A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis
Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screen...
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Gastroenterology Council for Gut and Liver
2025-01-01
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| Series: | Gut and Liver |
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| Online Access: | http://gutnliver.org/journal/view.html?doi=10.5009/gnl240367 |
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| author | Jeayeon Park Goh Eun Chung Yoosoo Chang So Eun Kim Won Sohn Seungho Ryu Yunmi Ko Youngsu Park Moon Haeng Hur Yun Bin Lee Eun Ju Cho Jeong-Hoon Lee Su Jong Yu Jung-Hwan Yoon Yoon Jun Kim |
| author_facet | Jeayeon Park Goh Eun Chung Yoosoo Chang So Eun Kim Won Sohn Seungho Ryu Yunmi Ko Youngsu Park Moon Haeng Hur Yun Bin Lee Eun Ju Cho Jeong-Hoon Lee Su Jong Yu Jung-Hwan Yoon Yoon Jun Kim |
| author_sort | Jeayeon Park |
| collection | DOAJ |
| description | Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions: As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD. |
| format | Article |
| id | doaj-art-ba8c23deb7f1451c9431f2d15545714b |
| institution | Kabale University |
| issn | 1976-2283 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Gastroenterology Council for Gut and Liver |
| record_format | Article |
| series | Gut and Liver |
| spelling | doaj-art-ba8c23deb7f1451c9431f2d15545714b2025-01-15T00:51:13ZengGastroenterology Council for Gut and LiverGut and Liver1976-22832025-01-0119112613510.5009/gnl240367gnl240367A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity CrisisJeayeon Park0Goh Eun Chung1Yoosoo Chang2So Eun Kim3Won Sohn4Seungho Ryu5Yunmi Ko6Youngsu Park7Moon Haeng Hur8Yun Bin Lee9Eun Ju Cho10Jeong-Hoon Lee11Su Jong Yu12Jung-Hwan Yoon13Yoon Jun Kim14Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, KoreaCenter for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, KoreaDepartment of Statistics, Sungkyunkwan University, Seoul, KoreaDivision of Gastroenterology and Hepatology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, KoreaCenter for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, KoreaBackground/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions: As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.http://gutnliver.org/journal/view.html?doi=10.5009/gnl240367machine learning; fatty liver; obesity; bioelectrical impedance |
| spellingShingle | Jeayeon Park Goh Eun Chung Yoosoo Chang So Eun Kim Won Sohn Seungho Ryu Yunmi Ko Youngsu Park Moon Haeng Hur Yun Bin Lee Eun Ju Cho Jeong-Hoon Lee Su Jong Yu Jung-Hwan Yoon Yoon Jun Kim A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis Gut and Liver machine learning; fatty liver; obesity; bioelectrical impedance |
| title | A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis |
| title_full | A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis |
| title_fullStr | A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis |
| title_full_unstemmed | A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis |
| title_short | A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis |
| title_sort | novel point of care prediction model for steatotic liver disease expected role of mass screening in the global obesity crisis |
| topic | machine learning; fatty liver; obesity; bioelectrical impedance |
| url | http://gutnliver.org/journal/view.html?doi=10.5009/gnl240367 |
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