Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2022
Detection of arterial pressure waveform error using machine learning trained algorithms.
In critically ill and high-risk surgical room patients, an invasive arterial catheter is often inserted to continuously measure arterial pressure (AP). The arterial waveform pressure measurement, however, may be compromised by damping or inappropriate reference placement of the pressure transducer. Clinicians, decision support systems, or closed-loop applications that rely on such information would benefit from the ability to detect error from the waveform alone. ⋯ A total of 40 h of monitoring time was recorded with approximately 120,000 heart beats featurized. For all error states, ROC AUCs for algorithm performance on classification of the state were greater than 0.9; when using patient-specific calibrated data AUCs were 0.94, 0.95, and 0.99 for the transducer low, transducer high, and damped conditions respectively. Machine-learning trained algorithms were able to discriminate arterial line transducer error states from the waveform alone with a high degree of accuracy.
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J Clin Monit Comput · Feb 2022
Assessment of a new volumetric capnography-derived parameter to reflect compression quality and to predict return of spontaneous circulation during cardiopulmonary resuscitation in a porcine model.
We aimed to evaluate a volumetric capnography (Vcap)-derived parameter, the volume of CO2 eliminated per minute and per kg body weight (VCO2/kg), as an indicator of the quality of chest compression (CC) and to predict the return to spontaneous circulation (ROSC) under stable ventilation status. Twelve male domestic pigs were utilized for the randomized crossover study. After 4 min of untreated ventricular fibrillation (VF), mechanical cardiopulmonary resuscitation and ventilation were administered. ⋯ PetCO2 and VCO2/kg have similar capabilities for discriminating survivors from non-survivors, and the area under the curve for both was 0.97. VCO2/kg had similar performance as PetCO2 in reflecting the quality of CC and prediction of achieving ROSC under stable ventilation status in a porcine model of VF-related cardiac arrest. However, VCO2/kg requires a longer time to achieve a stable state after adjusting for quality of CC than PetCO2.
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J Clin Monit Comput · Feb 2022
Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms.
Brain monitors which track quantitative electroencephalogram (EEG) signatures to monitor sedation levels are drug and patient specific. There is a need for robust sedation level monitoring systems to accurately track sedation levels across all drug classes, sex and age groups. Forty-four quantitative features estimated from a pooled dataset of 204 EEG recordings from 66 healthy adult volunteers who received either propofol, dexmedetomidine, or sevoflurane (all with and without remifentanil) were used in a machine learning based automated system to estimate the depth of sedation. ⋯ Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels. The results obtained in this study may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms. Clinical trial registration: NCT02043938 and NCT03143972.
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J Clin Monit Comput · Feb 2022
Impact of chronic treatment by β1-adrenergic antagonists on Nociceptive-Level (NOL) index variation after a standardized noxious stimulus under general anesthesia: a cohort study.
During the perioperative period, nociception control is certainly one of the anesthesiologist's main objectives when assuming care of a patient. There exists some literature demonstrating that the nociceptive stimuli experienced during surgery are responsible for peripheral and central sensitization phenomena, which can in turn lead to persistent postsurgical pain. An individualized approach to the evaluation and treatment of perioperative nociception is beneficial in order to avoid the sensitization phenomena that leads to prolonged postoperative pain and to minimize the consumption of opiates and their adverse effects. ⋯ In conclusion, the NOL index is a reliable monitor to assess nociception in a population of patients under chronic beta-blocker therapy. Patients under such therapy achieve similar maximal NOL values over a 180 s period after a standardized noxious stimulus and the NOL variation over time, represented by the AUC is not significantly different from a cohort of non-beta-blocked patients. Whether the patient takes beta-blockers or not, sensitivity of the NOL index is greater than that seen for BIS index or heart rate to detect an experimental noxious stimulus under general anesthesia.
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J Clin Monit Comput · Feb 2022
Anesthetics affect peripheral venous pressure waveforms and the cross-talk with arterial pressure.
Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two pediatric cohorts were studied to test the effect of anesthetic agents on the PVP waveform and cross-talk between peripheral veins and arteries: (1) dehydration setting in a pyloromyotomy using the infused anesthetic propofol and (2) hemorrhage setting during elective surgery for craniosynostosis with the inhaled anesthetic isoflurane. PVP waveforms were collected from 39 patients that received propofol and 9 that received isoflurane. ⋯ The k-NN prediction models had 82% and 77% accuracy for detecting propofol and MAC, respectively. The cross-talk relationship at each stage was: (a) ρ = 0.95, (b) ρ = 0.96, and (c) could not be evaluated using this cohort. Future research should consider anesthetic agents when analyzing PVP waveforms developing future clinical monitoring technology that uses PVP.