Development and external validation of a prognostic nomogram for acute decompensation of chronic hepatitis B cirrhosis

Identification of multiple complications as independent risk factors associated with 1-, 3-, and 5-year mortality in hepatitis B-associated cirrhosis patients

BACKGROUND

Hepatitis B-associated cirrhosis (HBC) is associated with severe complications and adverse clinical outcomes. This study aimed to develop and validate a predictive model for the occurrence of multiple complications (three or more) in patients with HBC and to explore the effects of multiple complications on HBC prognosis.

METHODS

In this retrospective cohort study, data from 121 HBC patients treated at Nanjing Second Hospital from February 2009 to November 2019 were analysed. The maximum follow-up period was 10.75 years, with a median of 5.75 years. Eight machine learning techniques were employed to construct predictive models, including C5.0, linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), k-nearest neighbour (KNN), gradient boosting decision tree (GBDT), support vector machine (SVM), generalised linear model (GLM) and naive Bayes (NB), utilising variables such as medical history, demographics, clinical signs, and laboratory test results. Model performance was evaluated via receiver operating characteristic (ROC) curve analysis, residual analysis, calibration curve analysis, and decision curve analysis (DCA). The influence of multiple complications on HBC survival time was assessed via Kaplan‒Meier curve analysis. Furthermore, LASSO and univariable and multivariable Cox regression analyses were conducted to identify independent prognostic factors for overall survival (OS) in patients with HBC, followed by ROC, C-index, calibration curve, and DCA curve analyses of the constructed prognostic nomogram model. This study utilized bootstrap resampling for internal validation and employed the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for external validation.

RESULTS

The GBDT model exhibited the highest area under the curve (AUC) and emerged as the optimal model for predicting the occurrence of multiple complications. The key predictive factors included posthospitalisation fever (PHF), body mass index (BMI), retinol binding protein (RBP), total bilirubin (TB) levels, and eosinophils (EOS). Kaplan-Meier analysis revealed that patients with multiple complications had significantly worse OS than those with fewer complications. Additionally, multivariable Cox regression analysis, informed by least absolute shrinkage and LASSO selection, identified hepatocellular carcinoma (HCC), multiple complications, and lactate dehydrogenase (LDH) levels as independent prognostic factors for OS. The prognostic model demonstrated 1-year, 3-year, and 5-year OS ROC AUCs of 0.802, 0.793, and 0.817, respectively. For the internal validation cohort, the corresponding AUC values were 0.797, 0.832, and 0.835. In contrast, the external validation cohort yielded a 1-year ROC AUC of 0.707. Calibration curves indicated good consistency of the model, and DCA demonstrated the model's clinical utility, showing high net benefits within certain threshold ranges. Compared with the univariable models, the multivariable ROC curves indicated higher AUC values for this prognostic model, and the model also possessed the best c-index.

CONCLUSION

The GBDT prediction model provides a reliable tool for the early identification of high-risk HBC patients prone to developing multiple complications. The concurrent occurrence of multiple complications is an independent prognostic factor for OS in patients with HBC. The constructed prognostic model demonstrated remarkable predictive performance and clinical applicability, indicating its crucial role in enhancing patient outcomes through timely and targeted interventions.

Influence of metabolic dysfunction-associated fatty liver disease on the prognosis of patients with HBV-related acute-on-chronic liver failure

OBJECTIVES

Metabolic-associated fatty liver disease (MAFLD) has clinical relevance in patients with acute-on-chronic liver failure (ACLF). We investigated the association between MAFLD and prognosis in patients with ACLF.

METHODS

We included patients with ACLF with available clinical data who visited our hospital for nearly 9 years. We compared the prognosis of patients in the different subgroups of ACLF and predicted the incidence of adverse outcomes. Moreover, a new model based on MAFLD was established.

RESULTS

Among 339 participants, 75 had MAFLD. The prognosis of patients with ACLF was significantly correlated with MAFLD. Patients with ACLF with concomitant MAFLD tended to have a lower cumulative survival rate (p = 0.026) and a higher incidence of hepatorenal syndrome (9.33% versus 3.40%, p = 0.033) than those without MAFLD. We developed an TIM2 model and the area under the ROC curve of the new model for 30-day and 60-day mortality (0.759 and 0.748) was higher than other predictive methods.

CONCLUSION

The presence of MAFLD in patients with HBV-related ACLF was associated with an increased risk of in-hospital mortality. Moreover, The TIM2 model is a high-performance prognostic score for HBV-related ACLF.

Establishment and validation of a nomogram model for riskprediction of hepatic encephalopathy: a retrospective analysis

To establish a high-quality, easy-to-use, and effective risk prediction model for hepatic encephalopathy, to help healthcare professionals with identifying people who are at high risk of getting hepatic encephalopathy, and to guide them to take early interventions to reduce the occurrence of hepatic encephalopathy. Patients (n = 1178) with decompensated cirrhosis who attended the First Affiliated Hospital of Guangxi University of Chinese Medicine between January 2016 and June 2022 were selected for the establishment and validation of a nomogram model for risk prediction of hepatic encephalopathy. In this study, we screened the risk factors for the development of hepatic encephalopathy in patients with decompensated cirrhosis by univariate analysis, LASSO regression and multifactor analysis, then established a nomogram model for predicting the risk of getting hepatic encephalopathy for patients with decompensated cirrhosis, and finally performed differentiation analysis, calibration analysis, clinical decision curve analysis and validation of the established model. A total of 1178 patients with decompensated cirrhosis who were hospitalized and treated at the First Affiliated Hospital of Guangxi University of Chinese Medicine between January 2016 and June 2022 were included for modeling and validation. Based on the results of univariate analysis, LASSO regression analysis and multifactor analysis, a final nomogram model with age, diabetes, ascites, spontaneous peritonitis, alanine transaminase, and blood potassium as predictors of hepatic encephalopathy risk prediction was created. The results of model differentiation analysis showed that the AUC of the model of the training set was 0.738 (95% CI 0.63-0.746), while the AUC of the model of the validation set was 0.667 (95% CI 0.541-0.706), and the two AUCs indicated a good discrimination of this nomogram model. According to the Cut-Off value determined by the Jorden index, when the Cut-Off value of the training set was set at 0.150, the sensitivity of the model was 72.8%, the specificity was 64.8%, the positive predictive value was 30.4%, and the negative predictive value was 91.9%; when the Cut-Off value of the validation set was set at 0.141, the sensitivity of the model was 69.7%, the specificity was 57.3%, the positive predictive value was 34.5%, and the negative predictive value was 84.7%. The calibration curve and the actual events curve largely overlap at the diagonal, indicating that the prediction with this model has less error. The Hosmer-Lemeshow test for goodness of fit was also applied, and the results showed that for the training set, χ2 = 1.237587, P = 0.998, and for the validation set, χ2 = 31.90904, P = 0.0202, indicating that there was no significant difference between the predicted and actual observed values. The results of the clinical decision curve analysis showed that the model had a good clinical benefit, compared with the two extreme clinical scenarios (all patients treated or none treated), and the model also had a good clinical benefit in the validation set. This study showed that aged over 55 years, complications of diabetes, ascites, and spontaneous bacterial peritonitis, abnormal glutamate aminotransferase and abnormal blood potassium are independent risks indicators for the development of hepatic encephalopathy in patients with decompensated cirrhosis. The nomogram model based on the indicators mentioned above can effectively and conveniently predict the risk of developing hepatic encephalopathy in patients with decompensated cirrhosis. The nomogram model established on this study can help clinical healthcare professionals to timely and early identify patients with high risk of developing hepatic encephalopathy.

EASL Clinical Practice Guidelines on acute-on-chronic liver failure

Acute-on-chronic liver failure (ACLF), which was described relatively recently (2013), is a severe form of acutely decompensated cirrhosis characterised by the existence of organ system failure(s) and a high risk of short-term mortality. ACLF is caused by an excessive systemic inflammatory response triggered by precipitants that are clinically apparent (e.g., proven microbial infection with sepsis, severe alcohol-related hepatitis) or not. Since the description of ACLF, some important studies have suggested that patients with ACLF may benefit from liver transplantation and because of this, should be urgently stabilised for transplantation by receiving appropriate treatment of identified precipitants, and full general management, including support of organ systems in the intensive care unit (ICU). The objective of the present Clinical Practice Guidelines is to provide recommendations to help clinicians recognise ACLF, make triage decisions (ICU vs. no ICU), identify and manage acute precipitants, identify organ systems that require support or replacement, define potential criteria for futility of intensive care, and identify potential indications for liver transplantation. Based on an in-depth review of the relevant literature, we provide recommendations to navigate clinical dilemmas followed by supporting text. The recommendations are graded according to the Oxford Centre for Evidence-Based Medicine system and categorised as 'weak' or 'strong'. We aim to provide the best available evidence to aid the clinical decision-making process in the management of patients with ACLF.

Derivation and Validation of a Prognostic Model for Acute Decompensated Cirrhosis Patients

Background

Acute decompensated cirrhosis (AD) is related to high medical costs and high mortality. We recently proposed a new score model to predict the outcome of AD patients and compared it with the common score model (CTP, MELD and CLIF-C AD score) in the training and validation sets.

Materials and Methods

A total of 703 patients with AD were enrolled from The First Affiliated Hospital of Nanchang University between December 2018 and May 2021. These patients were randomly assigned to the training set (n=528) and validation set (n=175). Risk factors affecting prognosis were identified by Cox regression analysis and then used to establish a new score model. The prognostic value was determined by the area under the receiver operating characteristic curve (AUROC).

Results

A total of 192 (36.3%) patients in the training cohort and 51 (29.1%) patients in the validation cohort died over the course of 6 months. A new score model was developed with predictors including age, bilirubin, INR, WBC, albumin, ALT and BUN. The new prognostic score (0.022×Age + 0.003×TBil + 0.397×INR + 0.023×WBC- 0.07×albumin + 0.001×ALT + 0.038×BUN) for long-term mortality was superior to three other scores based on both training and internal validation studies.

Conclusion

This new score model appears to be a valid tool for assessing the long-term survival of AD patients, improving the prognostic value compared with the CTP, MELD and CLIF-C AD scores.

Development of a novel prognostic assessment model for hepatitis B virus-related acute-on-chronic liver failure based on reexamination results

Acute-on-chronic liver failure (ACLF) is a common clinical emergency and critical illness with rapid progression and poor prognosis. This study aims to establish a more efficient system for the prognostic assessment of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF), which will provide a guiding scheme for subsequent treatment and improve the survival rate of patients. Data on 623 patients with HBV-ACLF were recorded. Univariate and multivariate analyses were performed to determine the discriminative abilities of the novel prognostic assessment model in predicting 90-day mortality. The area under the receiver operating characteristic curve was used to evaluate the accuracy of the models. Patients were divided into high- and low-scoring groups based on the best critical values, and survival rates were analyzed using Kaplan-Meier survival analysis and compared by applying log-rank tests. The area under the curve of the new scoring system established using the results of the first reexamination, the results of the first examination, the mean daily change in these results (MDCR) and the results of other first examinations were 0.911 (95% confidence interval [CI]: 0.889, 0.933), 0.893 (95% CI: 0.868, 0.917), and 0.895 (95% CI: 0.871, 0.919), respectively. The final prognostic scoring system established using the results of the first reexamination was chosen as a novel prognostic assessment model, and patients with lower scores (first reexamination results [FRER] score ≤ 3.65) had longer survival times (P < .001). The prognostic scoring system established using the FRER combined with other examination results can better assess the prognosis of HBV-ACLF at 90 days.

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Background

To develop a precise prognostic model of overall survival in patients with terminating cervical cancer based on surveillance, epidemiology, and end results (SEER) program.

Methods

The patients were retrieved from SEER data who are diagnosed with terminating cervical cancer from 2004 to 2016. The data were performed using univariate and multivariate analyses and constructed nomograms for predicting survival. Use C-index to validate the model accuracy.

Results

Totally 15839 patients diagnosed with cervical cancer were independently allocated into the training set (n = 11088) and validation set (n = 4751). The multivariate analysis results indicated that age, race, stage_T, stage_M, and stage_N were confirmed as independent risk predictors, and those factors are applied to construct this clinical model. The C-index of overall survival in the training set was 0.6816 (95% confidence intervene (CI), 0.694-0.763) and that in the validation set was 0.6931(95% CI, 0.613-0.779). All calibration curves of various factors were consistent with predicted and actual survival.

Conclusion

The nomogram provides a novel method for predicting the survival of patients with terminating cervical cancer, assisting in accurate therapeutic methods for patients with primary terminating cervical cancer.

Effect of type 2 diabetic mellitus in the prognosis of acute-on-chronic liver failure patients in China

BACKGROUND

Acute-on-chronic liver failure (ACLF) patients have a high short-term mortality rate, and the severity evaluation of ACLF is necessary for prognostication. Therefore, it was meaningful to evaluate the association between type 2 diabetic mellitus (DM) and ACLF and further explore the feasibility of using DM as a prognostic indicator in ACLF patients. The association between type 2 DM and the prognosis of patients with severe liver disease remains unclear.

AIM

To examine the effect of type 2 DM on the prognosis of patients with ACLF.

METHODS

Clinical data from 222 ACLF patients were collected and analyzed. The patients were categorized into two groups depending on whether they had DM or not, and the clinical data of ACLF patients were measured within 48 h after admission. Complications of ACLF were documented during treatment, such as hepatic encephalopathy, hepatorenal syndrome, acute upper gastrointestinal hemorrhage, and spontaneous peritonitis (SBP). Values of laboratory parameters, complication rates, and hospital mortality rates were compared between two groups.

RESULTS

Among 222 ACLF patients, 38 cases were categorized into DM groups, the mean age was 56.32 years and 73.68% were male. The prognosis of ACLF patients was significantly correlated with DM in univariate [hazard ratio (HR) = 2.4, 95% confidence interval (CI) =1.5-3.7, P < 0.001] and multivariable analysis (HR = 3.17, 95%CI =1.82-5.523, P < 0.001). The incident of SBP (34.21% vs 13.59%, P = 0.038) and other infections like lung, urinary, blood, and cholecyst (44.74% vs 28.26%, P = 0.046) were higher in DM patients than non-DM counterparts. In addition, the ACLF patients with DM tended to have a high mortality rate (P < 0.001). Cumulative survival time was also significantly shorter in the ACLF patients with DM than non-DM.

CONCLUSION

A significant association between DM and the prognosis of ACLF patients was found in China. The ACLF patients with DM had higher incidence of hospital mortality and infection than those without DM.

A Nomogram Predicts Individual Prognosis in Patients With Newly Diagnosed Glioblastoma by Integrating the Extent of Resection of Non-Enhancing Tumors

Background

The extent of resection of non-contrast enhancing tumors (EOR-NCEs) has been shown to be associated with prognosis in patients with newly diagnosed glioblastoma (nGBM). This study aimed to develop and independently validate a nomogram integrated with EOR-NCE to assess individual prognosis.

Methods

Data for this nomogram were based on 301 patients hospitalized for nGBM from October 2011 to April 2019 at the Beijing Tiantan Hospital, Capital Medical University. These patients were randomly divided into derivation (n=181) and validation (n=120) cohorts at a ratio of 6:4. To evaluate predictive accuracy, discriminative ability, and clinical net benefit, concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were calculated for the extent of resection of contrast enhancing tumor (EOR-CE) and EOR-NCE nomograms. Comparison between these two models was performed as well.

Results

The Cox proportional hazards model was used to establish nomograms for this study. Older age at diagnosis, Karnofsky performance status (KPS)<70, unmethylated O⁶-methylguanine-DNA methyltransferase (MGMT) status, wild-type isocitrate dehydrogenase enzyme (IDH), and lower EOR-CE and EOR-NCE were independent factors associated with shorter survival. The EOR-NCE nomogram had a higher C-index than the EOR-CE nomogram. Its calibration curve for the probability of survival exhibited good agreement between the identical and actual probabilities. The EOR-NCE nomogram showed superior net benefits and improved performance over the EOR-CE nomogram with respect to DCA and ROC for survival probability. These results were also confirmed in the validation cohort.

Conclusions

An EOR-NCE nomogram assessing individualized survival probabilities (12-, 18-, and 24-month) for patients with nGBM could be useful to provide patients and their relatives with health care consultations on optimizing therapeutic approaches and prognosis.

A Novel Diagnostic Nomogram for Noninvasive Evaluating Liver Fibrosis in Patients with Chronic Hepatitis B Virus Infection

Objective

To establish a novel nomogram for diagnosing liver fibrosis in patients with chronic hepatitis B virus (HBV) infection and verify the diagnostic performance of the established nomogram.

Methods

Patients with chronic HBV infection who met the inclusion and exclusion criteria were enrolled in this retrospective study; 70% and 30% of patients were randomly assigned to training dataset and validation dataset, respectively. The risk factors for liver fibrosis were screened using the univariate and multivariate logistic regression analyses. Based on the results, a nomogram was established and verified.

Results

508 patients with chronic HBV infection were included in this study (n = 355 for training dataset and n = 153 for validation dataset). The logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT) count, and prothrombin time (PT) were independent risk factors for liver fibrosis (P < 0.01), which were used to establish the nomogram. The consistency index (C-index) of the nomogram established for diagnosing liver fibrosis was 0.875. The calibration line and the ideal line were consistent, which indicated that diagnosis of liver fibrosis by the established model was accurate. The values of area under the receiver operator characteristic (ROC) curve (AUROC) for diagnosing liver fibrosis by the nomogram were 0.857 and 0.862 in the training dataset and validation dataset, respectively, which were noticeably higher than those in the well-known serological models, including the aspartate aminotransferase- (AST-) to-platelet ratio index (APRI) scoring model, fibrosis-4 (FIB-4) scoring model, APAG model (including age, PT, albumin, and γ-glutamyl transferase), and S-index model (all P < 0.05).

Conclusion

LSM, PT, and PLT were found as independent risk factors for liver fibrosis. The established nomogram exhibited an excellent diagnostic performance, and it can more visually and individually evaluate the probability of liver fibrosis in patients with chronic HBV infection.