Dataset Information

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study.

ABSTRACT:

Background and aim

This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.

Methods

This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups' demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results

Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram's area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively.

Conclusion

APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD.

SUBMITTER: Zhang F

PROVIDER: S-EPMC10997367 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature

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Publications

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study.

Zhang Fan F Han Yan Y Mao Yonghua Y Zheng Guojun G Liu Longgen L Li Wenjian W

Annals of medicine 20240404 1

<h4>Background and aim</h4>This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.<h4>Methods</h4>This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups' demo ...[more]

PMID: 38574396

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Project description:AbstractPatients with metabolic syndrome are at a higher risk of nonalcoholic fatty liver disease (NAFLD) and liver fibrosis than the general population. Still, accessibility of screening method for NAFLD with significant fibrosis, such as transient elastography (FibroScan) are limited in some settings. This study aimed to develop a simple clinical predictive score for detecting NAFLD with significant fibrosis in patients with metabolic syndrome.A cross-sectional study was designed to obtain the data from medical records of all relevant patients who underwent transient elastography between January 2011 and December 2020 at Siriraj Hospital, Thailand. A liver stiffness cutoff value of 7.0 kilopascal was used to define the presence of significant liver fibrosis. To examine potential predictors, medical history and clinical data commonly assessed in routine practice were selected by following expert opinions and univariable statistical analysis. Backward and forward stepwise logistic regression was performed to acquire a final prediction model. To simplify the model, a weighted score was assigned for each categorized predictor. In addition, eligible cutoff values of the score and their predictive performances were determined.A total of 745 medical records were reviewed. The prevalence of NAFLD with significant fibrosis in patients with metabolic syndrome was 12.6%. Most clinical characteristics of patients with NAFLD with significant fibrosis and those non-NAFLD and NAFLD with no/mild fibrosis were quite disparate. The most practical model comprised globulin, aspartate transaminase, platelet count, and type 2 diabetes. It provided a good predictive performance with an area under the receiver operating characteristic curve of 0.828 (95% confidence interval [CI]: 0.782, 0.874). At the proper cutoff value, sensitivity and specificity were 76.6% (95% CI: 66.7%, 84.7%) and 72.4% (95% CI: 68.7%, 75.8%), respectively. The likelihood ratio of testing positive for NAFLD with significant fibrosis was 2.8 (95% CI: 2.34, 3.27) among patients with scores above the cutoff value.The first score for detecting of NAFLD with significant fibrosis in patients with metabolic syndrome was developed. This practical score, providing a good predictive performance, should be useful to help clinicians prioritize needs for further investigations among high-risk patients, especially in resource-limited settings.

Project description:Recently, an expert panel proposed diagnostic criteria for metabolic dysfunction-associated fatty liver disease (MAFLD) in the pediatric population. The aim of this study was to evaluate the prevalence of MAFLD among US adolescents and to investigate whether the new MAFLD definition is able to identify individuals with more advanced liver disease. We analyzed data from participants 12-18 years old included in the 2017-2020 cycles of the National Health and Nutrition Examination Survey, a large survey aimed at including individuals representative of the non-institutionalized general US population. Participants with a complete vibration-controlled transient elastography exam were included. Steatosis was evaluated through the median controlled attenuation parameter (CAP) and fibrosis through median liver stiffness measurement (LSM). Recently proposed criteria for the diagnosis of MAFLD were applied. Multivariable logistic regression analysis was performed to evaluate the impact of the new MAFLD definition on the odds of significant liver fibrosis. We included a total of 1446 adolescents (mean age: 14.9 years; 52.0% male; 47.3% overweight or obese). No participant reported a previous history of viral hepatitis. Steatosis (CAP ≥ 248 dB/m) was present in 25.9% (95% confidence interval [CI] 23.3-28.9) of individuals, and among these, 87.7% met the MAFLD criteria. Only 22.9% of patients with steatosis had elevated alanine aminotransferase levels. Among participants with steatosis, prevalence of significant liver fibrosis (LSM ≥ 7.4 kPa) did not differ significantly according to whether they met MAFLD criteria (9.7% vs. 15.2%, p = 0.276). In the multivariable model, odds of significant fibrosis did not differ significantly between these two groups. MAFLD criteria are met by most US adolescents with elastographic evidence of steatosis. Nonetheless, these criteria do not appear to improve detection of subjects with more advanced liver disease. Further longitudinal studies are needed to evaluate whether metabolic dysfunction is associated with faster progression toward inflammation, fibrosis, and liver-related events.

Project description:Background and aims: Non-invasive tests (NITs) are needed in clinical practice to replace histology for the identification of liver fibrosis and prognostication in Non-Alcoholic Fatty Liver Disease (NAFLD). Novel collagen-derived fibrogenesis markers including N-terminal type III collagen pro-peptide (PRO-C3) are among the most promising tools in this field. The aim of this study was to assess the diagnostic accuracy of PRO-C3, the derivative ADAPT score, and other NITs for the identification of advanced fibrosis (stages 3-4) and changes over 12 months of follow-up. Methods: In this longitudinal study, 96 patients with biopsy-proven NAFLD were evaluated at baseline, of which 50 underwent a follow-up visit after 12 months. Clinical-biochemical parameters, liver stiffness (LS) by transient elastography, PRO-C3, and other NITs (ADAPT, FIB-4, NFS, APRI) were collected at baseline and follow-up. Results: LS showed the best accuracy for the identification of advanced fibrosis, with Area under the Receiving Operator Curve (AUROC) 0.82 (0.73-0.89) for a cut-off value of 9.4 kPa. Among the other NITs, the ADAPT score showed the best accuracy, with AUROC 0.80 (0.71-0.88) for a cut-off of 5.02 (Se 62%, Sp 89%, PPV 74%, NPV 83%). The comparison between the AUROC of LS with that of ADAPT was not statistically different (DeLong test p value 0.348). At follow-up, LS was slightly reduced, whilst PRO-C3 displayed a significant increase from baseline median 11.2 ng/mL to 13.9 ng/mL at follow-up (p = 0.017). Accordingly, ADAPT score increased from median 5.3 to 6.1 (p = 0.019). The other NITs did not significantly change over 12 months. Conclusions: The ADAPT score shows the best performance among non-invasive scores for the identification of advanced fibrosis, not different from LS. Collagen-derived biomarker PRO-C3 and the derivative score ADAPT display significant changes over time, and may be useful tools for monitoring the progression of liver disease or assessing responses to treatments.

Project description:ObjectiveMetabolic - associated fatty liver disease (MAFLD) is a common hepatic disorder with increasing prevalence, and early detection remains inadequately achieved. This study aims to explore the relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and MAFLD, and to establish a predictive model for MAFLD using NHHR as a key variable.MethodsAll participants were selected from the NHANES cohort, spanning from 2017 to March 2020. Multiple linear regression models were employed to examine the relationship between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and the controlled attenuation parameter (CAP). To explore the non-linear association between NHHR and CAP, smooth curve fitting and restricted cubic splines (RCS) of the adjusted variables were utilized. Subgroup analyses were conducted to identify variations in the relationships between the independent and dependent variables across different populations. Finally, a metabolic - associated fatty liver disease (MAFLD) prediction model was developed using seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and logistic regression. The SHAP (SHapley Additive exPlanations) value was employed to interpret the importance of various features.ResultWeighted multiple linear regression models revealed a significant positive correlation between the NHHR and the CAP (Beta = 7.42, 95% CI: 5.35-9.50, P < 0.001). Smooth curve fitting and RCS demonstrated a non-linear relationship between NHHR and CAP. Subgroup analyses indicated that this relationship was more pronounced in females. Among the seven machine learning predictive models incorporating NHHR, the XGBoost algorithm exhibited the highest predictive performance, with an area under the curve (AUC) of 0.828. Furthermore, NHHR was identified as the second most important feature in the SHAP analysis, following body mass index (BMI), highlighting its potential in predicting MAFLD.ConclusionA significant positive correlation was identified between the NHHR and the CAP. The inclusion of NHHR in the XGBoost predictive model for MAFLD demonstrated robust predictive capability, providing a valuable tool for the early detection of MAFLD with considerable clinical application potential.

Dataset Information

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study.

Background and aim

Methods

Results

Conclusion

Publications

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study.

Similar Datasets

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