Incorporating preoperative and intraoperative data to predict postoperative pneumonia in elderly patients undergoing non-cardiothoracic surgery: The online two-stage prediction tool.

Background: Prior research on postoperative pneumonia (POP) risk models focused on preoperative factors but overlooked intraoperative variables vital for precision. These models also neglected the higher-risk elderly population. This study seeks to develop and evaluate preoperative and combined models to predict POP risk in elderly patients undergoing non-cardiothoracic surgery.

Optimizing anesthesia management based on early identification of electroencephalogram burst suppression risk in non-cardiac surgery patients: a visualized dynamic nomogram.

Background: Burst suppression (BS) is a specific electroencephalogram (EEG) pattern that may contribute to postoperative delirium and negative outcomes. Few prediction models of BS are available and some factors such as frailty and intraoperative hypotension (IOH) which have been reported to promote the occurrence of BS were not included. Therefore, we look forward to creating a straightforward, precise, and clinically useful prediction model by incorporating new factors, such as frailty and IOH.

Practical prognostic tools to predict the risk of postoperative delirium in older patients undergoing cardiac surgery: visual and dynamic nomograms.

Purpose: Postoperative Delirium (POD) has an incidence of up to 65% in older patients undergoing cardiac surgery. We aimed to develop two dynamic nomograms to predict the risk of POD in older patients undergoing cardiac surgery.

Methods: This was a single-center retrospective cohort study, which included 531 older patients who underwent cardiac surgery from July 2021 to June 2022 at Nanjing First Hospital, China.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

[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.

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.

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).

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.

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.

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 ( = 10), cecal ligation and puncture group (CLP group, = 15), CLP+SCFAs group ( = 15), and CLP+SCFAs+GLPG0974 group ( = 15). Seven days after surgery, fecal samples were collected for microbiota composition and SCFA analysis from 6 mice in each group randomly.

[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.

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.

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.

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.