Cognitive function and delirium following sevoflurane or propofol anesthesia for valve replacement surgery: A multicenter randomized controlled trial.

Cognitive dysfunction is a common postoperative neurological complication in patients undergoing valve replacement surgery. This study aimed to compare the effects of sevoflurane versus propofol-based total intravenous anesthesia on the incidence of cognitive dysfunction following valve replacement surgery. This multicenter, randomized, controlled double-blinded study was conducted in three teaching hospitals in China. Patients receiving on-pump valve replacement surgery were enrolled. Stratified block randomization was used to randomly assign patients 1:1 to receive sevoflurane (1.0-1.5 MAC) or propofol (2.0-3.0 mg/kg/h) for anesthesia maintenance. The primary outcome was the incidence of cognitive dysfunction assessed by four cognitive tests before, as well as 7-14 days after surgery. Patients were randomly assigned to receive sevoflurane anesthesia (n = 144) or propofol-based total intravenous anesthesia (n = 145). The incidence of postoperative cognitive dysfunction in the sevoflurane anesthesia group (31.9%) was significantly lower than that in the total intravenous anesthesia group (43.4%; relative risk 0.61, 95% confidence interval [CI]: 0.38-0.97, p = 0.044). There was no difference in the incidence of delirium between patients receiving sevoflurane and total intravenous anesthesia (27.8% [35/144] vs. 25.9% [35/145], 1.10, 95% CI: 0.64 to 1.90, p = 0.736). There was a significant difference in the Katz Index on day 3 after surgery (3 [0.9) vs. 3 (1.0], 0.095, 95% CI: 0.05 to 0.43, p = 0.012). No difference was observed in other outcomes between the two groups. For patients undergoing on-pump valve replacement surgery, sevoflurane anesthesia had a smaller effect on cognitive function and independence in daily life activities compared with propofol anesthesia.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states.

BACKGROUND: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment. METHODS: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method. RESULTS: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods. CONCLUSIONS: The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method-with other evaluation methods, such as EEG-is expected to assist anaesthesiologists in the accurate evaluation of the DoA.