring neuromuscular block. Train-of-four (TOF) ratios, TOF counts, and post-tetanic counts (PTCs) were recorded simultaneously from the same hand muscle and compared. In total, 685 pairs of simultaneous TOF ratios were evaluated. The mean difference (bias) of TOF ratios between devices was small (- 2.1%). TOF counts from 285 data pairs were within a count of 2 or less 96% of the time. During deep block, PTC comparisons from 215 data pairs were within a count of 2 or less 95% of the time. These findings, along with prior EMG device evaluations, indicate that real-time EMG neuromuscular monitoring technology to detect muscle action potentials from the adductor pollicis in the clinical setting is closely aligned with the force of thumb contraction determined from MMG. The accuracy of quantitative EMG technology of the TetraGraph EMG system lends strong support for this monitor, along with other similarly validated EMG monitors, to become a clinical standard for all phases (onset, depth and reversal) of neuromuscular block in clinical practice.