AIM To establish a UPLC-MS/MS method for the simultaneouscontent determination of notoginsenosides R1,Fc,Fe,ginsenosides Re,Rg1,Rf,F3,Rg2,Ra2,Rb1,Ro,Rc,F1,Ra1,Rb2,Rb3,Rd,F2,Rg5,chikusetsusaponin Ⅳ a,20(S)-ginsenoside Rg3 and 20(R)-ginsenoside Rg3 in Dengzhan Shengmai Capsules.METHODS The analysis was performed on a 35 ℃ thermostatic Accucore Phenyl Hexyl column(2.1 mm×100 mm,2.6 μm),with the mobile phase comprising of 0.1％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and heated electrospray ionization source was adopted in negative ion scanning with parallel reaction monitoring mode.Subsequently,cluster analysis,principal component analysis and partial least squares discriminant analysis were adopted.RESULTS Twenty-two saponins showed good linear relationships within their own ranges(r ≥ 0.996 4),whose average recoveries were 98.5％-101.6％with the RSDs of 1.3％-5.2％.Ten batches of samples were clustered into four types,three principal components demonstrated the accumulative variance contribution rate of 90.265％,ginsenosides Rb1,Rg2,Rb2,Rd,Rg1,Ro,Rf,Re,Rg5,Rc were taken as potential quality markers.CONCLUSION This simple,efficient,accurate and sensitive method can be used for the quality control of Dengzhan Shengmai Capsules.

AIM To establish a UPLC-MS/MS method for the simultaneouscontent determination of notoginsenosides R1,Fc,Fe,ginsenosides Re,Rg1,Rf,F3,Rg2,Ra2,Rb1,Ro,Rc,F1,Ra1,Rb2,Rb3,Rd,F2,Rg5,chikusetsusaponin Ⅳ a,20(S)-ginsenoside Rg3 and 20(R)-ginsenoside Rg3 in Dengzhan Shengmai Capsules.METHODS The analysis was performed on a 35 ℃ thermostatic Accucore Phenyl Hexyl column(2.1 mm×100 mm,2.6 μm),with the mobile phase comprising of 0.1％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and heated electrospray ionization source was adopted in negative ion scanning with parallel reaction monitoring mode.Subsequently,cluster analysis,principal component analysis and partial least squares discriminant analysis were adopted.RESULTS Twenty-two saponins showed good linear relationships within their own ranges(r ≥ 0.996 4),whose average recoveries were 98.5％-101.6％with the RSDs of 1.3％-5.2％.Ten batches of samples were clustered into four types,three principal components demonstrated the accumulative variance contribution rate of 90.265％,ginsenosides Rb1,Rg2,Rb2,Rd,Rg1,Ro,Rf,Re,Rg5,Rc were taken as potential quality markers.CONCLUSION This simple,efficient,accurate and sensitive method can be used for the quality control of Dengzhan Shengmai Capsules.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

OBJECTIVE: To investigate a family with congenital dysfibrinogenemia, and analyze the risk of hemorrhage and thrombosis and blood transfusion strategies. METHODS: Prothrombin time (PT), activated partial thromboplastin time (APTT) and thrombin time (TT) of the proband and her family members were detected by automatic coagulometer, fibrinogen (Fg) activity and antigen were detected by Clauss method and PT algorithm respectively. Meanwhile, thromboelastometry was analyzed for proband and her family members. Then, peripheral blood samples of the proband and her family members were collected, and all exons of FGA, FGB and FGG and their flanks were amplified by PCR and sequenced to search for gene mutations. RESULTS: The proband had normal APTT and PT, slightly prolonged TT, reduced level of Fg activity (Clauss method). The Fg of the proband's aunt, son and daughter all decreased to varying degrees. The results of thromboelastogram indicated that Fg function of the proband and her family members (except her son) was basically normal. Gene analysis showed that there were 6233 G/A (p.AαArg35His) heterozygous mutations in exon 2 of FGA gene in the proband, her children and aunt. In addition, 2 polymorphic loci were found in the family, they were FGA gene g.9308A/G (p.AαThr331Ala) and FGB gene g.12628G/A (p.BβArg478Iys) polymorphism, respectively. The proband was injected with 10 units of cryoprecipitate 2 hours before delivery to prevent bleeding, and no obvious bleeding occurred during and after delivery. CONCLUSION Heterozygous mutation of 6233G/A (p.AαArg35His) of FGA gene is the biogenetic basis of the disease in this family with congenital dysfibrinogenemia.
Humans ; Child ; Female ; Fibrinogen/genetics* ; Pedigree ; Afibrinogenemia/genetics* ; Mutation ; Blood Transfusion

AIM:To observe the clinical effect of ranibizumab combined with 577nm micropulse laser in the treatment of severe diabetic macular edema(DME). METHODS:There were 52 eyes of 52 patients diagnosed with severe DME who admitted to the People's Hospital of Guangxi Zhuang Autonomous Region from June 2016 to September 2019. The patients were randomly divided into the observation group(26 patients with 26 eyes, treated with ranibizumab combined with 577nm micropulse laser)and the control group(26 patients with 26 eyes, treated with ranibizumab alone). Patients in both groups received intravitreal injection of ranibizumab with “3+PRN” regimen. Followed up at 9mo after treatment to observe the central macular thickness(CMT), the best corrected visual acuity(BCVA)and the times of intravitreal injection of ranibizumab in the two groups.RESULTS:Compared with before treatment, the CMT and BCVA of the two groups were significantly improved at each time point after treatment(all P<0.001), but there was no difference between the two groups(P>0.05). During the follow-up period, the times of vitreous injection of ranibizumabin the observation group was significantly less than that in the control group(5.88±1.24 times vs 7.12±1.24 times, P=0.001). CONCLUSION:Both ranibizumab combined with 577nm micropulse laser and ranibizumab alone are effective in reducing edema and improving vision in patients with severe DME, but the combination therapy reduces the times of injection.

Objective: To investigate the relationship between body mass index (BMI) and sexual development in Chinese children. Methods: A nationwide multicenter and population-based large cross-sectional study was conducted in 13 provinces, autonomous regions and municipalities of China from January 2017 to December 2018. Data on sex, age, height, weight were collected, BMI was calculated and sexual characteristics were analyzed. The subjects were divided into four groups based on age, including ages 3-<6 years, 6-<10 years, 10-<15 years and 15-<18 years. Multiple Logistic regression models were used for evaluating the associations of BMI with sexual development in children. Dichotomous Logistic regression was used to compare the differences in the distribution of early and non-early puberty among normal weight, overweight and obese groups. Curves were drawn to analyze the relationship between the percentage of early puberty and BMI distribution in girls and boys at different Tanner stages. Results: A total of 208 179 healthy children (96 471 girls and 111 708 boys) were enrolled in this study. The OR values of B2, B3 and B4+ in overweight girls were 1.72 (95%CI: 1.56-1.89), 3.19 (95%CI: 2.86-3.57), 7.14 (95%CI: 6.33-8.05) and in obese girls were 2.05 (95%CI: 1.88-2.24), 4.98 (95%CI: 4.49-5.53), 11.21 (95%CI: 9.98-12.59), respectively; while the OR values of G2, G3, G4+ in overweight boys were 1.27 (95%CI: 1.17-1.38), 1.52 (95%CI: 1.36-1.70), 1.88 (95%CI: 1.66-2.14) and in obese boys were 1.27 (95%CI: 1.17-1.37), 1.59 (95%CI: 1.43-1.78), and 1.93 (95%CI: 1.70-2.18) (compared with normal weight Tanner 1 group,all P<0.01). Analysis in different age groups found that OR values of obese girls at B2 stage and boys at G2 stage were 2.02 (95%CI: 1.06-3.86) and 2.32 (95%CI:1.05-5.12) in preschool children aged 3-<6 years, respectively (both P<0.05). And in the age group of 6-10 years, overweight girls had a 5.45-fold risk and obese girls had a 12.54-fold risk of B3 stage compared to girls with normal BMI. Compared with normal weight children, the risk of early puberty was 2.67 times higher in overweight girls, 3.63 times higher in obese girls, and 1.22 times higher in overweight boys, 1.35 times higher in obese boys (all P<0.01). Among the children at each Tanner stages, the percentage of early puberty increased with the increase of BMI, from 5.7% (80/1 397), 16.1% (48/299), 13.8% (27/195) to 25.7% (198/769), 65.1% (209/321), 65.4% (157/240) in girls aged 8-<9, 10-<11 and 11-<12 years, and 6.6% (34/513), 18.7% (51/273), 21.6% (57/264) to 13.3% (96/722), 46.4% (140/302), 47.5% (105/221) in boys aged 9-<10, 12-<13 and 13-<14 years, respectively. Conclusions: BMI is positively correlated with sexual development in both Chinese boys and girls, and the correlation is stronger in girls. Obesity is a risk factor for precocious puberty in preschool children aged 3-<6 years, and 6-<10 years of age is a high risk period for early development in obese girls.
Adolescent ; Body Mass Index ; Child ; Child, Preschool ; China/epidemiology* ; Cross-Sectional Studies ; Female ; Humans ; Male ; Obesity/epidemiology* ; Overweight/epidemiology* ; Puberty ; Puberty, Precocious ; Sexual Development

Objective: To investigate the incidence and trend of short-term outcomes among preterm infants born <34 weeks' gestation. Methods: A secondary analysis of data from the standardized database established by a multicenter cluster-randomized controlled study "reduction of infection in neonatal intensive care units (NICU) using the evidence-based practice for improving quality (REIN-EPIQ) study". This study was conducted in 25 tertiary NICU. A total of 27 192 infants with gestational age <34 weeks at birth and admitted to NICU within the first 7 days of life from May 2015 to April 2018 were enrolled. Infants with severe congenital malformation were excluded. Descriptive analyses were used to describe the mortality and major morbidities of preterm infants by gestational age groups and different admission year groups. Cochran-Armitage test and Jonckheere-Terpstra test were used to analyze the trend of incidences of mortality and morbidities in 3 study-years. Multiple Logistic regression model was constructed to analyze the differences of outcomes in 3 study-years adjusting for confounders. Results: A total of 27 192 preterm infants were enrolled with gestational age of (31.3±2.0) weeks at birth and weight of (1 617±415) g at birth. Overall, 9.5% (2 594/27 192) of infants were discharged against medical advice, and the overall mortality rate was 10.7% (2 907/27 192). Mortality for infants who received complete care was 4.7% (1 147/24 598), and mortality or any major morbidity was 26.2% (6 452/24 598). The incidences of moderate to severe bronchopulmonary dysplasia, sepsis, severe intraventricular hemorrhage or periventricular leukomalacia, proven necrotizing enterocolitis, and severe retinopathy of prematurity were 16.0% (4 342/27 192), 11.9% (3 225/27 192), 6.8% (1 641/24 206), 3.6% (939/25 762) and 1.5% (214/13 868), respectively. There was a decreasing of the overall mortality (P<0.001) during the 3 years. Also, the incidences for sepsis and severe retinopathy of prematurity both decreased (both P<0.001). However, there were no significant differences in the major morbidity in preterm infants who received complete care during the 3-year study period (P=0.230). After adjusting for confounders, infants admitted during the third study year showed significantly lower risk of overall mortality (adjust OR=0.62, 95%CI 0.55-0.69, P<0.001), mortality or major morbidity, moderate to severe bronchopulmonary dysplasia, sepsis and severe retinopathy of prematurity, compared to those admitted in the first study year (all P<0.05). Conclusions: From 2015 to 2018, the mortality and major morbidities among preterm infants in Chinese NICU decreased, but there is still space for further efforts. Further targeted quality improvement is needed to improve the overall outcome of preterm infants.
Bronchopulmonary Dysplasia/epidemiology* ; Gestational Age ; Humans ; Infant ; Infant Mortality/trends* ; Infant, Newborn ; Infant, Premature ; Infant, Premature, Diseases/epidemiology* ; Patient Discharge ; Retinopathy of Prematurity/epidemiology* ; Sepsis/epidemiology*

OBJECTIVES: To study the characteristics of amino acid metabolism in preterm infants in Guangxi, China. METHODS: A retrospective analysis was performed on the medical data of 30 757 neonates who underwent the screening for inherited metabolic diseases and had negative results in Guangxi Neonatal Disease Screening Center from 2018 to 2020. Among these neonates, there were 28 611 normal full-term infants (control group) and 2 146 preterm infants (preterm birth group). According to gestational age, the preterm infants were further divided into four groups: very preterm (n=209), moderately preterm (n=307), and late preterm group (n=1 630). According to birth weight, they were divided into three groups: very low birth weight group (n=161), low birth weight group (n=1 085), and normal birth weight group (n=900). According to blood collection time, they were divided into three groups: 3-7 days group (n=1 664), 8-14 days group (n=314) and 15-28 days group (n=168). Tandem mass spectrometry was performed to measure the levels of 11 amino acids in dried blood spots, which were then compared between groups. RESULTS: After adjustment for confounding factors, there were significant differences in the levels of 11 amino acids among different gestational age groups (P<0.05), and significant differences were observed in the levels of the 11 amino acids between the control group and the various preterm groups (except for citrulline and methionine in the late preterm group). There were significant differences in the levels of 11 amino acids among different birth weight groups (P<0.05). Except for ornithine, there were significant differences in the levels of other amino acids among the different blood collection time groups (P<0.05). CONCLUSIONS Gestational age, birth weight and blood collection time all affect amino acid metabolism in preterm infants in Guangxi, China. This provides a basis for the laboratory to establish the reference standard and clinical interpretation of blood amino acid levels in preterm infants, and to improve the nutritional metabolism of preterm infants.
Amino Acids ; China ; Gestational Age ; Humans ; Infant ; Infant, Newborn ; Infant, Premature ; Infant, Very Low Birth Weight ; Premature Birth ; Retrospective Studies

BACKGROUND: Perioperative treatment has become an increasingly important aspect of the management of patients with non-small cell lung cancer (NSCLC). Small-scale clinical studies performed in recent years have shown improvements in the major pathological remission rate after neoadjuvant therapy, suggesting that it will soon become an important part of NSCLC treatment. Nevertheless, neoadjuvant immunotherapy may be accompanied by serious adverse reactions that lead to delay or cancelation of surgery, additional illness, and even death, and have therefore attracted much attention. The purpose of the clinical recommendations is to form a diagnosis and treatment plan suitable for the current domestic medical situation for the immune-related adverse event (irAE). METHODS: This recommendation is composed of experts in thoracic surgery, oncologists, thoracic medicine and irAE related departments (gastroenterology, respirology, cardiology, infectious medicine, hematology, endocrinology, rheumatology, neurology, dermatology, emergency section) to jointly complete the formulation. Experts make full reference to the irAE guidelines, large-scale clinical research data published by thoracic surgery, and the clinical experience of domestic doctors and publicly published cases, and repeated discussions in multiple disciplines to form this recommendation for perioperative irAE. RESULTS: This clinical recommendation covers the whole process of prevention, evaluation, examination, treatment and monitoring related to irAE, so as to guide the clinical work comprehensively and effectively. CONCLUSIONS Perioperative irAE management is an important part of immune perioperative treatment of lung cancer. With the continuous development of immune perioperative treatment, more research is needed in the future to optimize the diagnosis and treatment of perioperative irAE.