Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2024
Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra.
Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. ⋯ This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.
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J Clin Monit Comput · Apr 2024
LetterReevaluating optic nerve sheath diameter in predicting postdural puncture headache: exploring clinical implications beyond threshold values.
The study by Boyaci et al. assessed using optic nerve sheath diameter (ONSD) ultrasound to predict postdural puncture headache (PDPH) in spinal anesthesia patients. In their single-center study of 83 patients, PDPH incidence was high at 22.9%, partly due to the use of a traumatic needle. ⋯ ONSD's relationship with intracranial pressure (ICP) is acknowledged, but a definitive ONSD cutoff for PDPH is lacking. Other studies suggest ONSD changes may be linked to treatment outcomes in related conditions, emphasizing the importance of investigating risks of epidural blood patch failure.
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J Clin Monit Comput · Apr 2024
Evaluating inter-individual variability captured by the Eleveld pharmacokinetics model.
Inter-individual variability in Pharmacokinetic (PK) and Pharmacodynamic (PD) models significantly affects the accuracy of Target Controlled Infusion and closed-loop control of anesthesia. We hypothesize that the novel Eleveld PK model captures more inter-individual variability relevant to both open-loop and closed-loop control design, resulting in reduced variability in PD models identified using the Eleveld PK model's plasma prediction compared to the Schuttler or Schnider PK model. We used a dataset of propofol infusion rates and Depth of Hypnosis measurements across three demographic groups: elderly, obese, and adult. ⋯ Validated PKPD models using the Schuttler and Schnider PK model showed no significant differences in predictive response and multiplicative uncertainty compared to the Eleveld PK model. The coefficient variations in step responses of PD model sets and the frequency ranges, corresponding to uncertainty below one, were comparable for all three PK models. The comparison of the accumulated coefficient of variation in the step-response and the uncertainty of the PD model sets indicated that the Eleveld PK model does not offer any advantage for the design of open-loop or closed-loop control of anesthesia.
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J Clin Monit Comput · Apr 2024
FiO2 prediction formula during low flow oxygen therapy in an adult model: a bench study.
During low-flow oxygen therapy, the true value of inspired oxygen fraction (FiO2) is generally unknown. Knowledge of delivered FiO2 values may be useful as well as to adjust oxygen therapy, as well as to predict patient deterioration. This study proposes a New FiO2 Prediction Formula (NFiO2) for low-flow oxygenation and compares its predictive value to precedent formulas. ⋯ Bias and limits of agreement between predicted FiO2 and benchtop FiO2 highlighted consistent differences between different FiO2 prediction formulas. The NFiO2 and the Duprez Formula 2018 seemed to be the most accurate formulas, followed by the Vincent Formula, and lastly the Shapiro Formula. A New FiO2 Prediction Formula was developed using clinical readily available variables (RR and O2 Flow rate) which showed good accuracy in predicting FiO2 during oxygenation at low flow.
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J Clin Monit Comput · Apr 2024
Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.
This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. ⋯ The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.