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Drozdov I, Szubert B, Rowe IA, Kendall TJ, Fallowfield JA. Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records. Ann Hepatol 2024; 29:101528. [PMID: 38971372 DOI: 10.1016/j.aohep.2024.101528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 06/13/2024] [Indexed: 07/08/2024]
Abstract
INTRODUCTION AND OBJECTIVES Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model. PATIENTS AND METHODS n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n = 528 MASLD patients. RESULTS In-sample model performance achieved AUROC curve 0.74-0.90 (95 % CI: 0.72-0.94), sensitivity 64 %-82 %, specificity 75 %-92 % and Positive Predictive Value (PPV) 94 %-98 %. Out-of-sample model validation had AUROC 0.70-0.86 (95 % CI: 0.67-0.90), sensitivity 69 %-70 %, specificity 96 %-97 % and PPV 75 %-77 %. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days. CONCLUSIONS A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.
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Affiliation(s)
| | | | - Ian A Rowe
- Leeds Institute of Medical Research, University of Leeds, UK; Leeds Liver Unit, St James's University Hospital, Leeds Teaching Hospitals, UK
| | - Timothy J Kendall
- Edinburgh Pathology, University of Edinburgh, Edinburgh, UK; Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Jonathan A Fallowfield
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK.
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Alizargar A, Chang YL, Alkhaleefah M, Tan TH. Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection. Bioengineering (Basel) 2024; 11:600. [PMID: 38927836 PMCID: PMC11201081 DOI: 10.3390/bioengineering11060600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/23/2024] [Accepted: 06/06/2024] [Indexed: 06/28/2024] Open
Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) is characterized by the accumulation of excess fat in the liver. If left undiagnosed and untreated during the early stages, NAFLD can progress to more severe conditions such as inflammation, liver fibrosis, cirrhosis, and even liver failure. In this study, machine learning techniques were employed to predict NAFLD using affordable and accessible laboratory test data, while the conventional technique hepatic steatosis index (HSI)was calculated for comparison. Six algorithms (random forest, K-nearest Neighbors, Logistic Regression, Support Vector Machine, extreme gradient boosting, decision tree), along with an ensemble model, were utilized for dataset analysis. The objective was to develop a cost-effective tool for enabling early diagnosis, leading to better management of the condition. The issue of imbalanced data was addressed using the Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN). Various evaluation metrics including the F1 score, precision, accuracy, recall, confusion matrix, the mean absolute error (MAE), receiver operating characteristics (ROC), and area under the curve (AUC) were employed to assess the suitability of each technique for disease prediction. Experimental results using the National Health and Nutrition Examination Survey (NHANES) dataset demonstrated that the ensemble model achieved the highest accuracy (0.99) and AUC (1.00) compared to the machine learning techniques that we used and HSI. These findings indicate that the ensemble model holds potential as a beneficial tool for healthcare professionals to predict NAFLD, leveraging accessible and cost-effective laboratory test data.
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Affiliation(s)
- Azadeh Alizargar
- Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan; (A.A.); (Y.-L.C.); (M.A.)
| | - Yang-Lang Chang
- Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan; (A.A.); (Y.-L.C.); (M.A.)
| | - Mohammad Alkhaleefah
- Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan; (A.A.); (Y.-L.C.); (M.A.)
| | - Tan-Hsu Tan
- Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan; (A.A.); (Y.-L.C.); (M.A.)
- Innovation Frontier Institute of Research for Science and Technology, National Taipei University of Technology, Taipei 10608, Taiwan
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Tudor MS, Gheorman V, Simeanu GM, Dobrinescu A, Pădureanu V, Dinescu VC, Forțofoiu MC. Evolutive Models, Algorithms and Predictive Parameters for the Progression of Hepatic Steatosis. Metabolites 2024; 14:198. [PMID: 38668326 PMCID: PMC11052048 DOI: 10.3390/metabo14040198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024] Open
Abstract
The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition points, they promise to revolutionize our approach to understanding and managing hepatic steatosis progression, ultimately leading to enhanced patient care and outcomes in clinical settings. This paradigm shift towards a more dynamic, personalized, and comprehensive approach to hepatic steatosis progression signifies a significant advancement in healthcare. The application of evolutive models and algorithms allows for a nuanced characterization of disease trajectories, facilitating tailored interventions and optimizing clinical decision-making. Furthermore, these computational tools offer a framework for integrating diverse data sources, creating a more holistic understanding of hepatic steatosis progression. In summary, the potential benefits encompass the ability to analyze complex datasets, capture temporal dynamics, provide personalized prognostic insights, optimize intervention planning, identify critical transition points, and integrate diverse data sources. The application of evolutive models and algorithms has the potential to revolutionize our understanding and management of hepatic steatosis, ultimately leading to improved patient outcomes in clinical settings.
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Affiliation(s)
- Marinela Sînziana Tudor
- Doctoral School, University of Medicine and Pharmacy of Craiova, Petru Rareș 2 Str, 200349 Craiova, Romania; (M.S.T.); (G.-M.S.)
| | - Veronica Gheorman
- Department 3 Medical Semiology, University of Medicine and Pharmacy of Craiova, Petru Rareș 2 Str, 200349 Craiova, Romania;
| | - Georgiana-Mihaela Simeanu
- Doctoral School, University of Medicine and Pharmacy of Craiova, Petru Rareș 2 Str, 200349 Craiova, Romania; (M.S.T.); (G.-M.S.)
| | - Adrian Dobrinescu
- Department of Thoracic Surgery, University of Medicine and Pharmacy of Craiova, Petru Rareș 2 Str, 200349 Craiova, Romania
| | - Vlad Pădureanu
- Department 3 Medical Semiology, University of Medicine and Pharmacy of Craiova, Petru Rareș 2 Str, 200349 Craiova, Romania;
| | - Venera Cristina Dinescu
- Department of Health Promotion and Occupational Medicine, University of Medicine and Pharmacy of Craiova, Petru Rareș 2 Str, 200349 Craiova, Romania;
| | - Mircea-Cătălin Forțofoiu
- Department 3 Medical Semiology, University of Medicine and Pharmacy of Craiova, Clinical Municipal Hospital “Philanthropy” of Craiova, 200143 Craiova, Romania;
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Liu L, Lin J, Liu L, Gao J, Xu G, Yin M, Liu X, Wu A, Zhu J. Automated machine learning models for nonalcoholic fatty liver disease assessed by controlled attenuation parameter from the NHANES 2017-2020. Digit Health 2024; 10:20552076241272535. [PMID: 39119551 PMCID: PMC11307367 DOI: 10.1177/20552076241272535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
Background Nonalcoholic fatty liver disease (NAFLD) is recognized as one of the most common chronic liver diseases worldwide. This study aims to assess the efficacy of automated machine learning (AutoML) in the identification of NAFLD using a population-based cross-sectional database. Methods All data, including laboratory examinations, anthropometric measurements, and demographic variables, were obtained from the National Health and Nutrition Examination Survey (NHANES). NAFLD was defined by controlled attenuation parameter (CAP) in liver transient ultrasound elastography. The least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection. Six algorithms were utilized on the H2O-automated machine learning platform: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost), and Deep Learning (DL). These algorithms were selected for their diverse strengths, including their ability to handle complex, non-linear relationships, provide high predictive accuracy, and ensure interpretability. The models were evaluated by area under receiver operating characteristic curves (AUC) and interpreted by the calibration curve, the decision curve analysis, variable importance plot, SHapley Additive exPlanation plot, partial dependence plots, and local interpretable model agnostic explanation plot. Results A total of 4177 participants (non-NAFLD 3167 vs NAFLD 1010) were included to develop and validate the AutoML models. The model developed by XGBoost performed better than other models in AutoML, achieving an AUC of 0.859, an accuracy of 0.795, a sensitivity of 0.773, and a specificity of 0.802 on the validation set. Conclusions We developed an XGBoost model to better evaluate the presence of NAFLD. Based on the XGBoost model, we created an R Shiny web-based application named Shiny NAFLD (http://39.101.122.171:3838/App2/). This application demonstrates the potential of AutoML in clinical research and practice, offering a promising tool for the real-world identification of NAFLD.
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Affiliation(s)
- Lihe Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guoting Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Minyue Yin
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Airong Wu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Mamandipoor B, Wernly S, Semmler G, Flamm M, Jung C, Aigner E, Datz C, Wernly B, Osmani V. Machine learning models predict liver steatosis but not liver fibrosis in a prospective cohort study. Clin Res Hepatol Gastroenterol 2023; 47:102181. [PMID: 37467893 DOI: 10.1016/j.clinre.2023.102181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 06/24/2023] [Accepted: 07/17/2023] [Indexed: 07/21/2023]
Abstract
INTRODUCTION Screening for liver fibrosis continues to rely on laboratory panels and non-invasive tests such as FIB-4-score and transient elastography. In this study, we evaluated the potential of machine learning (ML) methods to predict liver steatosis on abdominal ultrasound and liver fibrosis, namely the intermediate-high risk of advanced fibrosis, in individuals participating in a screening program for colorectal cancer. METHODS We performed ultrasound on 5834 patients admitted between 2006 and 2020, and transient elastography on a subset of 1240 patients. Steatosis on ultrasound was diagnosed if liver areas showed a significantly increased echogenicity compared to the renal parenchyma. Liver fibrosis was defined as a liver stiffness measurement ≥8 kPa in transient elastography. We evaluated the performance of three algorithms, namely Extreme Gradient Boosting, Feed-Forward neural network and Logistic Regression, deriving the models using data from patients admitted from January 2007 up to January 2016 and prospectively evaluating on the data of patients admitted from January 2016 up to March 2020. We also performed a performance comparison with the standard clinical test based on Fibrosis-4 Index (FIB-4). RESULTS The mean age was 58±9 years with 3036 males (52%). Modelling laboratory parameters, clinical parameters, and data on eight food types/dietary patterns, we achieved high performance in predicting liver steatosis on ultrasound with AUC of 0.87 (95% CI [0.87-0.87]), and moderate performance in predicting liver fibrosis with AUC of 0.75 (95% CI [0.74-0.75]) using XGBoost machine learning algorithm. Patient-reported variables did not significantly improve predictive performance. Gender-specific analyses showed significantly higher performance in males with AUC of 0.74 (95% CI [0.73-0.74]) in comparison to female patients with AUC of 0.66 (95% CI [0.65-0.66]) in prediction of liver fibrosis. This difference was significantly smaller in prediction of steatosis with AUC of 0.85 (95% CI [0.83-0.87]) in female patients, in comparison to male patients with AUC of 0.82 (95% CI [0.80-0.84]). CONCLUSION ML based on point-prevalence laboratory and clinical information predicts liver steatosis with high accuracy and liver fibrosis with moderate accuracy. The observed gender differences suggest the need to develop gender-specific models.
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Affiliation(s)
| | - Sarah Wernly
- Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University, Salzburg, Austria
| | - Georg Semmler
- Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Vienna, Austria
| | - Maria Flamm
- Institute of general practice, family medicine and preventive medicine, Paracelsus Medical University, Salzburg, Austria
| | - Christian Jung
- Department of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, Germany
| | - Elmar Aigner
- Clinic I for Internal Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Christian Datz
- Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University, Salzburg, Austria
| | - Bernhard Wernly
- Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University, Salzburg, Austria; Institute of general practice, family medicine and preventive medicine, Paracelsus Medical University, Salzburg, Austria
| | - Venet Osmani
- Information School, University of Sheffield, United Kingdom.
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Deng Y, Ma Y, Fu J, Wang X, Yu C, Lv J, Man S, Wang B, Li L. A dynamic machine learning model for prediction of NAFLD in a health checkup population: A longitudinal study. Heliyon 2023; 9:e18758. [PMID: 37576311 PMCID: PMC10412833 DOI: 10.1016/j.heliyon.2023.e18758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/15/2023] Open
Abstract
Background Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide. Currently, most NAFLD prediction models are diagnostic models based on cross-sectional data, which failed to provide early identification or clarify causal relationships. We aimed to use time-series deep learning models with longitudinal health checkup records to predict the onset of NAFLD in the future, and update the model stepwise by incorporating new checkup records to achieve dynamic prediction. Methods 10,493 participants with over 6 health checkup records from Beijing MJ Health Screening Center were included to conduct a retrospective cohort study, in which the constantly updated initial 5 checkup data were incorporated stepwise to predict the risk of NAFLD at and after their sixth health checkups. A total of 33 variables were considered, consisting of demographic characteristics, medical history, lifestyle, physical examinations, and laboratory tests. L1-penalized logistic regression (LR) was used for feature selection. The long short-term memory (LSTM) algorithm was introduced for model development, and five-fold cross-validation was conducted to tune and choose optimal hyperparameters. Both internal validation and external validation were conducted, using the 20% randomly divided holdout test dataset and previously unseen data from Shanghai MJ Health Screening Center, respectively, to evaluate model performance. The evaluation metrics included area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, Brier score, and decision curve. Bootstrap sampling was implemented to generate 95% confidence intervals of all the metrics. Finally, the Shapley additive explanations (SHAP) algorithm was applied in the holdout test dataset for model interpretability to obtain time-specific and sample-specific contributions of each feature. Results Among the 10,493 participants, 1662 (15.84%) were diagnosed with NAFLD at and after their sixth health checkups. The predictive performance of the deep learning model in the internal validation dataset improved over the incorporation of the checkups, with AUROC increasing from 0.729 (95% CI: 0.698,0.760) at baseline to 0.818 (95% CI: 0.798,0.844) when consecutive 5 checkups were included. The external validation dataset, containing 1728 participants, was used to verify the results, in which AUROC increased from 0.700 (95% CI: 0.657,0.740) with only the first checkups to 0.792 (95% CI: 0.758,0.825) with all five. The results of feature significance showed that body fat percentage, alanine transaminase (ALT), and uric acid owned the greatest impact on the outcome, time-specific, individual-specific and dynamic feature contributions were also produced for model interpretability. Conclusion A dynamic prediction model was successfully established in our study, and the prediction capability kept improving with the renewal of the latest checkup records. In addition, we identified key features associated with the onset of NAFLD, making it possible to optimize the prevention and control strategies of the disease in the general population.
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Affiliation(s)
- Yuhan Deng
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
- Meinian Institute of Health, Beijing, China
| | - Yuan Ma
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jingzhu Fu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Health Science Center Meinian Public Health Institute, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | | | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Health Science Center Meinian Public Health Institute, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Health Science Center Meinian Public Health Institute, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Sailimai Man
- Meinian Institute of Health, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Health Science Center Meinian Public Health Institute, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Bo Wang
- Meinian Institute of Health, Beijing, China
- Peking University Health Science Center Meinian Public Health Institute, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Health Science Center Meinian Public Health Institute, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
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Schreiner AD, Sattar N. Identifying Patients with Nonalcoholic Fatty Liver Disease in Primary Care: How and for What Benefit? J Clin Med 2023; 12:4001. [PMID: 37373694 DOI: 10.3390/jcm12124001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Despite its increasing prevalence, nonalcoholic fatty liver disease (NAFLD) remains under-diagnosed in primary care. Timely diagnosis is critical, as NAFLD can progress to nonalcoholic steatohepatitis, fibrosis, cirrhosis, hepatocellular carcinoma, and death; furthermore, NAFLD is also a risk factor linked to cardiometabolic outcomes. Identifying patients with NAFLD, and particularly those at risk of advanced fibrosis, is important so that healthcare practitioners can optimize care delivery in an effort to prevent disease progression. This review debates the practical issues that primary care physicians encounter when managing NAFLD, using a patient case study to illustrate the challenges and decisions that physicians face. It explores the pros and cons of different diagnostic strategies and tools that physicians can adopt in primary care settings, depending on how NAFLD presents and progresses. We discuss the importance of prescribing lifestyle changes to achieve weight loss and mitigate disease progression. A diagnostic and management flow chart is provided, showing the key points of assessment for primary care physicians. The advantages and disadvantages of advanced fibrosis risk assessments in primary care settings and the factors that influence patient referral to a hepatologist are also reviewed.
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Affiliation(s)
- Andrew D Schreiner
- Department of Medicine, Medical University of South Carolina, 171 Ashley Ave, Charleston, SC 29425, USA
| | - Naveed Sattar
- Institute of Cardiovascular & Medical Sciences, University of Glasgow, 126 University Place, Glasgow G12 8TA, UK
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Zhang CY, Liu S, Yang M. Antioxidant and anti-inflammatory agents in chronic liver diseases: Molecular mechanisms and therapy. World J Hepatol 2023; 15:180-200. [PMID: 36926234 PMCID: PMC10011909 DOI: 10.4254/wjh.v15.i2.180] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 11/30/2022] [Accepted: 02/09/2023] [Indexed: 02/24/2023] Open
Abstract
Chronic liver disease (CLD) is a continuous process that causes a reduction of liver function lasting more than six months. CLD includes alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), chronic viral infection, and autoimmune hepatitis, which can lead to liver fibrosis, cirrhosis, and cancer. Liver inflammation and oxidative stress are commonly associated with the development and progression of CLD. Molecular signaling pathways such as AMP-activated protein kinase (AMPK), C-Jun N-terminal kinase, and peroxisome proliferator-activated receptors (PPARs) are implicated in the pathogenesis of CLD. Therefore, antioxidant and anti-inflammatory agents from natural products are new potent therapies for ALD, NAFLD, and hepatocellular carcinoma (HCC). In this review, we summarize some powerful products that can be potential applied in all the stages of CLD, from ALD/NAFLD to HCC. The selected agents such as β-sitosterol, curcumin, genistein, and silymarin can regulate the activation of several important molecules, including AMPK, Farnesoid X receptor, nuclear factor erythroid 2-related factor-2, PPARs, phosphatidylinositol-3-kinase, and lysyl oxidase-like proteins. In addition, clinical trials are undergoing to evaluate their efficacy and safety.
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Affiliation(s)
- Chun-Ye Zhang
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, United States
| | - Shuai Liu
- The First Affiliated Hospital, Zhejiang University, Hangzhou 310006, Zhejiang Province, China
| | - Ming Yang
- Department of Surgery, University of Missouri, Columbia, MO 65211, United States
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Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort. Int J Med Inform 2023; 170:104932. [PMID: 36459836 DOI: 10.1016/j.ijmedinf.2022.104932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population. MATERIALS AND METHODS Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population). RESULTS For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs. CONCLUSIONS We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in the future, it might be implemented and potentially applied to program broad screenings for liver fibrosis.
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Han K, Tan K, Shen J, Gu Y, Wang Z, He J, Kang L, Sun W, Gao L, Gao Y. Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES. Front Public Health 2022; 10:1008794. [PMID: 36211651 PMCID: PMC9537573 DOI: 10.3389/fpubh.2022.1008794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/05/2022] [Indexed: 01/27/2023] Open
Abstract
Background Prevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential. Objective An XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM. Methods All data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort. Results A total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa]. Conclusions XGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.
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Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population. J Pers Med 2022; 12:jpm12071026. [PMID: 35887527 PMCID: PMC9317783 DOI: 10.3390/jpm12071026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/18/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.
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