Distinct neural activities of the cortical layer 2/3 across isoflurane anesthesia: A large-scale simultaneous observation of neurons.

Anesthesia inhibits neural activity in the brain, causing patients to lose consciousness and sensation during the surgery. Layers 2/3 of the cortex are important structures for the integration of information and consciousness, which are closely related to normal cognitive function. However, the dynamics of the large-scale population of neurons across multiple regions in layer 2/3 during anesthesia and recovery processes remains unclear. We conducted simultaneous observations and analysis of large-scale calcium signaling dynamics across multiple cortical regions within cortical layer 2/3 during isoflurane anesthesia and recovery in vivo by high-resolution wide-field microscopy. Under isoflurane-induced anesthesia, there is an overall decrease in neuronal activity across multiple regions in the cortical layer 2/3. Notably, some neurons display a paradoxical increase in activity during anesthesia. Additionally, the activity among multiple cortical regions under anesthesia was homogeneous. It is only during the recovery phase that variability emerges in the extent of increased neural activity across different cortical regions. Within the same duration of anesthesia, neural activity did not return to preanesthetic levels. To sum up, anesthesia as a dynamic alteration of brain functional networks, encompassing shifts in patterns of neural activity, homogeneousness among cortical neurons and regions, and changes in functional connectivity. Recovery from anesthesia does not entail a reversal of these effects within the same timeframe.

Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations.

Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients.

Preoperative prognostic nutritional index predicts postoperative delirium in aged patients after surgery: A matched cohort study.

OBJECTIVE: Prognostic nutritional index (PNI) is an indicator to evaluate the nutritional immune status of patients. This study aimed to assess whether preoperative PNI could predict the occurrence of postoperative POD in aged patients undergoing non-neurosurgery and non-cardiac surgery. METHOD: The aged patients undergoing non-neurosurgery and non-cardiac surgery between January 2014 and August 2019 were included in the retrospective cohort study. The correlation between POD and PNI was investigated by univariate and multivariable logistic regression analysis, propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and subgroup analysis. RESULTS: In the cohort (n = 29,814), the cutoff value of PNI was 46.01 determined by the receiver operating characteristic (ROC) curve. In univariate and three multivariable regression analysis, the ORs of PNI ≤ 46.01 was 2.573(95% CI:2.261-2.929, P < 0.001),1.802 (95% CI:1.567-2.071, P < 0.001),1.463(95% CI:1.246-1.718, P < 0.001),1.370(95% CI:1.165-1.611, P < 0.001). In the PSM model and IPTW model, the ORs of PNI ≤ 46.01 were 1.424(95% CI:1.172-1.734, P < 0.001) and 1.356(95% CI:1.223-1.505, P < 0.001). CONCLUSION: The PNI was found to have a predictive value for POD in patients undergoing non-neurosurgery and non-cardiac surgery. Improving preoperative nutritional status may be beneficial in preventing POD for aged patients.