Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States
BackgroundLiver fibrosis is closely related to abnormal liver function and liver cancer. Accurate noninvasive assessment of liver fibrosis is of great significance for preventing disease progression and treatment decisions. The purpose of this study was to develop and validate a non-invasive predict...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1207365/full |
| _version_ | 1797692565339766784 |
|---|---|
| author | Yuanhui Guo Baixuan Shen Yanli Xue Ying Li |
| author_facet | Yuanhui Guo Baixuan Shen Yanli Xue Ying Li |
| author_sort | Yuanhui Guo |
| collection | DOAJ |
| description | BackgroundLiver fibrosis is closely related to abnormal liver function and liver cancer. Accurate noninvasive assessment of liver fibrosis is of great significance for preventing disease progression and treatment decisions. The purpose of this study was to develop and validate a non-invasive predictive model for the asses`sment of significant fibrosis in patients with non-alcoholic fatty liver disease.MethodsInformation on all participants for 2017-2018 was extracted from the NHANES database. The eligible patients with significant fibrosis (n=123) and non-significant fibrosis (n=898) were selected to form the original dataset. Variable selection was performed using least absolute shrinkage and selection operator (Lasso) regression, and multivariate logistic regression analysis was used to develop a prediction model. The utility of the model is assessed in terms of its discrimination, calibration and clinical usability. Bootstrap-resampling internal validation was used to measure the accuracy of the prediction model.ResultsThis study established a new model consisting of 9 common clinical indicators and developed an online calculator to show the model. Compared with the previously proposed liver fibrosis scoring system, this model showed the best discrimination and predictive performance in the training cohort (0.812,95%CI 0.769-0.855) and the validation cohort (0.805,95%CI 0.762-0.847), with the highest area under curve. Specificity(0.823), sensitivity(0.699), positive likelihood ratio(3.949) and negative likelihood ratio(0.366) were equally excellent. The calibration plot of the predicted probability and the actual occurrence probability of significant fibrosis shows excellent consistency, indicating that the model calibration is outstanding. Combined with decision curve analysis, this model has a great benefit in the range of 0.1-0.8 threshold probability, and has a good application value for the diagnosis of clinical significant fibrosis.ConclusionThis study proposes a new non-invasive diagnostic model that combines clinical indicators to provide an accurate and convenient individualized diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease. |
| first_indexed | 2024-03-12T02:30:26Z |
| format | Article |
| id | doaj.art-ec11de60264f484f9a1e393430c26a7b |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-12T02:30:26Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-ec11de60264f484f9a1e393430c26a7b2023-09-05T09:14:11ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-09-011410.3389/fendo.2023.12073651207365Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United StatesYuanhui Guo0Baixuan Shen1Yanli Xue2Ying Li3Endocrine and Metabolic Disease Center, Medical Key Laboratory of Hereditary Rare Diseases of Henan, Luoyang Sub-center of National Clinical Research Center for Metabolic Diseases, First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaEndocrine and Metabolic Disease Center, Medical Key Laboratory of Hereditary Rare Diseases of Henan, Luoyang Sub-center of National Clinical Research Center for Metabolic Diseases, First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaDepartment of Pharmacy, First Affiliated Hospital of Henan University of Science and Technology, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaDepartment of Pharmacy, First Affiliated Hospital of Henan University of Science and Technology, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, ChinaBackgroundLiver fibrosis is closely related to abnormal liver function and liver cancer. Accurate noninvasive assessment of liver fibrosis is of great significance for preventing disease progression and treatment decisions. The purpose of this study was to develop and validate a non-invasive predictive model for the asses`sment of significant fibrosis in patients with non-alcoholic fatty liver disease.MethodsInformation on all participants for 2017-2018 was extracted from the NHANES database. The eligible patients with significant fibrosis (n=123) and non-significant fibrosis (n=898) were selected to form the original dataset. Variable selection was performed using least absolute shrinkage and selection operator (Lasso) regression, and multivariate logistic regression analysis was used to develop a prediction model. The utility of the model is assessed in terms of its discrimination, calibration and clinical usability. Bootstrap-resampling internal validation was used to measure the accuracy of the prediction model.ResultsThis study established a new model consisting of 9 common clinical indicators and developed an online calculator to show the model. Compared with the previously proposed liver fibrosis scoring system, this model showed the best discrimination and predictive performance in the training cohort (0.812,95%CI 0.769-0.855) and the validation cohort (0.805,95%CI 0.762-0.847), with the highest area under curve. Specificity(0.823), sensitivity(0.699), positive likelihood ratio(3.949) and negative likelihood ratio(0.366) were equally excellent. The calibration plot of the predicted probability and the actual occurrence probability of significant fibrosis shows excellent consistency, indicating that the model calibration is outstanding. Combined with decision curve analysis, this model has a great benefit in the range of 0.1-0.8 threshold probability, and has a good application value for the diagnosis of clinical significant fibrosis.ConclusionThis study proposes a new non-invasive diagnostic model that combines clinical indicators to provide an accurate and convenient individualized diagnosis of significant fibrosis in patients with non-alcoholic fatty liver disease.https://www.frontiersin.org/articles/10.3389/fendo.2023.1207365/fullnon-alcoholic fatty liver diseaseliver fibrosisprediction modellogistic regressionNHANES |
| spellingShingle | Yuanhui Guo Baixuan Shen Yanli Xue Ying Li Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States Frontiers in Endocrinology non-alcoholic fatty liver disease liver fibrosis prediction model logistic regression NHANES |
| title | Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States |
| title_full | Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States |
| title_fullStr | Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States |
| title_full_unstemmed | Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States |
| title_short | Development and validation of a non-invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the United States |
| title_sort | development and validation of a non invasive model for predicting significant fibrosis based on patients with nonalcoholic fatty liver disease in the united states |
| topic | non-alcoholic fatty liver disease liver fibrosis prediction model logistic regression NHANES |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1207365/full |
| work_keys_str_mv | AT yuanhuiguo developmentandvalidationofanoninvasivemodelforpredictingsignificantfibrosisbasedonpatientswithnonalcoholicfattyliverdiseaseintheunitedstates AT baixuanshen developmentandvalidationofanoninvasivemodelforpredictingsignificantfibrosisbasedonpatientswithnonalcoholicfattyliverdiseaseintheunitedstates AT yanlixue developmentandvalidationofanoninvasivemodelforpredictingsignificantfibrosisbasedonpatientswithnonalcoholicfattyliverdiseaseintheunitedstates AT yingli developmentandvalidationofanoninvasivemodelforpredictingsignificantfibrosisbasedonpatientswithnonalcoholicfattyliverdiseaseintheunitedstates |