In recent years， with the emergence of large language models like ChatGPT and DeepSeek into the public domain， artificial intelligence （AI） has become one of the most rapidly developing fields of the 21st century. As AI continues to evolve， new models are constantly emerging， particularly through the integration of multimodal data that enables more comprehensive information capture and analysis， demonstrating significant value in supporting clinical anesthesia decision-making. Preoperatively， AI can assist in evaluating patients’ overall health status， aid in selecting appropriate anesthesia methods， and predict potential risks such as difficult airways. Intraoperatively， AI can collaborate with anesthesiologists to monitor physiological parameters and optimize anesthesia management strategies， especially in predicting hypotension and improving automated drug infusion control systems. Postoperatively， AI can predict pulmonary complications， postoperative delirium， and major adverse cardiovascular events， thereby accelerating recovery and improving long-term survival.This article analyzes recent advances in AI technology in medicine worldwide， systematically reviews its application scenarios in clinical anesthesia， and provides detailed accounts of progress in preoperative risk assessment， formulation of personalized anesthesia plans， intraoperative monitoring and decision support， postoperative recovery， and long-term follow-up. It further highlights the need for future research to strengthen multimodal database construction， enhance model generalizability， develop explainable frameworks， and improve ethical governance. By summarizing current achievements and challenges， this review offers valuable reference for anesthesiologists and researchers， underscores the role of AI in driving the intelligent transformation of anesthesiology， and provides guidance for subsequent research directions.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.

Objective To establish an expert consensus on in-hospital transfer safety management for patients undergoing invasive mechanical ventilation,providing guidance for clinical medical teams to conduct standardized transfers,reduce transfer risks,and ensure patient safety.Methods Through systematic searching,screening,evaluation,and summary of evidence related to in-hospital transfer safety management for patients on invasive mechanical ventilation,we extracted recommendations to form a preliminary draft of the expert consensus.From July to October 2024,totally 2 rounds of expert consultations,and 2 rounds of expert reviews were conducted,and the content was refined and finalized based on expert feedback.Results The final consensus encompasses 9 aspects,including transfer assessment and decision-making,pre-transfer preparation of medical staff,pre-transfer patient preparation,pre-transfer equipment preparation,pre-transfer medication preparation,monitoring and intervention during transfer,emergency events and management,transfer handover and documentation,and post-transfer management.Conclusion This consensus demonstrates strong practicality and operability,offering professional guidance for enhancing the safety of in-hospital transfers for patients on invasive mechanical ventilation.

OBJECTIVE To explore the value of microbiological rapid on-site evaluation(M-ROSE)technique in treatment of the patients with severe community-acquired pneumonia(SCAP).METHODS A total of 124 patients with SCAP who were treated in the department of respiratory and critical care medicine of The Fourth Medical Center of Chinese PLA General Hospital from Sep.2023 to Dec.2024 were enrolled in the study and were random-ly divided into the M-ROSE group and the control group in a 1∶1 ratio based on the status of M-ROSE for bron-choalveolar lavage fluid(BALF).The M-ROSE test and conventional etiological test[metagenomic next genera-tion sequencing(mNGS),smear,culture]were performed for the M-ROSE group,and the conventional etiologi-cal test was only carried out for the control group.The baseline data,symptoms and signs,C-reactive protein lev-el,treatment status and outcomes were observed and compared between the two groups of patients.RESULTS A-mong the 62 patients for whom the BALF specimens were detected with M-ROSE,45(72.58％)patients showed the consistent test result for fungi with mNGS,47(75.81％)patients showed the same test result for cocci with mNGS,and 30(48.39％)patients showed the same test result for bacilli with mNGS.The duration of the M-ROSE test was 1.50(1.50,2.00)h,shorter than that of the smear,culture and mNGS(P＜0.05).The body temperature returning to the normal and the property,volume of sputum were improved more early in days in the M-ROSE group than in the control group after the anti-infection treatment(P＜0.05);the level of inflammatory factor CRP declined more quickly in the M-ROSE group than in the control group(P＜0.05);the effective rates of treatment of the M-ROSE group were higher than those of the control group after the hospitalization for 3,5 and 7 days(P＜0.05).CONCLUSION The M-ROSE test for BALF may facilitate the rapid etiological diagnosis for the patients with SCAP in early stage,provide guidance for the anti-infection treatment strategies,and accelerate the improvement of symptoms and inflammatory factors;it has certain clinical application value.

Objective:To investigate the relationship of several adiposity-related anthropometric parameters, including BMI, waist circumference (WC), waist-to-hip ratio (WHR), body fat percentage (BFP) and indoles in plasma with the incidence of atherosclerotic cardiovascular disease (ASCVD) in adults in China.Methods:In China Kadoorie Biobank (CKB) study, blood samples were collected from 2 183 participants in the first resurvey in 2008 to detect indoles. Participants' body weight, body height, WC, hip circumference, and BFP were measured at baseline survey in 2004 and resurvey in 2008, the BMI and WHR were calculated with standardized methods. The long-term follow-up of all participants started from the completion of the resurvey in 2008 until the occurrence of incident ASCVD, death, loss to follow-up or until December 31, 2018. CKB ascertained outcome status (incident ASCVD) through death and disease registries and national health insurance databases, supplemented by active follow-up. Multivariate linear regression model was used to estimate the associations of anthropometric measurements at baseline survey and the first resurvey, and changes in these measurements with 3 indoles [indole, indole-3-acetic acid (IAA), and indole-3-propionic acid (IPA)]. Cox proportional hazard regression model was used to estimate the associations between indoles and the risk for ASCVD.Results:Anthropometric measurements at baseline survey or the first resurvey were negatively associated with plasma IPA level. The regression coefficient ( β) of baseline BMI (per 1.0 kg/m 2) with 0.1 standard deviation ( SD) IPA was -0.23 (95% CI: -0.36 - -0.10) (false discovery rate=0.004). After adjusting for baseline BMI, the β of baseline WC, WHR and BFP with 0.1 SD IPA were -0.09 (95% CI: -0.18 - -0.01), -0.12 (95% CI: -0.19 - -0.05), and -0.20 (95% CI: -0.32 - -0.08), respectively. The annual change in BMI (difference between BMI in 2008 and 2004 divided by the time interval) was associated with indole and IAA, with β of 1.40 (95% CI: 0.58 - 2.21) and -1.07 (95% CI: -1.91 - -0.23), respectively, at each 0.1 increase of SD. Over a median ( Q1, Q3) follow-up of 10.46 (10.36, 10.53) years after 2008 resurvey, 236 cases of ASCVD were recorded. IAA and IPA levels were negatively associated with the risk for ASCVD, with hazard ratios for one SD increase of IAA and IPA of 0.87 (95% CI: 0.76 - 0.99) and 0.84 (95% CI: 0.73 - 0.96), respectively. Conclusions:Our results suggested that anthropometric measurements and their changing trends affect the levels of plasma imicrobial tryptophan metabolite levels, decreased levels of IAA and IPA levels are associated with increased risk of ASCVD and indoles in plasma including IPA and IAA might be the mediating factors for adiposity-induced ASCVD.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

Objective:To describe the prevalence of preserved ratio impaired spirometry (PRISm) in participants from the China Kadoorie Biobank (CKB) and explore the influencing factors.Methods:The CKB project conducted the baseline survey, the first and the second resurvey in 2004-2008, 2008, and 2013-2014, respectively. Based on the lung function tests, the participants were categorized into three groups: regular, PRISm, and airflow obstruction. The prevalence of PRISm was reported by gender, age, and region at the baseline survey. The secular trend in the prevalence of PRISm was described during the three surveys. Finally, we used the multiple logistic regression model to examine the factors related to PRISm in the baseline survey.Results:After standardization for gender, age, and region according to the sixth national census data in 2010, the overall prevalence of PRISm and airflow obstruction among the 434 760 participants at baseline was 24.8% and 6.1%, respectively. The prevalence of PRISm was higher in rural (25.4%) than that in urban areas (24.3%). Of the 10 study regions, Gansu had the highest prevalence of PRISm (56.0%), while Henan had the lowest (15.4%). After standardization for gender, age, and region according to the baseline population, the prevalence of PRISm decreased from 24.9% at baseline to 15.7% in the second resurvey, and the prevalence of airflow obstruction increased from 5.9% to 21.4%. Unmarried status, current smoking, using solid fuels for cooking, low body weight, being overweight, obesity, and central obesity were associated with an increased risk of PRISm. In contrast, higher education attainments, increased household income, and maintaining a specific degree of physical activity were associated with a reduced risk of PRISm.Conclusions:The prevalence of PRISm was high in adults aged 40 years and above in China, and it varied by sociodemographic and lifestyle factors.

Objective:To explore the impacts of smoking on the need for respiratory support and mortality in patients hospitalized with pneumonia.Methods:A total of 24 367 patients hospitalized with pneumonia from 2009 to 2017 in the China Kadoorie Biobank, were included. Smoking status was self-reported, and data regarding respiratory support during hospitalization and mortality during follow-up were obtained from medical claims and death registries, respectively. OR, HR, and 95% CI were calculated and adjusted for potential confounders using logistic regression models and Cox proportional hazards regression models, respectively. Results:Among males, current smokers or those who quit smoking due to illness had higher risks of requiring respiratory support ( OR=1.15, 95% CI: 1.03-1.29), 1-year mortality ( HR=1.66, 95% CI: 1.32-2.08), and 5-year mortality ( HR=1.32, 95% CI: 1.13-1.54) following pneumonia hospitalization compared to nonsmokers. Male smokers who started smoking at a younger age or with longer smoking duration had the highest mortality risks (trend test both P<0.05). Female current smokers or those who quit smoking due to illness had higher risks of 1-year mortality ( HR=1.62, 95% CI: 1.17-2.23) and 5-year mortality ( HR=1.33, 95% CI: 1.06-1.67). We found no statistically significant difference in 90-day mortality between current smokers/those who quit smoking due to illness and nonsmokers. Conclusions:Smoking was associated with higher risks of requiring respiratory support and mortality in patients hospitalized with pneumonia, especially among males and heavy smokers. These findings highlight the need for targeted strategies to promote smoking cessation in patients hospitalized with pneumonia.

Objective:To identify individuals with accelerated aging under the frailty index (FI) as a proxy indicator of biological age, and to investigate the associations of age at menarche, age at menopause, and reproductive lifespan with frailty status and multi-timepoint FI trajectories among Chinese adult women.Methods:The current study included 302 471 women from the China Kadoorie Biobank 2004-2008 baseline survey data. Their age at menarche and menopause were self-reported, and the duration of reproductive lifespan was calculated by subtracting the two ages. The baseline FI was constructed using 28 baseline variables, including diseases, symptoms, and anthropometric measurements. Frailty status was categorized into three groups: non-frail (FI≤0.10), pre-frail (0.10