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: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.

ObjectiveTo investigate the predictive factors for HBsAg clearance in chronic hepatitis B （CHB） patients with a low level of hepatitis B surface antigen （HBsAg） treated with pegylated interferon α-2b （PEG-IFN-α-2b）， to establish a combined predictive model and a nomogram based on multiple factors， and to provide a reference for formulating individualized treatment regimens and predicting treatment outcome in clinical practice. MethodsA retrospective analysis was performed for 167 CHB patients with HBsAg <1 500 IU/mL who attended The Third People’s Hospital of Kunming from January 2022 to January 2024 and were treated with PEG-IFN-α-2b. According to whether clinical cure was achieved， the patients were divided into HBsAg clearance group and HBsAg non-clearance group. Related data were collected， including general information and serological/biochemical/virological indicators at different time points during treatment. The independent samples t-test was used for comparison of normally distributed continuous data， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data； the chi-square test was used for comparison of categorical data. The multivariate logistic regression analysis was used to identify independent influencing factors. The receiver operating characteristic （ROC） curve was used to assess the value of indicators used alone or in combination in predicting clinical cure， and calibration curves were plotted to assess the risk prediction model. ResultsThe univariate analysis showed that there were significant differences between the two groups in age （t=-6.839， P<0.05）， history of nucleos（t）ide analogue treatment for over 1 year （χ²=59.339， P<0.05）， genotype （χ²=4.610， P<0.05）， nonalcoholic fatty liver disease （χ²=5.319， P<0.05）， hepatitis B virus DNA status before treatment （χ²=60.861， P <0.05）， compensated liver cirrhosis （χ²=10.960， P<0.05）， HBeAg status before treatment （χ²=19.060， P<0.05）， a history of interferon treatment （χ²=8.162， P<0.05）， presence of interferon antibodies after treatment （χ²=12.858， P<0.05）， HBsAg level before treatment （Z=-7.412， P<0.05）， alanine aminotransaminase （ALT） level at baseline （Z=-6.117， P<0.05）， ALT level at 12 weeks of treatment （Z=-7.171， P<0.05）， platelet count （PLT） at 24 weeks of treatment （Z=-3.622， P<0.05）， and thyroid stimulating hormone （TSH） level at 24 weeks of treatment （Z=-2.830， P<0.05）. The multivariate logistic regression analysis showed that age （odds ratio ［OR］=1.230， P=0.007）， history of nucleos（t）ide analogue treatment for over 1 year （OR=0.008， P=0.011）， HBeAg status before treatment （OR=0.003， P=0.012）， HBsAg level before treatment （OR=1.005， P=0.014）， ALT level at baseline （OR=0.949， P=0.014）， ALT level at 12 weeks of treatment （OR=0.969， P=0.016）， PLT at 24 weeks of treatment （OR=0.969， P=0.022）， and TSH level at 24 weeks of treatment （OR=3.608， P=0.045） were independent influencing factors for HBsAg clearance at 48 weeks of treatment in CHB patients with HBsAg <1 500 IU/mL. The Hosmer-Lemeshow goodness-of-fit test yielded χ²=1.398， P=0.994， indicating that the model had good fitting. The Bootstrap method was used to perform internal validation of the nomogram model， and there was a good degree of fitting between the calibration curve and the ideal curve， with a mean absolute error of 0.029. The ROC curve analysis showed that the combination of predictive factors had an area under the ROC curve of 0.982 （95% confidence interval： 0.961 — 0.999）， with a sensitivity of 94.10% and a specificity of 93.10%， suggesting that the nomogram model had a good discriminatory ability. For the CHB patients with HBsAg <1 500 IU/mL and different features， further analysis of HBsAg clearance rate at 48 weeks of treatment showed an HBsAg clearance rate of 69.60% for those with HBsAg ≤67.65 IU/mL before treatment， 58.30% for those with a baseline ALT level of ≥62.50 U/L， 68.30% for those with an ALT level of ≥92.50 U/L at 12 weeks of treatment， 42.40% for those with PLT ≥104×10⁹/L at 24 weeks of treatment， and 48.30% for those with a TSH level of ≤1.38 μIU/mL at 24 weeks of treatment， with significant differences between the two groups （all P<0.001）. ConclusionAge， history of nucleos（t）ide analogue treatment for over 1 year， HBeAg status before treatment， HBsAg level before treatment， baseline ALT level， ALT level at 12 weeks of treatment， PLT level at 24 weeks， and TSH level at 24 weeks of treatment are independent predictive factors. The combined prediction nomogram model constructed in this study has a relatively high value in predicting clinical cure at 48 weeks of PEG-IFN-α-2b treatment in CHB patients with HBsAg<1 500 IU/mL， thereby providing a reference for selecting suitable treatment population and predicting clinical cure.

Objective:To observe the effects of Maxing Loushi decoction on the inflammatory response and inflammatory indicators in mice with chronic obstructive pulmonary disease (COPD) models.Methods:Thirty-six BALB/C mice were randomly divided into 4 groups by random number table method: 10 mice in the COPD model group (referred to as the model group), 10 mice in the Maxing Loushi decoction group (referred to as the traditional Chinese medicine group), 10 mice in the programmed death receptor-1 (PD-1) inhibitor group (referred to as the control group), and 6 mice in the normal group. The COPD models of mice in the model group, the traditional Chinese medicine group and the control group were prepared by cigarette smoking combined with lipopolysaccharide (LPS) induction method. During the modeling process, the model group and the traditional Chinese medicine group were respectively given normal saline and Maxing Loushi decoction by gavage. The control group was given intraperitoneal injection of PD-1 inhibitor, while the normal group was given intragastric administration of normal saline. Pathological changes of lung tissues in each group of mice were detected by HE staining. The levels of inflammatory factors [monocyte chemotactic protein-1 (MCP-1), macrophage inflammatory protein-1α (MIP-1α), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)] in the plasma and alveolar lavage fluid (BALF) of mice in each group were determined by enzyme-linked immunosorbent assay (ELISA). The effects of Maxing Loushi decoction intervention on inflammatory responses and inflammatory factors were evaluated.Results:The lung tissue structure in the normal group was basically normal. There was no thickening of the alveolar walls, no infiltration of neutrophils in the tissues, and no obvious inflammatory infiltration. In the model group, the lung tissue structure was slightly abnormal. A small amount of alveolar atrophy could be observed, the alveolar walls were slightly thickened, and inflammatory infiltration could be seen. In the traditional Chinese medicine group, the lung tissue structure was slightly abnormal. A small amount of alveolar atrophy and collapse could be observed, the alveolar walls were not thickened, and individual neutrophil infiltration could be seen in the tissue. In the control group, the lung tissue structure was slightly abnormal, some alveoli atrophied, and a small amount of neutrophil infiltration could be seen in the tissue. The levels of MCP-1 and MIP-1α in plasma and lavage fluid of the model group were significantly higher than those of the normal group (all P<0.05), while the levels of MCP-1 and MIP-1α in plasma and lavage fluid of the traditional Chinese medicine group and the control group were significantly lower than those of the model group (all P<0.05). Moreover, the levels of plasma MCP-1 and MIP-1α in the traditional Chinese medicine group were significantly lower than those in the control group (all P<0.05), while there was no statistically significant difference in the levels of MCP-1 and MIP-1α in alveolar lavage fluid between the traditional Chinese medicine group and the control group (all P>0.05). The levels of IL-6 and TNF-α in plasma and alveolar lavage fluid of the model group were significantly higher than those of the normal group (all P<0.05), while the levels of IL-6 and TNF-α in plasma and alveolar lavage fluid of the traditional Chinese medicine group and the control group were significantly lower than those of the model group (all P<0.05). Moreover, the levels of IL-6 and TNF-α in plasma and alveolar lavage fluid of the traditional Chinese medicine group were significantly lower than those of the control group (all P<0.05). Conclusions:The intervention of Maxing Loushi decoction has a significant improvement effect on the inflammatory response of COPD model mice. Inflammatory factors such as MCP-1, MIP-1α, IL-6, and TNF-α can be used as indicators to determine the degree of COPD inflammation.

Objective To investigate the predictive factors for the occurrence of HBsAg clearance in patients with HBeAg-negative chronic hepatitis B(CHB)receiving peginterferon alfa-2b(PEG-IFN-α-2b)treatment,analyze the effects of various indicators on the HBsAg clearance rate under different characteristics,and construct and evaluate a combined predictive model.Methods We included 125 patients with HBeAg-negative CHB at Kunming Third People's Hospital from May 2021 to May 2023.After treatment with PEG-IFN-α-2b combined with nucleoside analogues for a course of 48 weeks,they were divided into HBsAg clearance group and HBsAg non-clearance group.Their general information and serological,biochemical,and virological indicators at different time points during treatment were recorded.Continuous data in normal distribution were compared using the t test.Continuous data in non-normal distribution were compared using the Mann-Whitney U test,and comparisons across different time points were performed using the multiple paired-sample Friedman test.Categorical data were compared using the χ2 test.A Logistic regression analysis was used to select variables to establish a combined multi-parameter predictive model.Receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic value of individual indicators and the combined predictive model for HBsAg clearance.Results Before treatment,there were significant differences in baseline HBsAg level(Z=-3.997,P<0.05)and treatment history(χ2=8.221,P<0.05)between the two groups.During treatment,gradually decreasing trends were observed in white blood cell count(χ2=104.944),neutrophil count(χ2=132.036),platelet count(χ2=162.881),and thyroid-stimulating hormone level(TSH,χ2=83.304,all P<0.05),while alanine aminotransferase(ALT,χ2=157.618)and alpha fetoprotein(χ2=159.472)showed gradually increasing trends(both P<0.05).At 48 weeks of treatment,treatment history(odds ratio[OR]=0.232,95%confidence interval[CI]:0.071-0.753),baseline HBsAg level(OR=13.423,95%CI:3.276-54.997),the extent of decrease in HBsAg from baseline after 12 weeks of treatment(OR=0.143,95%CI:0.040-0.515),the maximum ALT level during treatment(OR=0.986,95%CI:0.980-0.993),and the minimum TSH level during treatment(OR=3.281,95%CI:1.413-7.619)were independent factors affecting HBsAg clearance(all P<0.05).A combined predictive model for HBsAg clearance was built:Y=-1.603-1.462×treatment history+2.597×baseline HBsAg value-1.944×the extent of HBsAg reduction from baseline after 12 weeks of treatment-0.014×the maximum ALT value during treatment+1.188×the minimum TSH value during treatment.The diagnostic value of the individual indicators for HBsAg clearance from high to low was as following:the maximum ALT value during treatment(AUC=0.824),baseline HBsAg value(AUC=0.727),the minimum TSH value during treatment(AUC=0.707),the extent of HBsAg reduction from baseline after 12 weeks of treatment(AUC=0.641),and treatment history(AUC=0.636).The combined model showed better predictive performance than the individual indicators,with the AUC being 0.921(all P<0.05).Conclusion The combined model,constructed with baseline HBsAg value,the extent of HBsAg reduction from baseline after 12 weeks of treatment,the maximum ALT value during treatment,and the minimum TSH value during treatment,has high predictive value for the occurrence of HBsAg clearance in patients with HBeAg-negative CHB after 48 weeks of treatment with PEG-IFN-α-2b,which can provide a reference for identifying suitable patients for treatment and predicting clinical outcome.

Objective To investigate the independent risk factors for portal vein thrombosis(PVT)in patients with viral hepatitis-related decompensated cirrhosis,and to establish and validate a nomogram risk prediction model.Methods A retrospective analysis was performed for the clinical data of 1 116 patients with decompensated HBV/HCV cirrhosis who attended The Third People's Hospital of Kunming for the first time from January 2022 to December 2023,and according to the presence or absence of PVT,they were divided into PVT group and control group.The independent samples t-test or the Mann-Whitney U test was used for comparison of continuous data between groups,and the chi-square test was used for comparison of categorical data between groups.Univariate analysis and least absolute shrinkage and selection operator(LASSO)regression analysis were used to identify variables,and a binary logistic regression analysis was used to obtain independent influencing factors and establish a predictive model,which was visualized using a nomogram.The model was validated based on the receiver operating characteristic(ROC)curve,the area under the ROC curve(AUC),the Hosmer-Lemeshow test,Bootstrap sampling(1 000 iterations),the calibration curve,the decision curve analysis(DCA),and the clinical impact curve(CIC).Results There were 178 patients in the PVT group and 938 patients in the control group,and the prevalence rate of PVT was 15.9%(178/1 116).Male patients accounted for 68.5%(764/1 116),and the patients with drinking,Child-Pugh class B liver function,and ascites accounted for 51.0%(569/1 116),78.8%(879/1 116),and 67.1%(749/1 116),respectively.Compared with the control group,the PVT group had significantly higher age(Z=-2.362,P<0.05),prothrombin time(Z=-2.403,P<0.05),international normalized ratio(Z=-2.470,P<0.05),free thyroxine(Z=-5.910,P<0.05),D-dimer(Z=-5.764,P<0.05),interleukin-6(Z=-6.581,P<0.05),interleukin-10(IL-10)(Z=-3.915,P<0.05),interleukin-8(Z=-3.705,P<0.05),diameter of the portal vein(Z=-9.690,P<0.05),and spleen thickness(Z=-7.183,P<0.05),as well as significantly lower levels of white blood cell count(Z=-2.115,P<0.05),platelet count(Z=-3.026,P<0.05),fibrinogen(Z=-2.169,P<0.05),alanine aminotransferase(Z=-3.151,P<0.05),prealbumin(Z=-3.509,P<0.05),cholinesterase(Z=-3.415,P<0.05),alpha-fetoprotein(Z=-3.513,P<0.05),triglycerides(Z=-2.679,P<0.05),CD3 cell count(Z=-6.059,P<0.05),CD4 cell count(Z=-7.257,P<0.05),CD8 cell count(Z=-2.340,P<0.05),CD4+/CD8+cell ratio(Z=-4.479,P<0.05),triiodothyronine(Z=-3.338,P<0.05),free triiodothyronine(FT3)(Z=-9.560,P<0.05),and portal blood flow velocity(Z=-4.568,P<0.05).The multivariate logistic regression analysis was performed for the variables with statistical significance identified by the LASSO regression analysis,and the results showed that age(odds ratio[OR]=1.046,95%confidence interval[CI]:1.026-1.066),CD4+/CD8+cell ratio(OR=0.568,95%CI:0.410-0.787),FT3(OR=0.956,95%CI:0.944-0.968),IL-10(OR=1.021,95%CI:1.001-1.042),diameter of the portal vein(OR=1.446,95%CI:1.329-1.574),and spleen thickness(OR=1.035,95%CI:1.014-1.055)were independent influencing factors.A model was established as Logit(P)=-8.784+0.045×age-0.566×CD4+/CD8+-0.046×FT3+0.021×IL-10+0.369×diameter of the portal vein+0.034×spleen thickness,and a nomogram model was established and validated based on this model,with an AUC of 0.859(95%CI:0.833-0.887).The Hosmer-Lemeshow test showed that the model had a high goodness of fit(χ2=11.349,P=0.183).Bootstrap internal validation showed a mean absolute error of 0.006 and a C-index of 0.855.The decision curve analysis showed that the model had a high net clinical benefit within a wide range of thresholds.Conclusion Age,CD4+/CD8+ratio,FT3,IL-10,diameter of the portal vein,and spleen thickness may be independent influencing factors for PVT in patients with decompensated HBV/HCV cirrhosis.The predictive model established based on these six variables can help to predict the risk of PVT in patients with hepatitis-related decompensated cirrhosis in the early stage in clinical practice.

Objective To investigate the influencing factors for recompensation in patients with decompensated hepatitis B cirrhosis,and to establish a predictive model.Methods A total of 517 patients who attended The Third People's Hospital of Kunming and were diagnosed with decompensated hepatitis B cirrhosis from January 1,2016 to December 31,2022 were enrolled.The clinical data of the patients were reviewed,and the 207 patients with no portal hypertension-related complications within at least 1 year were enrolled as recompensation group,while the 310 patients without recompensation were enrolled as persistent decompensation group.Related clinical data were collected,and the univariate and multivariate Cox regression analyses were performed for the factors that might affect the occurrence of recompensation.The independent-samples t test was used for comparison of normally distributed continuous data between two groups,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups;the chi-square test or the Fisher's exact test was used for comparison of categorical data between two groups.The"rms"package was used to establish a nomogram;the receiver operating characteristic(ROC)curve was plotted,and the area under the ROC curve(AUC)was calculated;the Hosmer-Lemeshow test was used to evaluate the degree of fitting of the model;the"Calibration Curves"package was used to plot the calibration curve for model assessment.Results Among the patients with decompensated hepatitis B cirrhosis,207(40.03%)had recompensation.The univariate Cox regression analysis showed that there were significant differences between the recompensation group and the persistent decompensation group in TIPS history,genotyping,portal vein thrombosis,complicated infection,Child-Pugh class,age,hemoglobin(Hb),platelet count,total protein,albumin(Alb),alanine aminotransferase,triglyceride,cholesterol,creatinine,Na,interleukin-6,high-sensitivity C-reactive protein,portal vein width,and portal vein velocity(all P<0.05).The multivariate regression analysis showed that TIPS history(hazard ratio[HR]=2.491,95%confidence interval[CI]:1.325-4.681,P=0.005),portal vein thrombosis(HR=0.345,95%CI:0.152-0.783,P=0.001),Hb(HR=1.007,95%CI:1.000-1.013,P=0.028),Alb(HR=1.048,95%CI:1.017-1.080,P=0.002),and portal vein width(HR=0.899,95%CI:0.835-0.967,P=0.004)were independent influencing factors for recompensation in patients with decompensated hepatitis B cirrhosis.A nomogram model was established based on the above five influencing factors,and the Hosmers-Lemeshow test showed that this model had a good degree of fitting(χ2=3.202,P=0.921).The nomogram model had an AUC of 0.728,a sensitivity of 50.3%,and a specificity of 85.0%,and the calibration curve showed good consistency between the actual value of this model in predicting the occurrence of recompensation and the predicted value in patients with decompensated hepatitis B cirrhosis.Conclusion Patients with decompensated hepatitis B cirrhosis who have a history of TIPS and high levels of Alb and Hb are more likely to have recompensation,and it is relatively difficult for patients with portal vein thrombosis and an increase in portal vein width to achieve recompensation.

Objective To investigate the influencing factors for recompensation in patients with decompensated hepatitis B cirrhosis,and to establish a predictive model.Methods A total of 517 patients who attended The Third People's Hospital of Kunming and were diagnosed with decompensated hepatitis B cirrhosis from January 1,2016 to December 31,2022 were enrolled.The clinical data of the patients were reviewed,and the 207 patients with no portal hypertension-related complications within at least 1 year were enrolled as recompensation group,while the 310 patients without recompensation were enrolled as persistent decompensation group.Related clinical data were collected,and the univariate and multivariate Cox regression analyses were performed for the factors that might affect the occurrence of recompensation.The independent-samples t test was used for comparison of normally distributed continuous data between two groups,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups;the chi-square test or the Fisher's exact test was used for comparison of categorical data between two groups.The"rms"package was used to establish a nomogram;the receiver operating characteristic(ROC)curve was plotted,and the area under the ROC curve(AUC)was calculated;the Hosmer-Lemeshow test was used to evaluate the degree of fitting of the model;the"Calibration Curves"package was used to plot the calibration curve for model assessment.Results Among the patients with decompensated hepatitis B cirrhosis,207(40.03%)had recompensation.The univariate Cox regression analysis showed that there were significant differences between the recompensation group and the persistent decompensation group in TIPS history,genotyping,portal vein thrombosis,complicated infection,Child-Pugh class,age,hemoglobin(Hb),platelet count,total protein,albumin(Alb),alanine aminotransferase,triglyceride,cholesterol,creatinine,Na,interleukin-6,high-sensitivity C-reactive protein,portal vein width,and portal vein velocity(all P<0.05).The multivariate regression analysis showed that TIPS history(hazard ratio[HR]=2.491,95%confidence interval[CI]:1.325-4.681,P=0.005),portal vein thrombosis(HR=0.345,95%CI:0.152-0.783,P=0.001),Hb(HR=1.007,95%CI:1.000-1.013,P=0.028),Alb(HR=1.048,95%CI:1.017-1.080,P=0.002),and portal vein width(HR=0.899,95%CI:0.835-0.967,P=0.004)were independent influencing factors for recompensation in patients with decompensated hepatitis B cirrhosis.A nomogram model was established based on the above five influencing factors,and the Hosmers-Lemeshow test showed that this model had a good degree of fitting(χ2=3.202,P=0.921).The nomogram model had an AUC of 0.728,a sensitivity of 50.3%,and a specificity of 85.0%,and the calibration curve showed good consistency between the actual value of this model in predicting the occurrence of recompensation and the predicted value in patients with decompensated hepatitis B cirrhosis.Conclusion Patients with decompensated hepatitis B cirrhosis who have a history of TIPS and high levels of Alb and Hb are more likely to have recompensation,and it is relatively difficult for patients with portal vein thrombosis and an increase in portal vein width to achieve recompensation.

Objective To investigate the predictive factors for the occurrence of HBsAg clearance in patients with HBeAg-negative chronic hepatitis B(CHB)receiving peginterferon alfa-2b(PEG-IFN-α-2b)treatment,analyze the effects of various indicators on the HBsAg clearance rate under different characteristics,and construct and evaluate a combined predictive model.Methods We included 125 patients with HBeAg-negative CHB at Kunming Third People's Hospital from May 2021 to May 2023.After treatment with PEG-IFN-α-2b combined with nucleoside analogues for a course of 48 weeks,they were divided into HBsAg clearance group and HBsAg non-clearance group.Their general information and serological,biochemical,and virological indicators at different time points during treatment were recorded.Continuous data in normal distribution were compared using the t test.Continuous data in non-normal distribution were compared using the Mann-Whitney U test,and comparisons across different time points were performed using the multiple paired-sample Friedman test.Categorical data were compared using the χ2 test.A Logistic regression analysis was used to select variables to establish a combined multi-parameter predictive model.Receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic value of individual indicators and the combined predictive model for HBsAg clearance.Results Before treatment,there were significant differences in baseline HBsAg level(Z=-3.997,P<0.05)and treatment history(χ2=8.221,P<0.05)between the two groups.During treatment,gradually decreasing trends were observed in white blood cell count(χ2=104.944),neutrophil count(χ2=132.036),platelet count(χ2=162.881),and thyroid-stimulating hormone level(TSH,χ2=83.304,all P<0.05),while alanine aminotransferase(ALT,χ2=157.618)and alpha fetoprotein(χ2=159.472)showed gradually increasing trends(both P<0.05).At 48 weeks of treatment,treatment history(odds ratio[OR]=0.232,95%confidence interval[CI]:0.071-0.753),baseline HBsAg level(OR=13.423,95%CI:3.276-54.997),the extent of decrease in HBsAg from baseline after 12 weeks of treatment(OR=0.143,95%CI:0.040-0.515),the maximum ALT level during treatment(OR=0.986,95%CI:0.980-0.993),and the minimum TSH level during treatment(OR=3.281,95%CI:1.413-7.619)were independent factors affecting HBsAg clearance(all P<0.05).A combined predictive model for HBsAg clearance was built:Y=-1.603-1.462×treatment history+2.597×baseline HBsAg value-1.944×the extent of HBsAg reduction from baseline after 12 weeks of treatment-0.014×the maximum ALT value during treatment+1.188×the minimum TSH value during treatment.The diagnostic value of the individual indicators for HBsAg clearance from high to low was as following:the maximum ALT value during treatment(AUC=0.824),baseline HBsAg value(AUC=0.727),the minimum TSH value during treatment(AUC=0.707),the extent of HBsAg reduction from baseline after 12 weeks of treatment(AUC=0.641),and treatment history(AUC=0.636).The combined model showed better predictive performance than the individual indicators,with the AUC being 0.921(all P<0.05).Conclusion The combined model,constructed with baseline HBsAg value,the extent of HBsAg reduction from baseline after 12 weeks of treatment,the maximum ALT value during treatment,and the minimum TSH value during treatment,has high predictive value for the occurrence of HBsAg clearance in patients with HBeAg-negative CHB after 48 weeks of treatment with PEG-IFN-α-2b,which can provide a reference for identifying suitable patients for treatment and predicting clinical outcome.

Objective To investigate the independent risk factors for portal vein thrombosis(PVT)in patients with viral hepatitis-related decompensated cirrhosis,and to establish and validate a nomogram risk prediction model.Methods A retrospective analysis was performed for the clinical data of 1 116 patients with decompensated HBV/HCV cirrhosis who attended The Third People's Hospital of Kunming for the first time from January 2022 to December 2023,and according to the presence or absence of PVT,they were divided into PVT group and control group.The independent samples t-test or the Mann-Whitney U test was used for comparison of continuous data between groups,and the chi-square test was used for comparison of categorical data between groups.Univariate analysis and least absolute shrinkage and selection operator(LASSO)regression analysis were used to identify variables,and a binary logistic regression analysis was used to obtain independent influencing factors and establish a predictive model,which was visualized using a nomogram.The model was validated based on the receiver operating characteristic(ROC)curve,the area under the ROC curve(AUC),the Hosmer-Lemeshow test,Bootstrap sampling(1 000 iterations),the calibration curve,the decision curve analysis(DCA),and the clinical impact curve(CIC).Results There were 178 patients in the PVT group and 938 patients in the control group,and the prevalence rate of PVT was 15.9%(178/1 116).Male patients accounted for 68.5%(764/1 116),and the patients with drinking,Child-Pugh class B liver function,and ascites accounted for 51.0%(569/1 116),78.8%(879/1 116),and 67.1%(749/1 116),respectively.Compared with the control group,the PVT group had significantly higher age(Z=-2.362,P<0.05),prothrombin time(Z=-2.403,P<0.05),international normalized ratio(Z=-2.470,P<0.05),free thyroxine(Z=-5.910,P<0.05),D-dimer(Z=-5.764,P<0.05),interleukin-6(Z=-6.581,P<0.05),interleukin-10(IL-10)(Z=-3.915,P<0.05),interleukin-8(Z=-3.705,P<0.05),diameter of the portal vein(Z=-9.690,P<0.05),and spleen thickness(Z=-7.183,P<0.05),as well as significantly lower levels of white blood cell count(Z=-2.115,P<0.05),platelet count(Z=-3.026,P<0.05),fibrinogen(Z=-2.169,P<0.05),alanine aminotransferase(Z=-3.151,P<0.05),prealbumin(Z=-3.509,P<0.05),cholinesterase(Z=-3.415,P<0.05),alpha-fetoprotein(Z=-3.513,P<0.05),triglycerides(Z=-2.679,P<0.05),CD3 cell count(Z=-6.059,P<0.05),CD4 cell count(Z=-7.257,P<0.05),CD8 cell count(Z=-2.340,P<0.05),CD4+/CD8+cell ratio(Z=-4.479,P<0.05),triiodothyronine(Z=-3.338,P<0.05),free triiodothyronine(FT3)(Z=-9.560,P<0.05),and portal blood flow velocity(Z=-4.568,P<0.05).The multivariate logistic regression analysis was performed for the variables with statistical significance identified by the LASSO regression analysis,and the results showed that age(odds ratio[OR]=1.046,95%confidence interval[CI]:1.026-1.066),CD4+/CD8+cell ratio(OR=0.568,95%CI:0.410-0.787),FT3(OR=0.956,95%CI:0.944-0.968),IL-10(OR=1.021,95%CI:1.001-1.042),diameter of the portal vein(OR=1.446,95%CI:1.329-1.574),and spleen thickness(OR=1.035,95%CI:1.014-1.055)were independent influencing factors.A model was established as Logit(P)=-8.784+0.045×age-0.566×CD4+/CD8+-0.046×FT3+0.021×IL-10+0.369×diameter of the portal vein+0.034×spleen thickness,and a nomogram model was established and validated based on this model,with an AUC of 0.859(95%CI:0.833-0.887).The Hosmer-Lemeshow test showed that the model had a high goodness of fit(χ2=11.349,P=0.183).Bootstrap internal validation showed a mean absolute error of 0.006 and a C-index of 0.855.The decision curve analysis showed that the model had a high net clinical benefit within a wide range of thresholds.Conclusion Age,CD4+/CD8+ratio,FT3,IL-10,diameter of the portal vein,and spleen thickness may be independent influencing factors for PVT in patients with decompensated HBV/HCV cirrhosis.The predictive model established based on these six variables can help to predict the risk of PVT in patients with hepatitis-related decompensated cirrhosis in the early stage in clinical practice.