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 efficacy and safety of the direct-acting antiviral agents coblopasvir hydrochloride/sofosbuvir （CLP/SOF） regimen used alone or in combination with ribavirin （RBV） in the treatment of patients with genotype 3 hepatitis C virus （HCV） infection in terms of virologic response rate， liver function recovery， improvement in liver stiffness measurement （LSM）， and adverse drug reactions， and to provide a reference for clinical medication. MethodsA total of 98 patients with genotype 3 HCV infection who attended The Third People’s Hospital of Kunming from January 2022 to December 2023 were enrolled， and according to the treatment method， the patients were divided into CLP/SOF+RBV treatment group with 55 patients and CLP/SOF treatment group with 43 patients. The patients were observed in terms of rapid virologic response at week 4 （RVR4）， sustained virologic response （SVR）， previous treatment experience， underlying diseases， laboratory and imaging indicators， and adverse reactions during treatment. The course of treatment was 12 weeks， and the patients were followed up for 12 weeks after drug withdrawal. 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 Friedman test was used for comparison within each group at different time points， and the Bonferroni method was used for further comparison and correction of P value； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The univariate and multivariate Logistic regression analyses were used to investigate the influencing factors for SVR12. ResultsBefore treatment， there were significant differences between the CLP/SOF+RBV treatment group and the CLP/SOF treatment group in terms of LSM， total bilirubin （TBil）， gamma-glutamyl transpeptidase （GGT）， HCV genotype， and the presence or absence of liver cirrhosis and compensation （all P<0.05）. The 98 patients with genotype 3 HCV infection had an RVR4 rate of 81.6% and an SVR12 rate of 93.9%. The patients with genotype 3a HCV infection had an RVR4 rate of 84.44% and an SVR12 rate of 97.78%， while the patients with genotype 3b HCV infection had an RVR4 rate of 79.25% and an SVR12 rate of 90.57%. There were significant differences in RVR4 and SVR12 rates between the patients without hepatocellular carcinoma and those with hepatocellular carcinoma， there was a significant difference in RVR4 rate between the patients without HIV infection and those with HIV infection， and there was a significant difference in SVR12 rate between the previously untreated patients and the treatment-experienced patients （all P<0.05）. The univariate Logistic regression analysis showed that treatment history， hypertension， hepatocellular carcinoma， ascites， albumin （Alb）， and platelet count were influencing factors for SVR12 （all P<0.05）， and the multivariate Logistic regression analysis showed that hepatocellular carcinoma （odds ratio=0.034， 95% confidence interval： 0.002‍ ‍—‍‍ 0.666， P=0.026） was an independent influencing factor for SVR12. After treatment with CLP/SOF combined with RBV or CLP/SOF alone， the patients with genotype 3 HCV infection showed gradual reductions in the liver function parameters of TBil， GGT， and alanine aminotransferase （all P<0.05） and a gradual increase in the level of Alb （P<0.05）. As for renal function， there were no significant changes in blood urea nitrogen and creatinine after treatment （P>0.05）. For the patients with or without liver cirrhosis， there was a significant reduction in LSM from baseline after treatment for 12 weeks （P<0.05）. Among the 98 patients with genotype 3 HCV infection， 9 tested positive for HCV-RNA at 12 weeks after treatment， 2 showed no response during treatment， 4 showed virologic breakthrough， and 3 experienced recurrence. The overall incidence rate of adverse events during treatment was 17.35% for all patients. ConclusionCLP/SOF alone or in combination with RBV has a relatively high SVR rate in the treatment of genotype 3 HCV infection， with good tolerability and safety in patients during treatment， and therefore， it holds promise for clinical application.

The cellular and molecular components of the tumor immune microenvironment (TIME) and their information exchange processes significantly influence the trends of anti-tumor immunity. In recent years, numerous studies have begun to evaluate TIME in the context of previous cancer treatment strategies. This review will systematically summarize the compositional characteristics of TIME and, based on this foundation, explore the impact of current cancer therapies on the remodeling of TIME, aiming to provide new insights for the development of innovative immune combination therapies that can convert TIME into an anti-tumor profile.
Tumor Microenvironment/immunology* ; Humans ; Neoplasms/therapy* ; Immunotherapy/methods* ; Animals

ObjectiveTo investigate the value of noninvasive imaging detection （FibroScan）， two serological models of gamma-glutamyl transpeptidase-to-platelet ratio （GPR） score and S index， and two inflammatory factors of interleukin-6 （IL-6） and tumor necrosis factor-α （TNF-α） in predicting liver fibrosis in patients with HBeAg-positive chronic hepatitis B （CHB）， as well as the consistency of liver biopsy in pathological staging， and to provide early warning for early intervention of CHB. MethodsA retrospective analysis was performed for 131 HBeAg-positive CHB patients who underwent liver biopsy in The Third People’s Hospital of Kunming from January 2019 to December 2023. The results of liver biopsy were collected from all patients， and related examinations were performed before liver biopsy， including total bilirubin， alanine aminotransferase， platelet count， gamma-glutamyl transpeptidase， albumin， IL-6， TNF-α， liver stiffness measurement （LSM）， and abdominal ultrasound. An analysis of variance was used for comparison of normally distributed continuous data between groups， and the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A Kappa analysis was used to investigate the consistency between LSM noninvasive histological staging and pathological staging based on liver biopsy， and the Spearman analysis was used to investigate the correlation between each variable and FibroScan in the diagnosis of liver fibrosis stage. The Logistic regression analysis was used to construct joint predictive factors. The receiver operating characteristic （ROC） curve was used to evaluate the value of each indicator alone and the joint predictive model in the diagnosis of liver fibrosis， and the Delong test was used for comparison of the area under the ROC curve （AUC）. ResultsIn the consistency check， inflammation degree based on liver biopsy had a Kappa value of 0.807 （P<0.001）， and liver fibrosis degree based on liver biopsy had a Kappa value of 0.827 （P<0.001）， suggesting that FibroScan noninvasive histological staging and liver biopsy showed good consistency in assessing inflammation degree and liver fibrosis stage. Age was positively correlated with LSM， GPR score， S index， IL-6， and TNF-α （all P<0.05）， and GPR score， S index， IL-6， and TNF-α were positively correlated with LSM （all P<0.05）. GPR score， S index， IL-6， and TNF-α were all independent risk factors for diagnosing significant liver fibrosis （≥S2） and progressive liver fibrosis （≥S3） （all P<0.05）. As for each indicator alone， GPR score had the highest value in the diagnosis of significant liver fibrosis （≥S2）， followed by S index， IL-6， and TNF-α， while S index had the highest value in the diagnosis of progressive liver fibrosis （≥S3）， followed by GPR score， TNF-α， and IL-6. The joint model had a higher predictive value than each indicator alone （all P<0.05）. ConclusionThere is a good consistency between FibroScan noninvasive histological staging and pathological staging based on liver biopsy. GPR score， S index， IL-6， and TNF-α are independent risk factors for evaluating different degree of liver fibrosis in CHB， and the combined prediction model established by them can better diagnose liver fibrosis.

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 assess the use of R-R interval (RRI) sequence data collected via wearable devices to evaluate emotional dynamic factors in adolescents with depressive disorders, and to analyze their impact on diagnosis and severity assessment.Methods:Clinical data were prospectively collected from 154 adolescent inpatients with depressive disorders (132 females, 22 males; age 12-18, mean: 13.5±1.6 years) treated at the Child Psychiatry Ward of Beijing Anding Hospital, Capital Medical University between January 2023 and January 2024.A control group of 152 healthy adolescents (62 females, 90 males; age 12-18, mean 14.5±1.3 years) was recruited during the same period. RRI data were obtained using the built-in photoplethysmography (PPG) sensor in the HUAWEI Band 7 wearable device. The device′s integrated emotion evaluation system extracted arousal and emotional valence (as indicators of emotional dynamics) from the collected RRIs. A Long Short-Term Memory (LSTM) network was employed to develop a model for depression diagnosis and severity prediction, while a random forest model was applied to generate receiver operating characteristic (ROC) curves to evaluate model performance. Binary Logistic regression was conducted to investigate the influence of arousal and emotional valence on depression diagnosis and severity.Results:A total of 429 records were collected and analyzed from 306 participants. The LSTM-based diagnosis and severity assessment models achieved area under the curve (AUC) of 0.896 9 and 0.715 3, respectively, indicating good model performance. Binary Logistic regression analysis showed that arousal and emotional valence had significant effects on diagnosis and severity. Specially, lower arousal in both the first 4 hours ( β=-8.906, 95%CI:-17.497 to -0.315) and second 4 hours ( β=-3.033, 95%CI:-5.109 to -0.957) significantly predicted positive depression diagnosis ( β=-1.219, 95%CI:-2.205 to -0.233), while emotional valence in the second 4 hours showed a trend toward a positive association ( β=0.675, 95%CI:-0.107-1.457). First 4-hour emotional valence: significantly positive association with severity ( β=0.322, 95%CI: 0.067-0.577), second 4-hour arousal level: negative association with severity ( β=-0.258, 95%CI:-0.527 to 0.011), whereas arousal in the second 4 hours had a marginal negative effect (β=-0.258, 95% CI:-0.527 to 0.011). Conclusion:RRI may serve as a useful auxiliary measure in diagnosing depressive disorders and predicting severity among adolescents. Wearable smart devices offer promising potential for screening emotional dynamic factors related to adolescent depression.

Objective To analyze the influencing factors of chronic liver failure in patients with primary hepatic carcinoma(PHC)before intervention,and to establish and evaluate a nomogram risk prediction model.Methods A retrospective analysis was conducted by collecting general data and clinical test data within 24 hours of admission for PHC patients.Univariate analysis and Lasso regression were used for variable selection,followed by multivariate logistic regression analysis to identify independent influencing factors for CLF before PHC intervention,leading to the establishment of a nomogram risk prediction model.The model was evaluated using the Hosmer-Lemeshow test,receiver operating characteristic(ROC)curve,calibration curve,clinical decision curve,and clinical impact curve.Result A total of 353 cases of PHC patients were collected,including 153 cases in the liver failure group and 200 cases in the non-liver failure group,with a prevalence rate of 43.3％.Variables selected by Lasso regression included gastrointestinal bleeding,prothrombin time(PT),albumin(ALB),total bilirubin(TBIL),and gamma glutamyl transferase(GGT).Multivariate logistic regression analysis showed that gastrointestinal bleeding(OR=13.549,95％CI:2.899～63.322,P=0.001),PT(OR=1.599,95％CI:1.282～1.995,P＜0.001),TBIL(OR=1.016,95％CI:1.006～1.025,P=0.002),and GGT(OR=1.002,95％CI:1.000～1.003,P=0.028)were independent risk factors for chronic liver failure prior to PHC intervention,leading to the establishment of a nomogram risk prediction model.The Hosmer Lemeshow test showed that the model had a good fit(x2=6.152,P＞0.05);the area under ROC was 0.902(0.869-0.934),with a sensitivity of 80.4％and a specificity of 87.5％.The calibration curve indicated that the model predicts chronic liver failure prior to PHC intervention with good consistency.Clinical decision curve analysis and clinical impact curve analysis showed that the model has good clinical utility within a certain threshold range.Conclusion Gastrointestinal bleeding,PT ≥16.05s,TBIL≥37.80 mmol/L,and GGT≥ 99.00 U/L are independent risk factors for the occurrence of chronic liver failure before PHC intervention.The established nomogram risk prediction model has certain clinical application value in predicting the risk of chronic liver failure before PHC intervention.

Objective To compare the efficacy of Tenofovir Alafenamide(TMF)and Tenofovir Disoproxil Fumarate(TDF)in terms of liver function restoration,virus clearance,immune regulation,anti liver fibrosis,lipid metabolism,bone and renal safety,and adverse reactions.Methods A retrospective analysis was conducted on 110 patients with chronic hepatitis B(CHB)admitted to Kunming Third People's Hospital from January 2022 to December 2022.Patients were divided into the TMF treatment group(n=55)and the TDF treatment group(n=55)based on their treatment regimen.We compared the levels of transaminase levels,antiviral efficacy,T cell subsets,renal function electrolytes,lipid metabolism,four liver fibrosis-related indicators,and changes in liver stiffness grading before and after treatment in two groups of patients.The incidence of adverse reactions post-treatment was also compared.Results After 48 weeks of treatment,the levels of TBIL,ALT,AST,GGT,and GLOB in both groups of patients were significantly lower than pre-treatment levels(P<0.05).The decrease in AST levels in the TMF group was lower than that in the TDF group(P<0.05).After 48 weeks of treatment,the HBV-DNA seroconversion rate in the TMF group(90.90%)was higher than that in the TDF group(83.64%).The serological HBsAg clearance rate in the TMF group(7.3%)was lower than that in the TDF group(9.1%),while the HBeAg clearance rate in the TMF group(38.2%)was significantly higher than that in the TDF group(18.2%),with statistical significance(P<0.05).After 48 weeks of treatment,levels of CD3+,CD4+,and CD8+in both groups were significantly elevated compared to pre-treatment levels(P<0.05);notably,the TMF group had higher post-treatment levels of CD3+,CD4+,and CD8+than the TDF group.After 48 weeks,the average values of HA,IV-C,and LN among the TMF group for liver fibrosis indicators were significantly lower than those in the TDF group(P<0.05).The proportions of F0 and F2 in both groups significantly increased post-treatment,while the proportions of F3 and F4 significantly decreased(P to be supplemented);furthermore,the proportions of F0 and F2 in the TMF group were significantly higher than those in the TDF group,and the proportions of F3 and F4 in the TMF group were significantly lower than those in the TDF group(P<0.05).After 48 weeks,HDL-C levels in the TMF group increased compared to pre-treatment(P<0.05).There were no significant differences in TG,TC,HDL-C,or LDL-C levels in the TDF group compared to pre-treatment(P>0.05).After 48 weeks of treatment,there was no difference in the levels of BUN、Cr、P+,and Ca+in the TMF group compared to pre-treatment(P>0.05);however,BUN and Cr levels in the TDF group were significantly higher than pre-treatment levels,while P+and Ca+levels were significantly lower(P<0.05).The incidence of elevated uric acid and bone pain was significantly higher in the TMF group compared to the TDF group(P<0.05);the incidence of diarrhea and abdominal pain was slightly higher in the TMF group compared to the TDF group(P>0.05).Conclusion Compared to TDF,TMF demonstrates a higher rate of liver function recovery,a greater virological response,enhanced anti fibrotic efficacy,and improved drug safety,making it worthy of clinical application in the future.

ObjectiveTo investigate the influencing factors for hepatic hydrothorax （HH） before intervention for primary hepatic carcinoma （PHC）， and to construct and assess the nomogram risk prediction model. MethodsA retrospective analysis was performed for the clinical data of 353 hospitalized patients who attended the Third People’s Hospital of Kunming for the first time from October 2012 to October 2021 and there diagnosed with PHC， and according to the presence or absence of HH， they were divided into HH group with 153 patients and non-HH group with 200 patients. General data and the data of initial clinical testing after admission were collected from all PHC patients. 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 groups. After the multicollinearity test was performed for the variables with statistical significance determined by the univariate analysis， the multivariate Logistic regression analysis was used to identify independent influencing factors. The “rms” software package was used to construct a nomogram risk prediction model， and the Hosmer-Lemeshow test and the receiver operating characteristic （ROC） curve were used to assess the risk prediction model； the “Calibration Curves” software package was used to plot the calibration curve， and the “rmda” software package was used to plot the clinical decision curve and the clinical impact curve. ResultsAmong the 353 patients with PHC， there were 153 patients with HH， with a prevalence rate of 43.34%. Child-Pugh class B （odds ratio ［OR］=2.652， 95% confidence interval ［CI］： 1.050 — 6.698， P=0.039）， Child-Pugh class C （OR=7.963， 95%CI： 1.046‍ ‍—‍ ‍60.632， P=0.045）， total protein （OR=0.947， 95%CI： 0.914‍ ‍—‍ ‍0.981， P=0.003）， high-sensitivity C-reactive protein （OR=1.007， 95%CI： 1.001‍ ‍—‍ ‍1.014， P=0.025）， and interleukin-2 （OR=0.801， 95%CI： 0.653‍ ‍—‍ ‍0.981， P=0.032） were independent influencing factors for HH before PHC intervention， and a nomogram risk prediction model was established based on these factors. The Hosmer-Lemeshow test showed that the model had a good degree of fitting （χ²=5.006， P=0.757）， with an area under the ROC curve of 0.752 （95%CI： 0.701‍ ‍—‍ ‍0.803）， a sensitivity of 78.40%， and a specificity of 63.50%. The calibration curve showed that the model had good consistency in predicting HH before PHC intervention， and the clinical decision curve and the clinical impact curve showed that the model had good clinical practicability within a certain threshold range. ConclusionChild-Pugh class， total protein， interleukin-2， and high-sensitivity C-reactive protein are independent influencing factors for developing HH before PHC intervention， and the nomogram model established based on these factors can effectively predict the risk of developing HH.