Showing papers by "Jan Poelaert published in 2021"

Neurophysiological Assessments During Continuous Sedation Until Death Put Validity of Observational Assessments Into Question: A Prospective Observational Study

Abstract: In case of untreatable suffering at the end of life, continuous sedation until death (CSD) may be the only treatment option left. Because these patients cannot communicate anymore, caregivers have to rely on behavioral observation to assess the patient’s comfort. Recently, however, a number of studies from the neurosciences have shown that sometimes consciousness and pain are undetectable with these traditional behavioral methods. The aim of this study was to find out if subjective caregiver assessments of consciousness and pain would be confirmed by objective neurophysiological measures. In this prospective observational study, we observed patients from the start of palliative sedation until death. Subjective caregiver assessments of level of consciousness and pain based on behavioral observations were compared with objective measures from neurophysiological monitoring devices. We collected and analyzed 108 subjective caregiver assessments in a sample of 12 patients and 32 assessments by traditionally used observational scales. We compared these with objective neurophysiological measures. Sensitivity and specificity of caregivers’ subjective assessments of consciousness was 23.6 and 91.1% respectively, with an accuracy of 54.0% and interrater reliability (κ) of 0.13. For pain, this was 0 and 94.79%, respectively, an accuracy of 88%, and an inter-rater reliability (κ) of − 0.063. Agreement between caregivers’ subjective assessments and objective neurophysiological measures of consciousness and pain was very poor. Caregivers’ subjective assessment of level of consciousness and pain during CSD is unreliable compared with objective neurophysiological monitoring. Our results suggest that assessments of patient comfort during CSD could have been improved substantially by including objective monitoring of level of consciousness and pain. The protocol for this observational study has been registered retrospectively at Clinical-Trials.gov (ID NCT03273244).

Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach.

Abstract: Background: Perioperative quantitative monitoring of neuromuscular function in patients receiving neuromuscular blockers has become internationally recognized as an absolute and core necessity in modern anesthesia care. Because of their kinetic nature, artifactual recordings of acceleromyography-based neuromuscular monitoring devices are not unusual. These generate a great deal of cynicism among anesthesiologists, constituting an obstacle toward their widespread adoption. Through outlier analysis techniques, monitoring devices can learn to detect and flag signal abnormalities. Outlier analysis (or anomaly detection) refers to the problem of finding patterns in data that do not conform to expected behavior. Objective: This study was motivated by the development of a smartphone app intended for neuromuscular monitoring based on combined accelerometric and angular hand movement data. During the paired comparison stage of this app against existing acceleromyography monitoring devices, it was noted that the results from both devices did not always concur. This study aims to engineer a set of features that enable the detection of outliers in the form of erroneous train-of-four (TOF) measurements from an acceleromyographic-based device. These features are tested for their potential in the detection of erroneous TOF measurements by developing an outlier detection algorithm. Methods: A data set encompassing 533 high-sensitivity TOF measurements from 35 patients was created based on a multicentric open label trial of a purpose-built accelero- and gyroscopic-based neuromuscular monitoring app. A basic set of features was extracted based on raw data while a second set of features was purpose engineered based on TOF pattern characteristics. Two cost-sensitive logistic regression (CSLR) models were deployed to evaluate the performance of these features. The final output of the developed models was a binary classification, indicating if a TOF measurement was an outlier or not. Results: A total of 7 basic features were extracted based on raw data, while another 8 features were engineered based on TOF pattern characteristics. The model training and testing were based on separate data sets: one with 319 measurements (18 outliers) and a second with 214 measurements (12 outliers). The F1 score (95% CI) was 0.86 (0.48-0.97) for the CSLR model with engineered features, significantly larger than the CSLR model with the basic features (0.29 [0.17-0.53]; P<.001). Conclusions: The set of engineered features and their corresponding incorporation in an outlier detection algorithm have the potential to increase overall neuromuscular monitoring data consistency. Integrating outlier flagging algorithms within neuromuscular monitors could potentially reduce overall acceleromyography-based reliability issues. Trial Registration: ClinicalTrials.gov NCT03605225; https://clinicaltrials.gov/ct2/show/NCT03605225

Propofol for Induction and Maintenance of Anesthesia in Patients With Brugada Syndrome: A Single-Center, 25-Year, Retrospective Cohort Analysis.

Abstract: Background Propofol administration in patients with Brugada syndrome (BrS) is still a matter of debate. Despite lacking evidence for its feared arrhythmogenicity, up to date, expert cardiologists recommend avoiding propofol. The main aim of this study is to assess the occurrence of malignant arrhythmias or defibrillations in patients with BrS, during and 30 days after propofol administration. The secondary aim is to investigate the occurrence of adverse events during propofol administration and hospitalization, as the 30-day readmission and 30-day mortality rate. Methods We performed a retrospective cohort study on patients with BrS who received propofol anytime from January 1, 1996 to September 30, 2020. Anesthesia was induced by propofol in both groups. In the total intravenous anesthesia (TIVA) group, anesthesia was maintained by propofol, while in the BOLUS group, volatile anesthesia was provided. The individual anesthetic charts and the full electronic medical records up to 30 postprocedural days were scrutinized. Results One hundred thirty-five BrS patients who underwent a total of 304 procedures were analyzed. The TIVA group included 27 patients for 33 procedures, and the BOLUS group included 108 patients for 271 procedures. In the TIVA group, the median time of propofol infusion was 60 minutes (interquartile range [IQR] = 30-180). The estimated plasma or effect-site concentration ranged between 1.0 and 6.0 µg·mL-1 for target-controlled infusion (TCI). The infusion rate for manually driven TIVA varied between 0.8 and 10.0 mg·kg-1·h-1. In the BOLUS group, the mean propofol dose per kilogram total body weight was 2.4 ± 0.9 mg·kg-1. No malignant arrhythmias or defibrillations were registered in both groups. The estimated 95% confidence interval (CI) of the risk for malignant arrhythmias in the BOLUS and TIVA groups was 0-0.011 and 0-0.091, respectively. Conclusions The analysis of 304 anesthetic procedures in BrS patients, who received propofol, either as a TIVA or as a bolus during induction of volatile-based anesthesia, revealed no evidence of malignant arrhythmias or defibrillations. The present data do not support an increased risk with propofol-based TIVA compared to propofol-induced volatile anesthesia. Prospective studies are needed to investigate the electrophysiologic effects of propofol in BrS patents.