OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE To study the pharmacokinetic characteristics of antidepressant active components from Jiaotai pills in healthy subjects. METHODS Eight healthy subjects （3 males and 5 females） were recruited and given a single oral dose of 8.55 g of Jiaotai pills. Venous blood samples were collected before administration （0 h） and at intervals from 0.25 to 36.0 hours post- administration. After treating the plasma samples with protein precipitation， the blood concentrations of the antidepressant active ingredients （coptisine， berberine， magnoflorine， and palmatine） in Jiaotai pills were determined using liquid chromatography- tandem mass spectrometry （LC-MS/MS） method. DAS 2.0 software was employed to calculate the pharmacokinetic parameters of healthy subjects [half-life （t1/2）， peak concentration （cmax）， time to peak concentration （tmax）， area under the concentration-time curve （AUC）， and mean residence time （MRT）] using a non-compartmental model. RESULTS After healthy subjects took Jiaotai pills， the drug-time curve of the four antidepressant active ingredients conforms to a two-compartment model and tmax values were similar， with all reaching peak blood concentrations within 2.00 to 4.00 hours post-administration. However， the t1/2 and MRT of coptisine and berberine were significantly longer than that of magnoflorine and palmatine. There were also significant differences in the AUC and cmax among the four antidepressant active ingredients， with magnoflorine exhibiting markedly higher AUC0-t and cmax compared to the other three components. CONCLUSIONS In this study，LC-MS/MS is used to analyze the pharmacokinetic characteristics of the antidepressant active ingredients from Jiaotai pills in healthy subjects， can provide valuable references for the clinical application of Jiaotai pills.

ObjectiveThis paper aims to investigate the effects of icariin on spermatogenesis in mice with exercise-induced fatigue and explore the underlying mechanisms. MethodsICR male mice were screened by swimming and randomly divided into normal group， model group， vitamin C group， icariin groups with low， medium， and high doses， and medium-dose icariin+N-nitro-L-arginine methyl ester （L-NAME） group， with 10 mice per group. Except for the normal group， all the other groups underwent weighted swimming training to establish an exercise-induced fatigue model. No gavage was administered during the first two weeks of the weighted training. From week three to four， the icariin groups with low， medium， and high doses received 0.03， 0.06， and 0.12 g·kg^-1 icariin via gavage， respectively. The vitamin C group received 0.2 g·kg^-1 vitamin C. The L-NAME group received 0.06 g·kg^-1 icariin and 0.01 g·kg^-1 L-NAME via intraperitoneal injection. The normal and model groups received equivalent physiological saline. After the experiment， body weight and the last exhaustive swimming time were recorded. Blood urea nitrogen （BUN）， lactate （LA）， lactate dehydrogenase （LDH）， malondialdehyde （MDA）， testicular testosterone （T）， testicular Ca²⁺/Mg²⁺-adenosine triphosphatase （ATPase） （micro-assay）， and the levels of testicular cyclic guanosine monophosphate （cGMP） were measured by using kits. Sperm CD46 levels were detected by flow cytometry. Testicular seminiferous tubules were observed via hematoxylin-eosin （HE） staining， and the testicular morphometric score （TMS） was used to evaluate the spermatogenic function. Protein expression of regucalcin （RGN， SMP30）， cGMP-dependent protein kinase 1 （PKG）， and cGMP-dependent protein kinase anchoring protein （GKAP1） was detected by Western blot. Testicular regucalcin expression was examined by immunofluorescence （IF）. The epididymal sperm quality of mice was observed under a microscope. Fluorescence-stained sections of stimulated by retinoic acid gene 8 （STRA8）， synaptonemal complex protein 3 （SCP3）， and transition protein 1（TNP1） in testicular seminiferous tubules were assessed by immunohistochemistry （IHC）. ResultsCompared with the normal group， the model group showed decreased body weight and exhaustive swimming time （P<0.01）， significantly increased fatigue markers （LA， LDH， and BUN） and lipid peroxidation product MDA （P<0.01）， reduced testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， decreased sperm motility， sperm count， and TMS scores， and downregulated the expression of STRA8， SCP3， and TNP1. Compared with the model group， the icariin group with high dose exhibited increased exhaustive swimming time （P<0.01）， reduced LA， LDH， BUN， and MDA levels （P<0.01）， elevated superoxide dismutase （SOD） （P<0.01）， upregulated testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， improved sperm motility， sperm count， and TMS scores， and enhanced STRA8， SCP3， and TNP1 expression. Compared with the L-NAME group， the icariin group with medium dose showed increased expression of STRA8， SCP3， and TNP1 in the testicular tissue （P<0.01） and elevated cGMP and GKAP1 levels （P<0.01）. ConclusionExercise-induced fatigue reduces the expression of RGN and cGMP/PKG/GKAP1 in mice， thereby causing abnormal spermatogenesis and impairing reproductive function in mice. Icariin ameliorates spermatogenic dysfunction in exercise-induced fatigue mice by promoting the expression of RGN and cGMP/PKG/GKAP1， thereby mitigating the damage of exercise-induced fatigue to the reproductive system.

ObjectiveThis paper aims to investigate the effects of icariin on spermatogenesis in mice with exercise-induced fatigue and explore the underlying mechanisms. MethodsICR male mice were screened by swimming and randomly divided into normal group， model group， vitamin C group， icariin groups with low， medium， and high doses， and medium-dose icariin+N-nitro-L-arginine methyl ester （L-NAME） group， with 10 mice per group. Except for the normal group， all the other groups underwent weighted swimming training to establish an exercise-induced fatigue model. No gavage was administered during the first two weeks of the weighted training. From week three to four， the icariin groups with low， medium， and high doses received 0.03， 0.06， and 0.12 g·kg^-1 icariin via gavage， respectively. The vitamin C group received 0.2 g·kg^-1 vitamin C. The L-NAME group received 0.06 g·kg^-1 icariin and 0.01 g·kg^-1 L-NAME via intraperitoneal injection. The normal and model groups received equivalent physiological saline. After the experiment， body weight and the last exhaustive swimming time were recorded. Blood urea nitrogen （BUN）， lactate （LA）， lactate dehydrogenase （LDH）， malondialdehyde （MDA）， testicular testosterone （T）， testicular Ca²⁺/Mg²⁺-adenosine triphosphatase （ATPase） （micro-assay）， and the levels of testicular cyclic guanosine monophosphate （cGMP） were measured by using kits. Sperm CD46 levels were detected by flow cytometry. Testicular seminiferous tubules were observed via hematoxylin-eosin （HE） staining， and the testicular morphometric score （TMS） was used to evaluate the spermatogenic function. Protein expression of regucalcin （RGN， SMP30）， cGMP-dependent protein kinase 1 （PKG）， and cGMP-dependent protein kinase anchoring protein （GKAP1） was detected by Western blot. Testicular regucalcin expression was examined by immunofluorescence （IF）. The epididymal sperm quality of mice was observed under a microscope. Fluorescence-stained sections of stimulated by retinoic acid gene 8 （STRA8）， synaptonemal complex protein 3 （SCP3）， and transition protein 1（TNP1） in testicular seminiferous tubules were assessed by immunohistochemistry （IHC）. ResultsCompared with the normal group， the model group showed decreased body weight and exhaustive swimming time （P<0.01）， significantly increased fatigue markers （LA， LDH， and BUN） and lipid peroxidation product MDA （P<0.01）， reduced testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， decreased sperm motility， sperm count， and TMS scores， and downregulated the expression of STRA8， SCP3， and TNP1. Compared with the model group， the icariin group with high dose exhibited increased exhaustive swimming time （P<0.01）， reduced LA， LDH， BUN， and MDA levels （P<0.01）， elevated superoxide dismutase （SOD） （P<0.01）， upregulated testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， improved sperm motility， sperm count， and TMS scores， and enhanced STRA8， SCP3， and TNP1 expression. Compared with the L-NAME group， the icariin group with medium dose showed increased expression of STRA8， SCP3， and TNP1 in the testicular tissue （P<0.01） and elevated cGMP and GKAP1 levels （P<0.01）. ConclusionExercise-induced fatigue reduces the expression of RGN and cGMP/PKG/GKAP1 in mice， thereby causing abnormal spermatogenesis and impairing reproductive function in mice. Icariin ameliorates spermatogenic dysfunction in exercise-induced fatigue mice by promoting the expression of RGN and cGMP/PKG/GKAP1， thereby mitigating the damage of exercise-induced fatigue to the reproductive system.

Objective To explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

With the rising prevalence of chronic diseases and an aging population,China's traditional segmented health-care delivery model is increasingly inadequate for meeting the growing demand for long-term,systematic health management.In response,the"Physician-Pharmacist Co-management"model has emerged,aiming to enhance the quality and continuity of care through close collaboration between physicians and pharmacists.This paper starts from the concept and origin of"Physician-Phar-macist Co-management"model,focusing on its China-specific advantages shaped by national healthcare policies and clinical real-ities.Unlike the internationally recognized Collaborative Drug Therapy Management(CDTM)model,the Chinese approach re-flects local healthcare structures and needs.Using obstructive pulmonary disease(COPD)as a case study,we examine the mod-el's application and value in managing chronic respiratory diseases.Data indicate that,after the implementation of"physician-pharmacist co-management"model in COPD patients,the CAT score decreased by approximately 24％,the annual rate of acute exacerbation-related hospitalizations declined by about 72％,and the proportion of patients with regular pulmonary rehabilitation exercise habits increased by roughly 3.3-fold.Additionally,the percentage of patients without adverse reactions rose from 47.37％to 64.41％,and the vaccination rate increased by about 2.7-fold.These findings demonstrate the model's significant advantages in improving clinical outcomes,enhancing patient adherence,and reducing healthcare costs.Despite benefits,howev-er,the"Physician-Pharmacist Co-management"model in China faces several challenges,including limited public awareness,gaps in pharmacist training,and insufficient policy support.To address these challenges,this study recommends strengthening public education,establishing comprehensive evaluation systems for pharmaceutical professionals,and improving incentive mech-anisms.Overall,the findings suggest that the"Physician-Pharmacist Co-management"model holds considerable promise for im-proving the quality of chronic disease management,enhancing patient adherence,and optimizing healthcare resource utilization in China.

Objective To investigate whether Piceatannol(PIC)improves diabetic retinopathy(DR)mediated by microglial polarization and to elucidate the underlying molecular mechanisms.Methods Network pharmacology and bioinformatics were used to analyze the common targets of DR,PIC,and microglia.Human retinal vascular endothelial cells(HRVECs)were cultured in vitro and randomly divided into the NG-HRVECs group,HG-HRVECs group,and HG+PIC-HRVECs group.Cell viability was assessed by the CCK-8 assay,apoptosis was detected by the TUNEL assay,and the concentrations of tumor necrosis factor-α(TNF-α)and interleukin-6(IL-6)were measured using ELISA kits.BV-2 cells were cultured in vitro and randomly divided into the NG-BV-2 group,HG-BV-2 group,HG+PIC-BV-2 group,HG+Si-NC-BV-2 group,HG+Si-CXCR4-BV-2 group,and HG+Ibrutinib-BV-2 group.The levels of arginase-1(Arg-1)and inducible ni-tric oxide synthase(iNOS)were measured using ELISA kits.Furthermore,conditioned medium(CM)from BV-2 cells of each group was collected to treat HRVECs,after which the viability,apoptosis rate,and TNF-α and IL-6 concentrations of the HRVECs were measured.A DR rat model was established and intervened with PIC to investigate the ameliorative effects of PIC on retinal pathology.Results Bioinformatics analysis identified CXCR4 as the key target of this study.Compared with the NG-HRVECs group,the apoptosis rate and the concentrations of TNF-α and IL-6 were increased in the HG-HRVECs group.Compared with the HG-HRVECs group,the HG+PIC-HRVECs group showed a decreased apoptosis rate and reduced concentrations of TNF-α and IL-6(all P＜0.05).Compared with the NG-BV-2 group,the HG-BV-2 group ex-hibited decreased Arg-1 levels and increased iNOS levels.Compared with the HG-BV-2 group,the HG+PIC-BV-2,HG+Si-CXCR4-BV-2,and HG+Ibrutinib-BV-2 groups all showed increased Arg-1 levels and decreased iNOS levels(all P＜0.05).Compared with the NG-BV-2-CM group,the HG-BV-2-CM group led to decreased viability,increased apoptosis rate,and in-creased concentrations of TNF-α and IL-6 in HRVECs.In contrast,the HG+PIC-BV-2-CM,HG+Si-CXCR4-BV-2-CM,and HG+Ibrutinib-BV-2-CM groups reversed the effects induced by HG-BV-2-CM on HRVECs(all P＜0.05).Animal experiment results showed that compared with DR model rats,rats treated with different doses of PIC exhibited significantly ameliora-ted retinal histopathological damage,and the protein expressions of Arg-1,iNOS,CXCR4,and p-BTK were reversed.Con-clusion PIC ameliorates DR progression mediated by microglial polarization by inhibiting the CXCR4/BTK pathway.

Objective:To investigate the impact of postoperative complications on adverse outcomes following curative-intent resection for gallbladder cancer (GBC).Methods:The multi-center real-world study was conducted. The clinicopathological data of 629 patients with GBC, who were admitted to 14 medical centers including The First Affiliated Hospital of Army Medical University from the national multicenter database of Biliary Surgery Group of Elite Group of Chinese Journal of Digestive Surgery, from April 2020 to April 2024 were collected. There were 225 males and 404 females, aged (64±10)years. Patients underwent open curative-intent resection for GBC. Observation indicators: (1)surgery, postoperative complica-tions and adverse outcomes; (2) analysis of risk factors affecting postoperative adverse outcomes in patients and population attributable fraction (PAF). Missing data in predictor variables were addressed using multiple imputation with chained equations, while cases with missing outcome variables were addressed using the "multiple imputation then deletion (MID)" strategy. The severity of multicollinearity among independent variables was assessed using the variance inflation factor (VIF) test. Multivariable possion regression models with log link and robust error variance were construc-ted incorporating restricted cubic splines (3 knots) to address nonlinear relationships in continuous variables, calculating adjusted relative risk ( RR) with corresponding 95% confidence interval ( CI). Adjusted PAF was calculated for each imputed dataset using the AF package of R software, with subsequent pooling performed according to Rubin's rules. Results:(1) Surgery, postoperative complications and adverse outcomes. All 629 patients underwent curative-intent resection for GBC, of which 143 cases had postoperative complications, including 68 cases of intra-abdominal ascites, 39 cases of pulmonary infection, 21 cases of bile leakage, 12 cases of intra-abdominal hemorrhage, 11 cases of liver failure, 10 cases of pan-creatic fistula, 10 cases of wound infection, 10 cases of gastroparesis, 7 cases of cholangitis, 7 cases of sepsis. The same patient could have more than one kind of complication. Of 629 patients, there were 19 cases of postoperative 90-day death and 11 cases of missing data, 42 cases with post-operative 90-day reoperation and 7 cases with missing data, 44 cases with postoperative 90-day readmission and 3 cases with missing data, 155 cases with prolonged postoperative hospital stay and 3 cases with missing data. (2) Analysis of risk factors affecting the postoperative adverse outcomes in patients and PAF. Results of multivariate analysis showed that pulmonary infection and liver failure were independent risk factors for postoperative 90-day mortality ( RR=3.74, 12.15, 95% CI as 1.18-11.83, 1.98-74.48, P<0.05). Pulmonary infection demons-trated the highest PAF as 4.61% (95% CI as 3.94%-5.28%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, and intra-abdominal hemorrhage were independent risk factors for post-operative 90-day reoperation ( RR=4.80, 3.62, 3.46, 4.99, 95% CI as 2.49-9.26, 1.42-9.21, 1.34-8.92, 1.55-16.06, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 8.65% (95% CI as 8.22%-9.08%, P<0.05). Intra-abdominal ascites, bile leakage, and liver failure were independent risk factors for postoperative 90-day readmission ( RR=6.20, 3.33, 14.33, 95% CI as 3.21-11.95, 1.33-8.35, 3.72-55.28, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 9.11% (95% CI as 8.85%-9.37%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, liver failure, and wound infection were independent risk factors for prolonged postoperative hospital stay ( RR=2.29, 2.21, 2.26, 2.14, 3.35, 95% CI as 1.63-3.23, 1.41-3.46, 1.32-3.86, 1.11-4.13, 1.70-6.60, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 6.03% (95% CI as 5.71%-6.35%, P<0.05). Conclusion:Pulmonary infection is the most significant risk factor for postoperative 90-day mortality after curative-intent resection for GBC, while intra-abdominal ascites is the most significant risk factor for postoperative 90-day reoperation, postoperative 90-day readmission, and prolonged postoperative hospital stay.