Objective:To explore the application effect of continuous quality improvement based on trauma team activation model in emergency triage.Methods:This study is a historical controlled study. A total of 12 215 trauma patients receiving initial treatment in the Emergency Department of the First Affiliated Hospital with Nanjing Medical University from January 2020 to June 2021 were selected as the research objects by the convenience sampling method, and 3 896 trauma patients from January to June 2020 were enrolled as the baseline group, and 4 054 patients from July to December 2020 were enrolled as the phaseⅠ improvement group, and 4 265 patients from January to June 2021 were enrolled as the phaseⅡ improvement group. The baseline group adopted the one-tiered trauma team activation model, which was evaluated and decided by the first-visiting doctor. The phaseⅠ improvement group received the one-tiered trauma team activation model, which was evaluated and decided by pretest triage nurses. The stageⅡ improvement group conducted the two-tiered trauma team activation model, which was evaluated and decided by pretest triage nurses. The trauma team activation time, overtriage rate and undertriage rate were compared between the three groups.Results:The trauma team activation time of the phaseⅠ improvement group and phaseⅡ improvement group was shorter than that of the baseline group, and the difference was statistically significant ( P<0.05). The overtriage rate of the phaseⅡ improvement group was lower than that of the baseline group and the phaseⅠ improvement group, and the differences were statistically significant ( P<0.05). The underage rate of the three groups were <5%, and the differences were not statistically significant ( P>0.05) . Conclusions:The two-tiered activation of trauma team mode led by nurses can shorten the trauma team activation time, reduce the overtriage rate and undertriage rate, improve the quality of trauma team activation.

Objective:To compare the performance of multiple machine learning algorithms in predicting recurrence after resection of early-stage hepatocellular carcinoma(HCC).Methods:Clinical data of 882 early-stage HCC patients who were admitted to the First Affiliated Hospital of Nanjing Medical University from May 2009 to December 2019 and treated with curative surgical resection were retrospectively collected. There were 701 males and 181 females,with an age of (57.3±10.5)years(range:21 to 86 years). All patients were randomly assigned in a 2∶1 ratio, the training dataset consisted of 588 patients and the test dataset consisted of 294 patients. The construction of machine learning-based prediction models included random survival forest(RSF),gradient boosting machine,elastic net regression and Cox regression model. The prediction accuracy of the model was measured by the concordance index(C-index). The prediction error of the model was measured by the integrated Brier score. Model fit was assessed by the calibration plot. The performance of machine learning models with that of rival model and HCC staging systems was compared. All models were validated in the independent test dataset.Results:Median recurrence-free survival was 61.7 months in the training dataset while median recurrence-free survival was 61.9 months in the validation dataset, there was no significant difference between two datasets in terms of recurrence-free survival( χ2=0.029, P=0.865). The RSF model consisted of 5 commonly used clinicopathological characteristics, including albumin-bilirubin grade,serum alpha fetoprotein,tumor number,type of hepatectomy and microvascular invasion. In both training and test datasets,the RSF model provided the best prediction accuracy,with respective C-index of 0.758(95% CI:0.725 to 0.791) and 0.749(95% CI:0.700 to 0.797),and the lowest prediction error,with respective integrated Brier score of 0.171 and 0.151. The prediction accuracy of RSF model for recurrence after resection of early-stage HCC was superior to that of other machine learning models,rival model(ERASL model) as well as HCC staging systems(BCLC,CNLC and TNM staging),with statistically significant difference( P<0.01). Calibration curves demonstrated good agreement between RSF model-predicted probabilities and observed outcomes.All patients could be stratified into low-risk,intermediate-risk or high-risk group based on RSF model;statistically significant differences among three risk groups were observed in both training and test datasets(all P<0.01). The risk stratification of RSF model was superior to that of TNM staging. Conclusion:The proposed RSF model assembled with 5 commonly used clinicopathological characteristics in this study can predict the recurrence risk with favorable accuracy that may facilitate clinical decision-support for patients with early-stage HCC.

Objective:To compare the performance of multiple machine learning algorithms in predicting recurrence after resection of early-stage hepatocellular carcinoma(HCC).Methods:Clinical data of 882 early-stage HCC patients who were admitted to the First Affiliated Hospital of Nanjing Medical University from May 2009 to December 2019 and treated with curative surgical resection were retrospectively collected. There were 701 males and 181 females,with an age of (57.3±10.5)years(range:21 to 86 years). All patients were randomly assigned in a 2∶1 ratio, the training dataset consisted of 588 patients and the test dataset consisted of 294 patients. The construction of machine learning-based prediction models included random survival forest(RSF),gradient boosting machine,elastic net regression and Cox regression model. The prediction accuracy of the model was measured by the concordance index(C-index). The prediction error of the model was measured by the integrated Brier score. Model fit was assessed by the calibration plot. The performance of machine learning models with that of rival model and HCC staging systems was compared. All models were validated in the independent test dataset.Results:Median recurrence-free survival was 61.7 months in the training dataset while median recurrence-free survival was 61.9 months in the validation dataset, there was no significant difference between two datasets in terms of recurrence-free survival( χ2=0.029, P=0.865). The RSF model consisted of 5 commonly used clinicopathological characteristics, including albumin-bilirubin grade,serum alpha fetoprotein,tumor number,type of hepatectomy and microvascular invasion. In both training and test datasets,the RSF model provided the best prediction accuracy,with respective C-index of 0.758(95% CI:0.725 to 0.791) and 0.749(95% CI:0.700 to 0.797),and the lowest prediction error,with respective integrated Brier score of 0.171 and 0.151. The prediction accuracy of RSF model for recurrence after resection of early-stage HCC was superior to that of other machine learning models,rival model(ERASL model) as well as HCC staging systems(BCLC,CNLC and TNM staging),with statistically significant difference( P<0.01). Calibration curves demonstrated good agreement between RSF model-predicted probabilities and observed outcomes.All patients could be stratified into low-risk,intermediate-risk or high-risk group based on RSF model;statistically significant differences among three risk groups were observed in both training and test datasets(all P<0.01). The risk stratification of RSF model was superior to that of TNM staging. Conclusion:The proposed RSF model assembled with 5 commonly used clinicopathological characteristics in this study can predict the recurrence risk with favorable accuracy that may facilitate clinical decision-support for patients with early-stage HCC.

Objective:To construct a computed tomography (CT)-based radiomics model for predicting tumor recurrence of early-stage hepatocellular carcinoma (HCC) after resection, and explore its application value.Methods:The retrospective cohort study was conducted. The clinicopathological data of 243 patients with early-stage HCC who underwent hepatectomy in 2 medical centers between January 2009 and December 2016 were collected, including 165 in the First Affiliated Hospital of Nanjing Medical University and 78 in the Wuxi People′s Hospital. There were 182 males and 61 females, aged from 30 to 86 years, with a median age of 57 years. According to the random numbers showed in the computer, 243 patients were randomly assigned into training dataset consisting of 162 patients and test dataset consisting of 81 patients, with a ratio of 2∶1. Using radiomics technique, a total of 3 384 radiomics features were extracted from the tumor and its periphery at arterial-phase and portal-phase images of CT scan. In the training dataset, a radiomics signature was constructed and predicted its performance after dimension reduction of stable features by using aggregated feature selection algorithms [feature ranking via maximal relevance and minimal redundancy (MRMR) combined with random survival forest (RSF) + LASSO-COX regression analysis]. Risk factors for tumor recurrence were selected using the univariate COX regression analysis, and two radiomics models including radiomics 1 (preoperative) and radiomics 2 (postoperative) were constructed and predicted their performance using backward stepwise multivariate COX regression analysis. The two models were validated in the training and test dataset. Observation indicators: (1) follow-up; (2) construction of HCC recurrence-related radiomics signature for early-stage HCC after resection; (3) prediction performance of HCC recurrence-related radiomics signature for early-stage HCC after resection; (4) construction of HCC recurrence-related radiomics prediction model for early-stage HCC after resection; (5) validation of HCC recurrence-related radiomics prediction model for early-stage HCC after resection; (6) comparison of the prediction performance of radiomics model with that of other clinical statistical models and current HCC staging systems; (7) stratification analysis of postoperative recurrence risk based on radiomics models for early-stage HCC after resection. Patients were followed up using outpatient examination or telephone interview once every 3 months within the first 2 years and once every 6 months after 2 years. The follow-up included collection of medical history, laboratory examination, and abdominal ultrasound examination. Contrast-enhanced CT or magnetic resonance imaging (MRI) examination was performed once every 6 months, and they were performed in advance on patients who had suspected recurrence based on laboratory examination or abdominal ultrasound for further diagnosis. Follow-up was up to January 2019. The endpoint was time to recurrence, which was from the date of surgery to the date of first detected disease recurrence or metastasis. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed by the t test. Measurement data with skewed distribution were described as M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were described as absolute numbesr or percentages, and comparison between groups was analyzed using the chi-square test. The survival curve and survival rate were respectively drawn and calculated by the Kaplan-Meier method, and the survival analysis was performed using the Log-rank test. Serum alpha-fetoprotein level was analyzed after the natural logarithm transformation. X-tile software was used to select the optimal cut-point for continuous markers. Results:(1) Follow-up: all the 243 HCC patients received follow-up. Patients in the training dataset were followed up for 4.2-109.2 months, with a median follow-up time of 51.6 months. Patients in the test dataset were followed up for 12.7-107.6 months, with a median follow-up time of 73.2 months. The 2-, 5-year disease-free survival rates were 77.8% and 53.1% of the training dataset respectively, versus 86.4% and 61.7% of the test dataset. There was no significant difference in terms of disease-free survival between two datasets ( χ2=1.773, P>0.05). (2) Construction of HCC recurrence-related radiomics signature for early-stage HCC after resection: of the 3 384 radiomics features, 2 426 radiomics features with high stability were selected for analysis. There were 37 radiomics features identified after combining the top 20 radiomics features ranked by MRMR and RSF algorithms. LASSO-COX regression algorithm further reduced their dimensionality to retain 7 radiomics features and construct a radiomics signature. The indicators including region, scanning phase, and weighting coefficient of above mentioned seven features were Feature 1 (peritumoral, arterial phase, 0.041), Feature 2 (peritumoral, arterial phase, -0.103), Feature 3 (peritumoral, arterial phase, -0.259), Feature 4 (intratumoral, arterial phase, 0.211), Feature 5 (peritumoral, portal venous phase, -0.170), Feature 6 (intratumoral, portal venous phase, 0.130), and Feature 7 (intratumoral, portal venous phase, 0.090), respectively. Radiomics signature score=0.041×Feature 1-0.103×Feature 2-0.259×Feature 3+ 0.211×Feature 4-0.170×Feature 5+ 0.130×Feature 6+ 0.090×Feature 7. (3) Prediction performance of HCC recurrence-related radiomics signature for early-stage HCC after resection: the radiomics signature showed favorable prediction performance in both training and test datasets, with respective C-index of 0.648 [95% confidence interval ( CI): 0.583-0.713] and 0.669 (95% CI: 0.587-0.750). (4) Construction of HCC recurrence-related radiomics prediction model for early-stage HCC after resection: results of univariate analysis showed that ln(serum alpha-fetoprotein), liver cirrhosis, tumor margin status, arterial peritumoral enhancement, intratumoral necrosis, radiomics signature, satellite nodules, and microvascular invasion were related factors for tumor recurrence after resection of early-stage HCC ( hazard ratio=1.202, 1.776, 1.889, 2.957, 1.713, 4.237, 4.364, 4.258, 95% CI: 1.083-1.333, 1.068-2.953, 1.181-3.024, 1.462-5.981, 1.076-2.728, 2.593-6.923, 2.468-7.717, 2.427-7.468, P<0.05 ). Results of multivariate analysis showed that the radiomics model 1 (preoperative) consisted of ln(serum alpha-fetoprotein), tumor margin status, and radiomics signature ( hazard ratio=1.145, 1.838, 3.525, 95% CI: 1.029-1.273, 1.143-2.955, 2.172-5.720, P<0.05); the radiomics model 2 (postoperative) consisted of ln(serum alpha-fetoprotein), radiomics signature, microvascular invasion, and satellite nodules ( hazard ratio=1.123, 2.386, 3.456, 3.481, 95% CI: 1.005-1.254, 1.501-3.795, 1.863-6.410, 1.891-6.408, P<0.05). Risk prediction formulas: radiomics model 1 = 0.135×ln(serum alpha-fetoprotein)+ 0.608×tumor margin status (0: smooth; 1: non-smooth)+ 1.260×radiomics signature; radiomics model 2 = 0.116×ln(serum alpha-fetoprotein)+ 0.870×radiomics signature + 1.240×microvascular invasion (0: absent; 1: present)+ 1.247×satellite nodules (0: absent; 1: present). (5) Validation of HCC recurrence-related radiomics prediction model for early-stage HCC after resection: in both training and test datasets, radiomics model 1 provided good prediction performance, with respective C-index of 0.716 (95% CI: 0.662-0.770) and 0.724 (95% CI: 0.642-0.806), while radiomics model 2 provided better prediction performance, with respective C-index of 0.765 (95% CI: 0.712-0.818) and 0.741 (95% CI: 0.662-0.820). Calibration curves demonstrated good agreement between model-predicted probabilities and observed outcomes. (6) Comparison of the prediction performance of radiomics model with that of other clinical statistical models and current HCC staging systems: in the training dataset, the prediction performance of radiomics model 1 for tumor recurrence after resection of early-stage HCC was significantly different from that of ERASL model (preoperative), Barcelona clinic liver cancer (BCLC) staging, Hong Kong liver cancer (HKLC) staging, and cancer of the liver Italian program (CLIP) classification (C-index=0.562, 0.484, 0.520, 0.622, 95% CI: 0.490-0.634, 0.311-0.658, 0.301-0.740, 0.509-0.736, P<0.05); the prediction performance of radiomics model 2 for tumor recurrence after resection of early-stage HCC was significantly different from that of ERASL model (postoperative), Korean model, and the eighth edition TNM staging (C-index=0.601, 0.523, 0.513, 95% CI: 0.524-0.677, 0.449-0.596, 0.273-0.753, P<0.05). In the test dataset, the prediction performance of radiomics model 1 for tumor recurrence after resection of early-stage HCC was significantly different from that of ERASL model (preoperative), BCLC staging, HKLC staging, CLIP classification (C-index=0.540, 0.473, 0.504, 0.545, 95% CI: 0.442-0.638, 0.252-0.693, 0.252-0.757, 0.361-0.730, P<0.05); the prediction performance of radiomics model 2 for tumor recurrence after resection of early-stage HCC was significantly different from that of ERASL model (postoperative), Korean model, and the eighth edition TNM staging (C-index=0.562, 0.513, 0.521, 95% CI: 0.451-0.672, 0.399-0.626, 0.251-0.791, P<0.05). (7) Stratification analysis of postoperative recurrence risk based on radiomics models for tumor recurrence after resection of early-stage HCC: according to the analysis of X-tile, the score of radiomics model 1 < 1.4 (corresponding to total points < 62.0 in nomogram) was classified into low-risk group while the score of radiomics model 1 ≥ 1.4 (corresponding to total points ≥ 62.0 in nomogram) was classified into high-risk group. The score of radiomics model 2 < 1.7 (corresponding to total points < 88.0 in nomogram) was classified into low-risk group while the score of radiomics model 2 ≥ 1.7 (corresponding to total points ≥ 88.0 in nomogram) was classified into high-risk group. In the training dataset, the 2- and 5-year recurrence rates were 14.1%, 35.3% for low-risk patients and 63.0%, 100.0% for high-risk patients, which were predicted by radiomics model 1. There were significant differences between the two groups ( χ2= 70.381, P<0.05). The 2- and 5-year recurrence rates were 12.9%, 38.2% for low-risk patients and 81.8%, 100.0% for high-risk patients, which were predicted by radiomics model 2. There were significant differences between the two groups ( χ2= 98.613, P<0.05). In the test dataset, the 2- and 5-year recurrence rates were 5.6%, 29.3% for low-risk patients and 70.0%, 100.0% for high-risk patients, which were predicted by radiomics model 1. There were significant differences between the two groups ( χ2= 64.453, P<0.05). Ther 2- and 5-year recurrence rates were 5.7%, 28.1% for low-risk patients and 63.6%, 100.0% for high-risk patients, which were predicted by radiomics model 2. There were significant differences between the two groups ( χ2= 58.032, P<0.05). Conclusions:The 7-feature-based radiomics signature is built by selection of CT radiomics features in this study, and then HCC recurrence-related radiomics prediction model for early-stage HCC after resection is constructed. The proposed radiomics models can complement the existing clinical-radiological-pathological prognostic sources, accurately and individually predict tumor recurrence risk preoperatively and postoperatively, which facilitate clinical decision-support for patients with early-stage HCC.

Objective:To compair the outcomes of repairing soft tissue defects of hand between a biodegrad- able repair patch—porcine small intestinal submucosa (SIS) and skin grafting.Methods:From December, 2017 to December, 2018, 36 cases of hand soft tissue defect were treated and analyzed retrospectively. According to the defect area and treatment methods, 36 cases were divided into 2 groups: SIS group (21 cases) and grafting group(15 cases). In SIS group, the area of soft tissue defects was 2.0 cm×1.5 cm-9.0 cm×3.5 cm with an average of 5.3 cm×2.1 cm, treat- ed with SIS; In grafting group, the area of soft tissue defects was 9.0 cm×4.0 cm-16.0 cm×9.0 cm with an average of 12.0 cm×8.5 cm, treated with autologous skin grafting after wet dressing. Wound healing was evaluated at 14, 21 and 28 days, and 3 months after the surgery according to the appearance of colour, elasticity, sensory recovery and prog- noses of partial tendon exposure.Results:All patients were followed-up for 3 to 10 months, with an average of 5 months. All wounds in both groups were completely healed; the appearance was normal, and the skin elasticity and sensation had recovered. Sensation recovery in SIS group: 14 cases were good (66.6%), 5 cases were fine (23.8%), and 2 cases were bad (9.6%); in grafting group: 9 cases were good (60.0%), 4 cases were fine (26.0%), and 2 cases were bad (14.0% ). Wound healing effect in SIS group: 14 cases were good, 5 cases were fine, and 2 cases were bad; in grafting group: 9 cases were good, 4 cases were fine, and 2 cases were bad.Conclusion:The SIS patch can be used in the reconstruction of soft tissue defects in hand. There was no significant difference in colour compared to the sur- rounding skin and left no scar. The patch is an ideal repair material for superficial skin defects.

Radiomics, as an emerging technique of omics, shows the pathophysiological information of images via extracting innumerable quantitative features from digital medical images. In recent years, it has been an exponential increase in the number of radiomics studies. The applications of radiomics in hepatobiliary diseases at present include: assessment of liver fibrosis, discrimination of malignant from benign tumors, prediction of biological behavior, assessment of therapeutic response, and prognosis. Integrating radiomics analysis with machine learning algorithms has emerged as a non-invasive method for predicting liver fibrosis stages, microvascular invasion and post-resection recurrence in liver cancers, lymph node metastasis in biliary tract cancers as well as treatment response in colorectal liver metastasis, with high performance. Although the challenges remain in the clinical transformation of this technique, radiomics will have a broad application prospect in promoting the precision diagnosis and treatment of hepatobiliary diseases, backed by multi-center study with large sample size or multi-omics study.

Radiomics, as an emerging technique of omics, shows the pathophysiological information of images via extracting innumerable quantitative features from digital medical images. In recent years, it has been an exponential increase in the number of radiomics studies. The applications of radiomics in hepatobiliary diseases at present include: assessment of liver fibrosis, discrimination of malignant from benign tumors, prediction of biological behavior, assessment of therapeutic response, and prognosis. Integrating radiomics analysis with machine learning algorithms has emerged as a non-invasive method for predicting liver fibrosis stages, microvascular invasion and post-resection recurrence in liver cancers, lymph node metastasis in biliary tract cancers as well as treatment response in colorectal liver metastasis, with high performance. Although the challenges remain in the clinical transformation of this technique, radiomics will have a broad application prospect in promoting the precision diagnosis and treatment of hepatobiliary diseases, backed by multi-center study with large sample size or multi-omics study.

Objective To evaluate the diagnostic value of magnetic resonance spectroscopy analysis in patients with prostate specific antigen (PSA) abnormal prostate disease. Methods The patients who had abnormal PSA from January 2017 to December 2018 in Weifang People′s Hospital were selected. Patients with prostate puncture indications were as research target. A total of 137 patients including 44 patients with prostate cancer and 93 patients with benign prostatic hyperplasia were diagnosed by puncture. All patients underwent magnetic resonance scan and enhancement and spectroscopy before surgery. The comparative value of magnetic resonance scan + enhancement, magnetic resonance spectroscopy and joint examination was obtained. Results Sensitivity of magnetic resonance imaging and enhancement was 77.3% (34/44), specificity was 86.0% (80/93), and accuracy was 83.2% (114/137). Sensitivity of magnetic resonance spectroscopy was 52.3% (23/44), specificity was 77.4% (72/93), and accuracy was 69.3% (95/137). The sensitivity of the combined application was 90.9% (40/44), specificity was 91.4% (85/93), and accuracy was 91.2% (125/137). Conclusions The application of magnetic resonance spectroscopy can increase the sensitivity, specificity and accuracy of magnetic resonance plain scan and intensive examination for diagnosis of prostate cancer.

Objective To investigate the feasibility and technique of using the anterolateral thigh perforator flap pedicled with the lateral branch of the lateral circumflex femoral artery (LBLCFA) to repair soft tissue defect of extremities.Methods Eighty-six cases of anterolateral thigh perforator flap transplantation were performed from May,2014 to May,2018.A total of 37 cases of soft tissue defect of extremities were treated by anterolateral thigh perforator flap used the LBLCFA as vascular pedicle,of which there were 19 cases of upper limbs and 18 of lower limbs.There were 27 cases of defects caused by trauma,and 10 by soft tissue tumor resection.The flaps were designed centering around the point proximal to the midpoint of the iliac-patellar line.The dimensions of soft tissue defect were from 9.0 cm×6.0 cm to 26.0 cm×10.0 cm,and the flap were from 10.0 cm×7.0 cm to 27.0 cm×11.0 cm.The length of vascular pedicle ranged of 7.0-13.0 cm,with an average of 11.5 cm.The donor sites were directly sutured.All of the patients were followed-up regularly in the outpatient department.Results All the flaps survived and the donor sites were primarily healed.Of these 37 cases,2 trauma patients and 2 patients treated with local radiotherapy had poor wound healing,but still healed after multiple dressing changes.All the patients were followed-up for 3-26 months,with an average of 13 months.The texture,color and elasticity of the flap were similar to the surrounding tissue of the recipient sites,while only a linear scar remained at the donor sites.Ten tumor patients were treated with routine radiotherapy and chemotherapy after the repairation;there was no tumor recurrence during the follow-up period.Conclusion The LBLCFA gives off a relatively large and thick perforator proximal 5.0-7.0 cm of the iliacpatellar line,which locates in the upper lateral side,travels parallel to the vastus lateralis,and give off the skin and muscular perforators.The lateral branch can be used as pedicle to make into perforator flap or chimeric flap,which provides a novel selection of vascular pedicel for anterolateral thigh perforator flap.When the descending branch does not provide a thick and large perforator,the LBLCFA has important practical value and is worth utilizing in the clinic.

Objective To explore the empowerment education application effect on the healthy education for patients with hepatitis B cirrhosis. Methods A total of 100 cases of hepatitis B cirrhosis decompensation were selected from January to May 2017 in a ClassⅢ Grade A infectious disease specialist hospital in Nantong with convenience sampling method. All the research subjects were divided into observation group and control group by random number table method, with 50 cases in each group. Routine health guidance of clinical pathway was conducted for the control group, and empowerment education based on routine education was carried out for the observation group. Before and after the intervention, self-management behaviors, the incidence rate of cirrhosis complications and nursing job satisfaction in the two groups were compared respectively. Results There were 1 death case in the observation group and 2 cases discharged after 3 days in the control group during the intervention. Finally, there were 49 cases in the observation group and 48 cases in the control group. The evaluation scores of self-management behaviors which includes diet management, daily life management, medication administration, disease monitoring management were higher than those in the control group. The differences were statistically significant(t=-4.047, -3.635, -3.437, -3.831; P＜0.05). Satisfaction with nursing service which includes service attitude, communication ability, humanistic care and theoretical guidance in the observation group was significantly higher than that in the control group. The differences were statistically significant (χ2=7.789, 6.804, 6.557, 6.239; P＜ 0.05). The incidence rate of cirrhosis complications which includes hepatic encephalopathy and electrolyte disorder was lower than that in the control group, and the differences were statistically significant (χ2=4.549, 4.401; P＜ 0.05). Conclusions Empowerment education can improve self-management behavior of patients with hepatitis B cirrhosis and satisfaction, and reduce the incidence rate of complications.