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. 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). 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 5min 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. 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.

Airway closure is a interruption of communication between larger and smaller airways. The presence of airway closure during mechanical ventilation may lead to the overestimation of driving pressure (DP), introducing errors in the assessment of respiratory mechanics and in positive end-expiratory pressure (PEEP) setting on the ventilator. Patients with severe acute respiratory distress syndrome (ARDS) may exhibit the airway closure phenomenon, which can be easily diagnosed with a low-flow inflation. Prone positioning is a therapeutic manoeuver proven to reduce mortality in ARDS patients, and has been widely implemented also in patients requiring veno-venous extracorporeal membrane oxygenation (V-V ECMO). To date, the impact of prone positioning on changes in airway closure has not been described. We present an image analysis of the pressure waveform during volume-controlled ventilation and low-flow inflations before and after prone positioning in an ARDS patient on VV ECMO. A high airway opening pressure level (23 cmH2O) was detected in the supine position during tidal ventilation. Airway closure was confirmed by using a low-flow inflation. Prone positioning significantly attenuated airway closure, with the airway opening pressure decreasing to 13 cmH2O. After re-supination, airway closure was lower as compared with supine position at baseline (17 cmH2O). Prone positioning reduced airway closure in an ARDS patient on VV ECMO support.

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 (Tbaseline, Tpost−bolus, T3min, Tnadir) within and between groups Heart rate, bispectral index (BIS) and the incidence of hypotension were compared. Results: Data from 76 patients was used. At Tpost−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 T3min and Tnadir 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 T3min and Tnadir (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.

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.

To determine how percutaneous tracheostomy (PT) impacts on respiratory system compliance (Crs) 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 3s each within 1min). 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 Crs (55 ± 13 vs. 62 ± 13 mL/cmH2O; p < 0.001) as compared to baseline. RM was able to revert EELI loss and restore Crs (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 Crs 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.

The purpose of this in vitro study was to evaluate the impact of the vertical level of the stopcock connecting the infusion line to the central venous catheter on start-up fluid delivery in microinfusions. Start-up fluid delivery was measured under standardized conditions with the syringe outlet and liquid flow sensors positioned at heart level (0 cm) and exposed to a simulated CVP of 10 mmHg at a set flow rate of 1 ml/h. Flow and intraluminal pressures were measured with the infusion line connected to the stopcock primarily placed at vertical levels of 0 cm, + 30 cm and − 30 cm or primarily placed at 0 cm and secondarily, after connecting the infusion line, displaced to + 30 cm and − 30 cm. Start-up fluid delivery 10 s after opening the stopcock placed at zero level and after opening the stopcock primarily connected at zero level and secondary displaced to vertical levels of + 30 cm and – 30 cm were similar (− 10.52 [− 13.85 to − 7.19] µL; − 8.84 [− 12.34 to − 5.33] µL and − 11.19 [− 13.71 to − 8.67] µL (p = 0.469)). Fluid delivered at 360 s related to 65% (zero level), 71% (+ 30 cm) and 67% (− 30 cm) of calculated infusion volume (p = 0.395). Start-up fluid delivery with the stopcock primarily placed at + 30 cm and − 30 cm resulted in large anterograde and retrograde fluid volumes of 34.39 [33.43 to 35.34] µL and − 24.90 [− 27.79 to − 22.01] µL at 10 s, respectively (p < 0.0001). Fluid delivered with the stopcock primarily placed at + 30 cm and − 30 cm resulted in 140% and 35% of calculated volume at 360 s, respectively (p < 0.0001). Syringe infusion pumps should ideally be connected to the stopcock positioned at heart level in order to minimize the amounts of anterograde and retrograde fluid volumes after opening of the stopcock.

Opioid administration is particularly challenging in the perioperative period. Computerized-based Clinical Decision Support Systems (CDSS) are a promising innovation that might improve perioperative pain control. We report the development and feasibility validation of a knowledge-based CDSS aiming at optimizing the management of perioperative pain, postoperative nausea and vomiting (PONV), and laxative medications. This novel CDSS uses patient adaptive testing through a smartphone display, literature-based rules, and individual medical prescriptions to produce direct medical advice for the patient user. Our objective was to test the feasibility of the clinical use of our CDSS in the perioperative setting. This was a prospective single arm, single center, cohort study conducted in Strasbourg University Hospital. The primary outcome was the agreement between the recommendation provided by the experimental device and the recommendation provided by study personnel who interpreted the same care algorithm (control). Thirty-seven patients were included in the study of which 30 (81%) used the experimental device. Agreement between these two care recommendations (computer driven vs. clinician driven) was observed in 51 out 54 uses of the device (94.2% [95% CI 85.9–98.4%]). The agreement level had a probability of 86.6% to exceed the 90% clinically relevant agreement threshold. The knowledge-based, patient CDSS we developed was feasible at providing recommendations for the treatment of pain, PONV and constipation in a perioperative clinical setting.Trial registration number & date The study protocol was registered in ClinicalTrial.gov before enrollment began (NCT05707247 on January 26th, 2023).

Hypotension induced by general anesthesia is associated with postoperative complications, increased mortality, and morbidity, particularly elderly patients. The aim of this study was to investigate the effectiveness of corrected carotid artery flow time (FTc) for predicting hypotension following anesthesia induction in patients over 65 years old.After faculty ethical committee approval and written informed consent, 138 patients (65 years and older, ASA physical status I-III) who scheduled for elective surgery were included in this study. In the pre-operative anesthesia unit, the carotid artery FTc value was measured by ultrasound and hemodynamic values were recorded. Following anesthesia induction with propofol, hemodynamic data were recorded at 1-minute intervals for 3min. Measurements were terminated prior to endotracheal intubation, as direct laryngoscopy and endotracheal intubation could cause sympathetic stimulation and hemodynamic changes.Hypotension occurred in 52 patients (37.7%). The preoperative FTc value of the patients who developed hypotension was statistically lower (312.5 ms) than the patients who did not (345.0 ms) (p < 0.001). The area under the ROC curve for carotid artery FTc was 0.93 (95% CI for AUC:0.89-0.97; p < 0.001) with an optimal cut-off of value for predicting post-anesthesia hypotension 333 ms, a sensitivity of 90.4% and a specificity of 84.9%. As a result of the multiple logistic regression model, carotid artery FTc emerged as the sole independent risk factor for hypotension following anesthesia induction.Preoperative carotid artery FTc measurement is a simple, bedside, noninvasive, and reliable method for predicting anesthesia-induced hypotension in elderly patients.

PurposeTo determine the precise induction dose, an objective assessment of individual propofol sensitivity is necessary. This study aimed to investigate whether preinduction electroencephalogram (EEG) data are useful in determining the optimal propofol dose for the induction of general anesthesia in healthy adult patients.MethodsSeventy healthy adult patients underwent total intravenous anesthesia (TIVA), and the effect-site target concentration of propofol was observed to measure each individual’s propofol requirements for loss of responsiveness. We analyzed preinduction EEG data to assess its relationship with propofol requirements and conducted multiple regression analyses considering various patient-related factors.ResultsPatients with higher relative delta power (ρ = 0.47, p < 0.01) and higher absolute delta power (ρ = 0.34, p = 0.01) required a greater amount of propofol for anesthesia induction. In contrast, patients with higher relative beta power (ρ = -0.33, p < 0.01) required less propofol to achieve unresponsiveness. Multiple regression analysis revealed an independent association between relative delta power and propofol requirements.ConclusionPreinduction EEG, particularly relative delta power, is associated with propofol requirements during the induction of general anesthesia. The utilization of preinduction EEG data may improve the precision of induction dose selection for individuals.

Large data sets from electronic health records (EHR) have been used in journal articles to demonstrate race-based imprecision in pulse oximetry (SpO2) measurements. These articles do not appear to recognize the impact of the variability of the SpO2 values with respect to time ("deviation time"). This manuscript seeks to demonstrate that due to this variability, EHR data should not be used to quantify SpO2 error.Using the MIMIC-IV Waveform dataset, SpO2 values are sampled from 198 patients admitted to an intensive care unit and used as reference samples. The error derived from the EHR data is simulated using a set of deviation times. The laboratory oxygen saturation measurements are also simulated such that the performance of three simulated pulse oximeter devices will produce an average root mean squared (ARMS) error of 2%. An analysis is then undertaken to reproduce a medical device submission to a regulatory body by quantifying the mean error, the standard deviation of the error, and the ARMS error. Bland-Altman plots were also generated with their Limits of Agreements. Each analysis was repeated to evaluate whether the measurement errors were affected by increasing the deviation time.All error values increased linearly with respect to the logarithm of the time deviation. At 10min, the ARMS error increased from a baseline of 2% to over 4%.EHR data cannot be reliably used to quantify SpO2 error. Caution should be used in interpreting prior manuscripts that rely on EHR data.