Develop and validate a novel online AIHI-nomogram to predict severe liver inflammation in patients with autoimmune hepatitis
Copyright © 2023 Fundación Clínica Médica Sur, A.C. Published by Elsevier España, S.L.U. All rights reserved..
INTRODUCTION AND OBJECTIVES: Assessment of liver inflammation plays a vital role in the management of patients with autoimmune hepatitis (AIH). We aimed to establish and validate a nomogram to predict severe liver inflammation in AIH patients.
PATIENTS AND METHODS: AIH patients who underwent liver biopsy were included and randomly divided into a training set and a validation set. Independent predictors of severe liver inflammation were selected by the least absolute shrinkage and selection operator regression from the training set and used to conduct a nomogram. Receiver characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) were adopted to evaluate the performance of nomogram.
RESULTS: Of the 213 patients, female patients accounted for 83.1% and the median age was 53.0 years. The albumin, gamma-glutamyl transpeptidase, total bilirubin, red cell distribution width, prothrombin time, and platelets were independent predictors of severe inflammation. An online AIHI-nomogram was established and was available at https://ndth-zzy.shinyapps.io/AIHI-nomogram/. The calibration curve revealed that the AIHI-nomogram had a good agreement with actual observation in the training and validation sets. The area under the ROCs of AIHI-nomogram were 0.795 in the training set and 0.759 in the validation set, showing significantly better performance than alanine aminotransferase and immunoglobulin G in the training and validation sets, as well in AIH patients with normal ALT in the training set. DCA indicated that the AIHI-nomogram was clinically useful.
CONCLUSIONS: This novel AIHI-nomogram provided an excellent prediction of severe liver inflammation in AIH patients and could be used for the better management of AIH.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Annals of hepatology - 28(2023), 6 vom: 11. Juli, Seite 101134 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Zhiyi [VerfasserIn] |
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| Links: |
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| Themen: |
Autoimmune hepatitis |
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| Anmerkungen: |
Date Revised 04.08.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.aohep.2023.101134 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM359431267 |
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| 041 | |a eng | ||
| 100 | 1 | |a Zhang, Zhiyi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Develop and validate a novel online AIHI-nomogram to predict severe liver inflammation in patients with autoimmune hepatitis |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 04.08.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright © 2023 Fundación Clínica Médica Sur, A.C. Published by Elsevier España, S.L.U. All rights reserved. | ||
| 520 | |a INTRODUCTION AND OBJECTIVES: Assessment of liver inflammation plays a vital role in the management of patients with autoimmune hepatitis (AIH). We aimed to establish and validate a nomogram to predict severe liver inflammation in AIH patients | ||
| 520 | |a PATIENTS AND METHODS: AIH patients who underwent liver biopsy were included and randomly divided into a training set and a validation set. Independent predictors of severe liver inflammation were selected by the least absolute shrinkage and selection operator regression from the training set and used to conduct a nomogram. Receiver characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) were adopted to evaluate the performance of nomogram | ||
| 520 | |a RESULTS: Of the 213 patients, female patients accounted for 83.1% and the median age was 53.0 years. The albumin, gamma-glutamyl transpeptidase, total bilirubin, red cell distribution width, prothrombin time, and platelets were independent predictors of severe inflammation. An online AIHI-nomogram was established and was available at https://ndth-zzy.shinyapps.io/AIHI-nomogram/. The calibration curve revealed that the AIHI-nomogram had a good agreement with actual observation in the training and validation sets. The area under the ROCs of AIHI-nomogram were 0.795 in the training set and 0.759 in the validation set, showing significantly better performance than alanine aminotransferase and immunoglobulin G in the training and validation sets, as well in AIH patients with normal ALT in the training set. DCA indicated that the AIHI-nomogram was clinically useful | ||
| 520 | |a CONCLUSIONS: This novel AIHI-nomogram provided an excellent prediction of severe liver inflammation in AIH patients and could be used for the better management of AIH | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Autoimmune hepatitis | |
| 650 | 4 | |a Liver inflammation | |
| 650 | 4 | |a Prediction model | |
| 700 | 1 | |a Wang, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Huali |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yiguang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jiacheng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yilin |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yuxin |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Shengxia |e verfasserin |4 aut | |
| 700 | 1 | |a Tong, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Xiaomin |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yongfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Chuanwu |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Yuanwang |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Chao |e verfasserin |4 aut | |
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