Incorporating preoperative frailty to assist in early prediction of postoperative pneumonia in elderly patients with hip fractures: an externally validated online interpretable machine learning model.

BACKGROUND: This study aims to implement a validated prediction model and application medium for postoperative pneumonia (POP) in elderly patients with hip fractures in order to facilitate individualized intervention by clinicians. METHODS: Employing clinical data from elderly patients with hip fractures, we derived and externally validated machine learning models for predicting POP. Model derivation utilized a registry from Nanjing First Hospital, and external validation was performed using data from patients at the Fourth Affiliated Hospital of Nanjing Medical University. The derivation cohort was divided into the training set and the testing set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used for feature screening. We compared the performance of models to select the optimized model and introduced SHapley Additive exPlanations (SHAP) to interpret the model. RESULTS: The derivation and validation cohorts comprised 498 and 124 patients, with 14.3% and 10.5% POP rates, respectively. Among these models, Categorical boosting (Catboost) demonstrated superior discrimination ability. AUROC was 0.895 (95%CI: 0.841-0.949) and 0.835 (95%CI: 0.740-0.930) on the training and testing sets, respectively. At external validation, the AUROC amounted to 0.894 (95% CI: 0.821-0.966). The SHAP method showed that CRP, the modified five-item frailty index (mFI-5), and ASA body status were among the top three important predicators of POP. CONCLUSION: Our model's good early prediction ability, combined with the implementation of a network risk calculator based on the Catboost model, was anticipated to effectively distinguish high-risk POP groups, facilitating timely intervention.

A machine learning stacking model accurately estimating gastric fluid volume in patients undergoing elective sedated gastrointestinal endoscopy.

BACKGROUND: The current point-of-care ultrasound (POCUS) assessment of gastric fluid volume primarily relies on the traditional linear approach, which often suffers from moderate accuracy. This study aimed to develop an advanced machine learning (ML) model to estimate gastric fluid volume more accurately. METHODS: We retrospectively analyzed the clinical data and POCUS data (D1: craniocaudal diameter, D2: anteroposterior diameter) of 1386 patients undergoing elective sedated gastrointestinal endoscopy (GIE) at Nanjing First Hospital to predict gastric fluid volume using ML techniques, including six different ML models and a stacking model. We evaluated the models using the adjusted Coefficient of Determination (R2), mean absolute error (MAE) and root mean square error (RMSE). The SHapley Additive exPlanations (SHAP) method was used to interpret the importance of the variables. Finally, a web calculator was constructed to facilitate its clinical application. RESULTS: The stacking model (Linear regression + Multilayer perceptron) performed best, with the highest adjusted R2 of 0.718 (0.632 to 0.804). The mean prediction bias was 4 ml (MAE: 4.008 (3.68 to 4.336)), which is better than that of the linear model. D1 and D2 ranked high in the SHAP plot and performed better in the right lateral decubitus (RLD) than in the supine position. The web calculator can be accessed at https://cheason.shinyapps.io/Stacking_regressor/. CONCLUSION: The stacking model and its web calculator can serve as practical tools for accurately estimating gastric fluid volume in patients undergoing elective sedated GIE. It is recommended that anesthesiologists measure D1 and D2 in the patient's RLD position.

Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool.

OBJECTIVES: Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation. METHODS: In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use. RESULTS: We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models. CONCLUSION: Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently.

Colonoscopy under sedation is an effective technique for the inspection and treatment of alimentary canal diseases, but hypoxemia associated with this process cannot be ignored, since prolonged or severe hypoxemia may result in several serious consequences.We wanted to develop a practical and accurate model to predict the risk of hypoxemia for outpatient colonoscopy under sedation, which could help clinicians make more accurate and objective judgments to prevent patients from being harmed.A total of 839 patients were included in our study and we constructed five machine learning models and selected the best one, which demonstrated satisfactory performance. On this basis, a user-friendly data interface has been developed for convenient application. Clinicians can log in to this interface at any time and it will automatically calculate the patient's risk of hypoxemia when entering patient information.This study offers evidence that machine learning algorithms can accurately predict the risk of hypoxemia for outpatient colonoscopy under sedation and the model we developed is a practical and interpretable tool that could be used as a clinical decision-making aid.

Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study.

OBJECTIVE: Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS: From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS: Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS: We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.

Development and validation of a novel nomogram model to assess the risk of gastric contents in outpatients undergoing elective sedative gastrointestinal endoscopy procedures.

BACKGROUND: Gastric contents may contribute to patients' aspiration during anesthesia. Ultrasound can accurately assess the risk of gastric contents in patients undergoing sedative gastrointestinal endoscopy (GIE) procedures, but its efficiency is limited. Therefore, developing an accurate and efficient model to predict gastric contents in outpatients undergoing elective sedative GIE procedures is greatly desirable. METHODS: This study retrospectively analyzed 1501 patients undergoing sedative GIE procedures. Gastric contents were observed under direct gastroscopic vision and suctioned through the endoscope. High-risk gastric contents were defined as having solid content or liquid volume > 25 ml and pH < 2.5; otherwise, they were considered low-risk gastric contents. Univariate analysis and multivariate analysis were used to select the independent risk factors to predict high-risk gastric contents. Based on the selected independent risk factors, we assigned values to each independent risk factor and established a novel nomogram. The performance of the nomogram was verified in the testing cohort by the metrics of discrimination, calibration, and clinical usefulness. In addition, an online accessible web calculator was constructed. RESULTS: We found BMI, cerebral infarction, cirrhosis, male, age, diabetes, and gastroesophageal reflux disease were risk factors for gastric contents. The AUROCs were 0.911 and 0.864 in the development and testing cohort, respectively. Moreover, the nomogram showed good calibration ability. Decision curve analysis and Clinical impact curve demonstrated that the predictive nomogram was clinically useful. The website of the nomogram was https://medication.shinyapps.io/dynnomapp/. CONCLUSIONS: This study demonstrates that clinical variables can be combined with algorithmic techniques to predict gastric contents in outpatients. Nomogram was constructed from routine variables, and the web calculator had excellent clinical applicability to assess the risk of gastric contents accurately and efficiently in outpatients, assist anesthesiologists in assessment and identify the most appropriate patients for ultrasound.

Down-regulating nuclear factor of activated T cells 1 alleviates cognitive deficits in a mouse model of sepsis-associated encephalopathy, possibly by stimulating hippocampal neurogenesis.

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, and has been associated with increased morbidity and mortality. Nuclear factor of activated T cells (NFATs) 1, a transcriptional factor that regulates T cell development, activation and differentiation, has been implicated in neuronal plasticity. Here we examined the potential role of NFAT1 in sepsis-associated encephalopathy in mice. Adult male C57BL/6J mice received intracerebroventricular injections of short interfering RNA against NFAT1 or sex-determining region Y-box 2 (SOX2), or a scrambled control siRNA prior to cecal ligation and perforation (CLP). A group of mice receiving sham surgery were included as an additional control. CLP increased escape latency and decreased the number of crossings into, and total time spent within, the target quadrant in the Morris water maze test. CLP also decreased the freezing time in context-dependent, but not context-independent, fear conditioning test. Knockdown of either NFAT1 or SOX2 attenuated these behavioral deficits. NFAT1 knockdown also attenuated CLP-induced upregulation of SOX2, increased the numbers of nestin-positive cells and newborn astrocytes, reduced the number of immature newborn neurons, and promoted the G1 to S transition of neural stem cells in hippocampus. These findings suggest that NFAT1 may contribute to sepsis-induced behavioral deficits, possibly by promoting SOX2 signaling and neurogenesis.

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy.

BACKGROUND AND AIMS: Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups. METHODS: We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively. RESULTS: Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group. CONCLUSIONS: The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.

This study is the first model utilizing advanced ML techniques based on multiple clinical variables to identify full stomach. The model is suitable for patient-rich outpatient clinics, primary hospitals, remote regions, and specific clinical settings where POCUS is not available.The developed convenient RF model predicted full stomach with high accuracy and interpretability. The test cohort AUROC was 0.837. We further established an online accessible individualized risk calculator and provided waterfall plots to increase the interpretability of each prediction.The propensity score matching (PSM) showed that preoperative oral carbohydrate (POCs) were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may provide information for anesthesiologists to guide patients on POCs.

A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery.

BACKGROUND: Early identification of elderly patients undergoing non-cardiac surgery who may be at high risk for postoperative cognitive dysfunction (POCD) can increase the chances of prevention for them, as extra attention and limited resources can be allocated more to these patients. AIM: We performed this analysis with the aim of developing a simple, clinically useful machine learning (ML) model to predict the probability of POCD at 3 months in elderly patients after non-cardiac surgery. METHODS: We collected information on patients who received surgical treatment at Nanjing First Hospital from May 2020 to May 2021. We used LASSO regression to select key features and built 5 ML models to assess the risk of POCD at 3 months in elderly patients after non-cardiac surgery. The Shapley Additive exPlanations (SHAP) and methods were introduced to interpret the best model. RESULTS: A total of 415 patients with non-cardiac surgery were included. The support vector machine (SVM) was the best-performing model of the five ML models. The model showed excellent performance compared to the other four models. The SHAP results showed that VAS score, age, intraoperative hypotension, and preoperative hemoglobin were the four most important features, indicating that the SVM model had good interpretability and reliability. The website of the web-based calculator was https://modricreagan-non-3-pocd-9w2q78.streamlit.app/ . CONCLUSION: Based on six important perioperative variables, we successfully established a series of ML models for predicting POCD occurrence at 3 months after surgery in elderly non-cardiac patients, with SVM model being the best-performing model. Our models are expected to serve as decision aids for clinicians to monitor screened high-risk patients more closely or to consider further interventions.

Incorporating intraoperative blood pressure time-series variables to assist in prediction of acute kidney injury after type a acute aortic dissection repair: an interpretable machine learning model.

BACKGROUND: Acute kidney injury (AKI) is a common and serious complication after the repair of Type A acute aortic dissection (TA-AAD). However, previous models have failed to account for the impact of blood pressure fluctuations on predictive performance. This study aims to develop machine learning (ML) models combined with intraoperative medicine and blood pressure time-series data to improve the accuracy of early prediction for postoperative AKI risk. METHODS: Indicators reflecting the duration and depth of hypotension were obtained by analyzing continuous mean arterial pressure (MAP) monitored intraoperatively with multiple thresholds (<65, 60, 55, 50) set in the study. The predictive features were selected by logistic regression and the least absolute shrinkage and selection operator (LASSO), and 4 ML models were built based on the above features. The performance of the models was evaluated by area under receiver operating characteristic curve (AUROC), calibration curve and decision curve analysis (DCA). Shapley additive interpretation (SHAP) was used to explain the prediction models. RESULTS: Among the indicators reflecting intraoperative hypotension, 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI (p < .001). Among 4 models, the extreme gradient boosting (XGBoost) model demonstrated the highest AUROC: 0.800 (95% 0.683-0.917) and sensitivity: 0.717 in the testing set and was verified the best-performing model. The SHAP summary plot indicated that intraoperative urine output, cumulative time of mean arterial pressure lower than 65 mmHg outside cardiopulmonary bypass (OUT_CPB_MAP_65 time), autologous blood transfusion, and smoking were the top 4 features that contributed to the prediction model. CONCLUSION: With the introduction of intraoperative blood pressure time-series variables, we have developed an interpretable XGBoost model that successfully achieve high accuracy in predicting the risk of AKI after TA-AAD repair, which might aid in the perioperative management of high-risk patients, particularly for intraoperative hemodynamic regulation.

In this study, we combined intraoperative blood pressure time-series data for the first time to build 4 machine learning (ML) models that successfully improve the accuracy of early prediction of postoperative AKI risk, with the XGBoost model displaying the best predictive performance.We explored the impact of multiple intraoperative hypotension thresholds (MAP <65, <60, <55 < 50 mmHg) on the occurrence of postoperative AKI in patients and attempted to provide clinicians with recommendations for hemodynamic management during surgery.Our study found that 65 mmHg showed a statistically superior difference to other thresholds in patients with or without AKI after undergoing TA-AAD repair (p < .001).

[Phosphate and tension homology-induced kinase 1/Parkin signaling mediates cognitive dysfunction in sepsis-associated encephalopathy through activation of hippocampal mitochondrial autophagy].

OBJECTIVE: To investigate the effects of gene of phosphate and tension homology (PTEN)-induced putative kinase 1 (PINK1)/Parkin pathway on hippocampal mitophagy and cognitive function in mice with sepsis-associated encephalopathy (SAE) and its possible mechanism. METHODS: A total of 80 male C57BL/6J mice were randomly divided into Sham group, cecal ligation puncture (CLP) group, PINK1 plasmid transfection pretreatment groups (p-PINK1+Sham group, p-PINK1+CLP group), empty vector plasmid transfection control group (p-vector+CLP group), with 16 mice in each group. The mice in CLP groups were treated with CLP to reproduce SAE models. The mice in the Sham groups were performed laparotomy only. Animals in the p-PINK1+Sham and p-PINK1+CLP groups were transfected with PINK1 plasmid through the lateral ventricle at 24 hours before surgery, while mice in the p-vector+CLP group were transfected with the empty plasmid. Morris water maze experiment was performed 7 days after CLP. The hippocampal tissues were collected, the pathological changes were observed under a light microscope after hematoxylin-eosin (HE) staining, and the mitochondrial autophagy was observed under a transmission electron microscopy after uranyl acetate and lead citrate staining. The expressions of PINK1, Parkin, Beclin1, interleukins (IL-6, IL-1ß) and microtubule-associated protein 1 light chain 3 (LC3) were detected by Western blotting. RESULTS: Compared with the Sham group, CLP group mice in Morris water maze experiment had longer escape latency, shorter target quadrant residence time, and fewer times of crossing the platform at 1-4 days. Under the light microscope, the hippocampal structure of the mouse was injured, the neuronal cells were arranged in disorder, and the nuclei were pyknotic. Under the electron microscope, the mitochondria appeared swollen, round, and wrapped by bilayer or multilayer membrane structures. Compared with the Sham group, CLP group had higher expressions of PINK1, Parkin, Beclin1, LC3II/LC3I ratio, IL-6 and IL-1ß in hippocampus, indicating that sepsis induced by CLP could activated inflammatory response and caused PINK1/Parkin-mediated mitophagy. Compared with the CLP group, p-PINK1+CLP group had shorter escape latencies, spent more time in the target quadrant and had more number of crossings in the target quadrant at 1-4 days. Under the light microscope, the hippocampal structures of mice was destroyed, the neurons were arranged disorderly, and the nuclei were pyknotic. Under transmission electron microscope, swollen and rounded mitochondria and mitochondrial structure wrapped by double membrane or multilayer membrane structure were observed. Compared with the CLP group, the levels of PINK1, Parkin, Beclin1 and LC3II/LC3 ratio in the p-PINK1+CLP group were significantly increased [PINK1 protein (PINK1/ß-actin): 1.95±0.17 vs. 1.74±0.15, Parkin protein (Parkin/ß-actin): 2.06±0.11 vs. 1.78±0.12, Beclin1 protein (Beclin1/ß-actin): 2.11±0.12 vs. 1.67±0.10, LC3II/LC3I ratio: 3.63±0.12 vs. 2.27±0.10, all P < 0.05], while the levels of IL-6 and IL-1ß were significantly decreased [IL-6 protein (IL-6/ß-actin): 1.69±0.09 vs. 2.00±0.11, IL-1ß protein (IL-1ß/ß-actin): 1.11±0.12 vs. 1.65±0.12, both P < 0.05], suggesting that overexpression of PINK1 protein could further activate mitophagy and reduce the inflammatory response caused by sepsis. There was no statistically significant difference in the above pathological changes and related indicators between Sham group and p-PINK1+Sham group, CLP group and p-vector+CLP group. CONCLUSIONS: PINK1 overexpression can further activate CLP-induced mitophagy by upregulating Parkin, thereby inhibiting inflammation response and alleviate cognitive function impairment in SAE mice.

An interpretable artificial neural network model for predicting hypoxemia via an online tool in adult (18-64) patients during esophagogastroduodenoscopy.

Background: The hypoxemia risk in adult (18-64) patients treated with esophagogastroduodenoscopy (EGD) under sedation often poses a dilemma for anesthesiologists. We aimed to establish an artificial neural network (ANN) model to solve this problem, and introduce the Shapley additive explanations (SHAP) algorithm to further improve the interpretability. Methods: The relevant data of patients underwent routine anesthesia-assisted EGD were collected. Elastic network was used to filter the optimal features. Airway-ANN and Basic-ANN models were established based on all collected indicators and remaining variables excluding airway assessment indicators, respectively. The performance of Basic-ANN, Airway-ANN and STOP-BANG was evaluated by the area under the precision-recall curve (AUPRC) on temporal validation set. The SHAP was used for revealing the predictive behavior of our best model. Results: 999 patients were eventually included. The AUPRC value of Airway-ANN model was significantly higher than Basic-ANN model in the temporal validation set (0.532 vs 0.429, P < 0.05). And the performance of both two ANN models was significantly better than that of STOP-BANG score (both P < 0.05). The Airway-ANN model was deployed to the cloud (http://njfh-yxb.com.cn:2022/airway_ann). Conclusion: Our online interpretable Airway-ANN model achieved satisfying ability in identifying the hypoxemia risk in adult (18-64) patients undergoing EGD.

Dynamic prediction of hypoxemia risk at different time points based on preoperative and intraoperative features: machine learning applications in outpatients undergoing esophagogastroduodenoscopy.

BACKGROUND: Hypoxemia often occurs in outpatients undergoing anesthesia-assisted esophagogastroduodenoscopy (EGD). However, there is a scarcity in tools to predict the hypoxemia risk. We aimed to solve this problem by developing and validating machine learning (ML) models based on preoperative and intraoperative features. METHODS: All data were retrospectively collected from June 2021 to February 2022. The most appropriate predictive features were selected by the least absolute shrinkage and selection operator, which were incorporated and modelled by 4 ML algorithms. The area under the precision-recall curve (AUPRC) was used as the main evaluation metric to select the best models, and the selected models were compared with the STOP-BANG score. Their predictive performance was visually interpreted by SHapley Additive exPlanations. The primary endpoint of this study was hypoxemia during the procedure, defined as at least one reading of pulse oximetry < 90% without probes misplacement from the anesthesia induction beginning to the end of EGD, while the secondary endpoint was hypoxemia during induction, from the induction beginning to the start of endoscopic intubation. RESULTS: Of 1160 patients in the derivation cohort, 112 patients (9.6%) developed intraoperative hypoxemia, of which 102 (8.8%) occurred during the induction period. In temporal and external validation, no matter whether based on preoperative variables or still based on preoperative plus intraoperative variables, our models showed excellent predictive performance for the two endpoints, significantly better than STOP-BANG score. In the model interpretation section, preoperative variables (airway assessment indicators, pulse oximeter oxygen saturation and BMI) and intraoperative variables (the induced propofol dose) made the highest contribution to the predictions.To our knowledge, our ML models were the first to predict hypoxemia risk, which achieved excellent overall predictive ability integrating various clinical indicators. These models have the potential to become an effective tool for adjusting sedation strategies flexibly and reducing the workload of anesthesiologists.KEY MESSAGESThis study is the first model employing ML methods based on preoperative and preoperative plus intraoperative variables for predicting the risk of hypoxemia during induction and the whole EGD procedure respectively.Our four models achieved satisfactory predictive performance and outperformed STOP-BANG score in terms of AUPRC in the temporal and external validation cohorts respectively.We found that the relevant variables of airway assessment should be fully taken into account when analyzing the risk factor of hypoxemia, and the effect of patients' age on their hypoxemia risk should be considered in conjunction with the propofol dose.

Role of nuclear factor of activated T Cells-1 in Sepsis-induced behavioral deficits in mice.

INTRODUCTION: The nuclear factor of activated T cells-1 (NFAT1) is involved in both neuroinflammation and cognitive dysfunction. In this study, we examined the role of NFAT1 in sepsis-induced cognitive impairment in a mouse model. METHODS: Sepsis was established in adult mice by cecal ligation and puncture (CLP). Novel object recognition tests on days 14-21 and fear conditioning tests on days 22-23 post-surgery showed that CLP impaired both behaviors. BV2 microglia cells exposed to lipopolysaccharide (LPS) were used to examine the effects of short interfering RNA targeting NFAT1 on autophagy and inflammatory cytokines. RESULTS: CLP increased the expression of NFAT1 in hippocampal microglia and induced hippocampal autophagy by downregulating p62, upregulating beclin-1 and autophagy-related gene-5, and increasing the ratio of microtubule-associated protein 1 light chain 3-I (LC3-I) to LC3-II. In addition, CLP shifted microglial polarization from M2 to M1 and the production of inflammatory cytokines, similar to the effects of lipopolysaccharide on BV2 microglia cells. Conversely, NFAT1 knockdown or the autophagy inhibitor 3-methyladenine attenuated the effects of CLP on autophagy and inflammation in vitro and in vivo, while rapamycin partially reversed the protective effects of NFAT1 inhibition. CONCLUSION: This study suggests that NFAT1 downregulation attenuates sepsis-induced behavioral deficits by inhibiting autophagy, microglia polarization, and neuroinflammation..

Disrupted metabolic and spontaneous neuronal activity of hippocampus in sepsis associated encephalopathy rats: A study combining magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging.

Background: The diagnosis of sepsis associated encephalopathy (SAE) remains challenging in clinical settings because of a lack of specific biomarkers. Functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1H-MRS) can be used to aid in the diagnosis of cognition related diseases. This study investigated changes in functional activities and brain metabolites in the hippocampus in SAE rats by fMRI and 1H-MRS. Materials and methods: Sepsis associated encephalopathy rats underwent cecal ligation and perforation (CLP) surgery. The Morris water maze (MWM) test was then used to evaluate cognitive function. Resting state-fMRI and 1H-MRS scanning were performed 7 and 14 days after CLP surgery to reveal spontaneous neuronal activity and metabolite changes in the hippocampus. The amplitude of low-frequency fluctuation (ALFF) was used to evaluate spontaneous neuronal activity in the hippocampus. Creatine (Cr), Myo-inositol (mI), and glutamine/glutamate (Glx) levels were measured with 1H-MRS scanning. Immunofluorescence and levels of interleukin (IL)-1ß, interleukin (IL)-6, and C-reactive protein (CRP) in the hippocampus were additionally detected to evaluate microglial mediated inflammatory responses. Statistical analysis was performed to evaluate correlations between hippocampal metabolism and behavioral findings. Results: Cecal ligation and perforation treated rats exhibited impaired learning and memory function in the MWM test at days 7 and 14. Elevation of IL-1ß in the hippocampus, as well as immunofluorescence results, confirmed severe neuro inflammation in the hippocampus in SAE rats. Compared with the sham group, the ALFF of the right CA-1 area of the hippocampus was higher at day 7after CLP surgery. The Glx/Cr and mI/Cr ratios were enhanced at day 7 after CLP surgery and slightly lower at day 14 after CLP surgery. The ALFF value, and Glx/Cr and mI/Cr ratios were negatively correlated with time spent in the target quadrant in the MWM test. Conclusion: Spontaneous neuronal activity and metabolites showed significant alterations in SAE rats. The elevated ALFF value, Glx/Cr ratio, and mI/Cr ratio in the hippocampus were positively associated with cognitive deficits. Changes in ALFF and metabolites in hippocampus may serve as potential neuroimaging biomarkers of cognitive disorders in patients with SAE.

Novel Bedside Dynamic Nomograms to Predict the Probability of Postoperative Cognitive Dysfunction in Elderly Patients Undergoing Noncardiac Surgery: A Retrospective Study.

Purpose: Early and accurate prediction of elderly patients at high risk of postoperative cognitive dysfunction (POCD) after non-cardiac surgery will provide favorable evidence for rational perioperative management and long-term postoperative recovery. This study aimed to develop bedside dynamic nomograms to provide accurately an individualized prediction of the risk of POCD at 6-month postoperatively with patients undergoing non-cardiac surgery and to guide clinical decision-making and postoperative management. Patients and Methods: We retrospectively collected patients undergoing surgical treatment at the Nanjing First Hospital between May 2020 and May 2021. We collected the data on preoperative, intraoperative, and postoperative variables. Clinical and laboratory data on admission and intraoperative variables and postoperative variables were used. We measured the performances of the nomograms using sensitivity, specificity of the receiver operating characteristic (ROC), the area under the ROC curves (AUC), the 10-fold cross-validation, and decision curve analysis (DCA). Results: POCD was observed in 23 of 415 patients (5.6%) at 6-month postoperatively. The preoperative and postoperative models obtained 91.6% and 94.0% accuracy rates on the data. Compared to the preoperative model, the postoperative model had an area under the receiver characteristic curve (AUC) of 0.973 vs 0.947, corresponding to a specificity of 0.941 vs 0.918 and a sensitivity of 0.913 vs 0.870. The overall performance of the postoperative model was better than the preoperative model. Conclusion: In this study, we developed novel bedside dynamic nomograms with reasonable clinical utility that can provide individualized prediction of POCD risk at 6-month postoperatively in elderly patients undergoing non-cardiac surgery at different time points based on patient admission and postoperative data. External validations are needed to ensure their value in predicting POCD in elderly patients.

Short Chain Fatty Acids Protect the Cognitive Function of Sepsis Associated Encephalopathy Mice via GPR43.

Objective: This study aims to analyze the changes of fecal short chain fatty acids (SCFAs) content and gut microbiota composition in sepsis associated encephalopathy (SAE) mice, further evaluating the effect of SCFAs on cognitive function and the underlying mechanism in SAE mice. Methods: A total of 55 male adult C57BL/6 mice (2-3 months of age, 20-25 g) were divided into four groups randomly: sham group (n = 10), cecal ligation and puncture group (CLP group, n = 15), CLP+SCFAs group (n = 15), and CLP+SCFAs+GLPG0974 group (n = 15). Seven days after surgery, fecal samples were collected for microbiota composition and SCFA analysis from 6 mice in each group randomly. Behavioral test was applied to assess cognitive impairment at the same time. After that, mice were sacrificed and brain tissue was harvested for inflammatory cytokines analysis. Results: The levels of acetic acid (.57 ± 0.09 vs 2.00 ± 0.24, p < 0.001) and propionic acid (.32 ± 0.06 vs .66 ± 0.12, p = 0.002) were significantly decreased in the CLP group compared with the sham group. The administration of SCFAs significantly increased the levels of acetic acid (1.51 ± 0.12 vs. 0.57 ± 0.09, p < 0.001) and propionic acid (0.54 ± 0.03 vs. 0.32 ± 0.06, p = 0.033) in CLP+SCFAs group compared with CLP group. Relative abundance of SCFAs-producing bacteria, including Allobaculum (0.16 ± 0.14 vs. 15.21 ± 8.12, p = 0.037), Bacteroides (1.82 ± 0.38 vs. 15.21 ± 5.95, p = 0.002) and Bifidobacterium (0.16 ± 0.06 vs. 2.24 ± 0.48, p = 0.002), significantly decreased in the CLP group compared with the sham group. The behavioral tests suggested that cognitive function was impaired in SAE mice, which could be alleviated by SCFAs pretreatment. ELISA tests indicated that the levels of IL-1ß, IL-6, and TNF-α were elevated in SAE mice and SCFAs could lower them. However, the GPR43 antagonist, GLPG0974, could reverse the cognitive protective effect and anti-neuroinflammation effect of SCFAs. Conclusion: Our study suggested that in SAE, the levels of acetate and propionate decreased significantly, accompanied by gut microbiota dysbiosis, particularly a decrease in SCFAs-producing bacteria. GPR43 was essential for the anti-neuroinflammation and cognitive protective effect of SCFAs in SAE.

[Research progress of sepsis associated encephalopathy influenced by gut microbiota via efferocytosis].

Sepsis associated encephalopathy (SAE) is a severe disease secondary to sepsis, which is associated with increased mortality and causes long-term cognitive deficits in survivors. Recently, an increasing body of evidence has shown that gut microbiota is closely related to the central nervous system, and could influence brain function via microbiota-gut-brain axis. Therefore, in the occurrence and development of SAE, cholinergic anti-inflammatory pathway is one of the mechanisms by which gut microbiota could improve cognitive function. Efferocytosis, a process of eliminating apoptotic cells in the body, has anti-inflammatory effects and provides organ protection in sepsis. On the other hand, it could be enhanced by some metabolites of gut microbiota, making it another potential mechanism for gut microbiota regulating SAE. This review summarizes the mutual regulation of gut microbiota, efferocytosis and SAE, to explore potential mechanisms and therapeutic targets of SAE.

The Impact on 30-Day Mortality From a Brief Focused Ultrasound-Guided Management Protocol Immediately Before Emergency Noncardiac Surgery in Critically Ill Patients: A Multicenter Randomized Controlled Trial.

OBJECTIVE: To determine whether brief ultrasound-guided treatment of hemodynamic shock and respiratory failure immediately before emergency noncardiac surgery reduced 30-day mortality. DESIGN: Parallel, nonblinded, randomized trial with 1:1 allocation to control and intervention groups. SETTING: Twenty-eight major hospitals within China. PARTICIPANTS: Six-hundred sixty patients ≥14 years of age, scheduled for emergency noncardiac surgery with evidence of shock (heart rate >120 beat/min, systolic blood pressure< 90 mmHg or requiring inotrope infusion), or respiratory failure (Pulse Oxygen Saturation <92%, respiratory rate >20 beat/min, or requiring mechanical ventilation). INTERVENTIONS: A brief (<15 minutes) focused ultrasound of ventricular filling and function, lung, and peritoneal spaces, with predefined treatment recommendation based on the ultrasound was performed before surgery or standard care. MEASUREMENTS AND MAIN RESULTS: The primary outcome was 30-day mortality. Secondary outcomes included changes in medical or surgical diagnosis and management due to ultrasound, intensive care unit, and hospital stay and cost, and Short Form-8 quality-of-life scores. Although there were frequent changes in diagnosis (82%) and management (49%) after the ultrasound, mortality at 30 days was not different between groups (50 [15.7%] v 53 [16.3%]; odds ratio 1.05, 0.69-1.6, p = 0.826). There were no differences in the secondary outcomes of the days spent in the hospital (mean 13.8 days, 95% confidence interval [CI] 12.1-15.6 v 14.4 d, 11.8-17.1, p = 0.718) or intensive care unit (mean 9.3 days, 95% CI 7.7-11.0 v 8.7 d, 7.2-10.2, p = 0.562), hospital cost (USD$14.5K, 12.2-16.7 v 13.7, 11.5-15.9, p = 0.611) or Short Form-8 scores at one year (mean 80.9, 95% CI 78.4-83.3 v 79.7, 76.9-82.5, p = 0.54) between participants allocated to the ultrasound and control groups. CONCLUSIONS: In critically ill patients with hemodynamic shock or respiratory failure, a focused ultrasound-guided management did not reduce 30-day mortality but led to frequent changes in diagnosis and patient management.

CXCR5 down-regulation alleviates cognitive dysfunction in a mouse model of sepsis-associated encephalopathy: potential role of microglial autophagy and the p38MAPK/NF-κB/STAT3 signaling pathway.

BACKGROUND: Cognitive deficits are common in patients with sepsis. Previous studies in sepsis-associated encephalopathy (SAE) implicated the C-X-C chemokine receptor type (CXCR) 5. The present study used a mouse model of SAE to examine whether CXCR5 down-regulation could attenuate cognitive deficits. METHODS: Sepsis was induced in adult male C57BL/6 J and CXCR5-/- mice by cecal ligation and puncture (CLP). At 14-18 days after surgery, animals were tested in a Morris water maze, followed by a fear conditioning test. Transmission electron microscopy of hippocampal sections was used to assess levels of autophagy. Primary microglial cultures challenged with lipopolysaccharide (LPS) were used to examine the effects of short interfering RNA targeting CXCR5, and to investigate the possible involvement of the p38MAPK/NF-κB/STAT3 signaling pathway. RESULTS: CLP impaired learning and memory and up-regulated CXCR5 in hippocampal microglia. CLP activated hippocampal autophagy, as reflected by increases in numbers of autophagic vacuoles, conversion of microtubule-associated protein 1 light chain 3 (LC3) from form I to form II, accumulation of beclin-1 and autophagy-related gene-5, and a decrease in p62 expression. CLP also shifted microglial polarization to the M1 phenotype, and increased levels of IL-1ß, IL-6 and phosphorylated p38MAPK. CXCR5 knockout further enhanced autophagy but partially reversed all the other CLP-induced effects, including cognitive deficits. Similar effects on autophagy and cytokine expression were observed after knocking down CXCR5 in LPS-challenged primary microglial cultures; this knockdown also partially reversed LPS-induced up-regulation of phosphorylated NF-κB and STAT3. The p38MAPK agonist P79350 partially reversed the effects of CXCR5 knockdown in microglial cultures. CONCLUSIONS: CXCR5 may act via p38MAPK/NF-κB/STAT3 signaling to inhibit hippocampal autophagy during sepsis and thereby contribute to cognitive dysfunction. Down-regulating CXCR5 can restore autophagy and mitigate the proinflammatory microenvironment in the hippocampus.

Knockdown of VDAC1 alleviates the cognitive dysfunction secondary to sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE) is a serious and diffuse cerebral dysregulation with a high morbidity and mortality caused by sepsis. Mitophagy plays an important role in SAE, and microglial phagocytosis of apoptotic cells (efferocytosis) is the core of the brain regenerative response. Voltage dependent anion channel (VDAC1) is an important regulator of mitophagy. However, it remains unknown whether VDAC1 influences SAE progression by regulating mitophagy and efferocytosis. Herein, we explored the mechanism where knockdown of VDAC1 alleviated the cognitive dysfunction caused by sepsis-associated encephalopathy and further elucidated the underlying molecular mechanisms. SAE model in mice was established through caecal ligation and puncture (CLP). The increased mitophagy and decreased efferocytosis were observed by the transmission electron microscope (TEM) in the SAE model. Besides, immunoblot tests showed an interaction between autophagy and efferocytosis. Further behavior tests and TEM results indicated that knockdown of VDAC1 alleviated the cognitive dysfunction by decreasing the autophagy and increasing the efferocytosis in a PINK1/Parkin-dependent manner. Based on these results, we conclude that knockdown of VDAC1 alleviates the cognitive dysfunction in the CLP-induced SAE mouse model.