T C-F Yip1,2, A J Ma3, V W-S Wong1,2, Y-K Tse1,2, H L-Y Chan1,2, P-C Yuen3, G L-H Wong1,2. 1. Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong. 2. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong. 3. Department of Computer Science, Hong Kong Baptist University, Hong Kong.
Abstract
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiological studies, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. AIM: To develop and validate a laboratory parameter-based machine learning model to detect NAFLD for the general population. METHODS: We randomly divided 922 subjects from a population screening study into training and validation groups; NAFLD was diagnosed by proton-magnetic resonance spectroscopy. On the basis of machine learning from 23 routine clinical and laboratory parameters after elastic net regulation, we evaluated the logistic regression, ridge regression, AdaBoost and decision tree models. The areas under receiver-operating characteristic curve (AUROC) of models in validation group were compared. RESULTS: Six predictors including alanine aminotransferase, high-density lipoprotein cholesterol, triglyceride, haemoglobinA1c , white blood cell count and the presence of hypertension were selected. The NAFLD ridge score achieved AUROC of 0.87 (95% CI 0.83-0.90) and 0.88 (0.84-0.91) in the training and validation groups respectively. Using dual cut-offs of 0.24 and 0.44, NAFLD ridge score achieved 92% (86%-96%) sensitivity and 90% (86%-93%) specificity with corresponding negative and positive predictive values of 96% (91%-98%) and 69% (59%-78%), and 87% of overall accuracy among 70% of classifiable subjects in the validation group; 30% of subjects remained indeterminate. CONCLUSIONS:NAFLD ridge score is a simple and robust reference comparable to existing NAFLD scores to exclude NAFLD patients in epidemiological studies.
RCT Entities:
BACKGROUND:Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiological studies, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. AIM: To develop and validate a laboratory parameter-based machine learning model to detect NAFLD for the general population. METHODS: We randomly divided 922 subjects from a population screening study into training and validation groups; NAFLD was diagnosed by proton-magnetic resonance spectroscopy. On the basis of machine learning from 23 routine clinical and laboratory parameters after elastic net regulation, we evaluated the logistic regression, ridge regression, AdaBoost and decision tree models. The areas under receiver-operating characteristic curve (AUROC) of models in validation group were compared. RESULTS: Six predictors including alanine aminotransferase, high-density lipoprotein cholesterol, triglyceride, haemoglobin A1c , white blood cell count and the presence of hypertension were selected. The NAFLD ridge score achieved AUROC of 0.87 (95% CI 0.83-0.90) and 0.88 (0.84-0.91) in the training and validation groups respectively. Using dual cut-offs of 0.24 and 0.44, NAFLD ridge score achieved 92% (86%-96%) sensitivity and 90% (86%-93%) specificity with corresponding negative and positive predictive values of 96% (91%-98%) and 69% (59%-78%), and 87% of overall accuracy among 70% of classifiable subjects in the validation group; 30% of subjects remained indeterminate. CONCLUSIONS: NAFLD ridge score is a simple and robust reference comparable to existing NAFLD scores to exclude NAFLD patients in epidemiological studies.
Authors: Paolo Sorino; Angelo Campanella; Caterina Bonfiglio; Antonella Mirizzi; Isabella Franco; Antonella Bianco; Maria Gabriella Caruso; Giovanni Misciagna; Laura R Aballay; Claudia Buongiorno; Rosalba Liuzzi; Anna Maria Cisternino; Maria Notarnicola; Marisa Chiloiro; Francesca Fallucchi; Giovanni Pascoschi; Alberto Rubén Osella Journal: Sci Rep Date: 2021-10-12 Impact factor: 4.379
Authors: Yonghui Wu; Xi Yang; Heather L Morris; Matthew J Gurka; Elizabeth A Shenkman; Kenneth Cusi; Fernando Bril; William T Donahoo Journal: JMIR Med Inform Date: 2022-06-06
Authors: Mohammad A Karim; Amit G Singal; Hye Chung Kum; Yi-Te Lee; Sulki Park; Nicole E Rich; Mazen Noureddin; Ju Dong Yang Journal: Clin Gastroenterol Hepatol Date: 2022-03-17 Impact factor: 13.576
Authors: Luis A Rodriguez; Stephen C Shiboski; Patrick T Bradshaw; Alicia Fernandez; David Herrington; Jingzhong Ding; Ryan D Bradley; Alka M Kanaya Journal: J Gen Intern Med Date: 2021-01-26 Impact factor: 6.473