Research Article

Artificial Intelligence to Predict Esophageal Varices in Patients with Cirrhosis

Volume: 12 Number: 3 July 1, 2021
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Artificial Intelligence to Predict Esophageal Varices in Patients with Cirrhosis

Abstract

Background: Screening for varices remains as the best strategy to decrease associated mortality that reaches 25%. Diagnostic endoscopy is gold standard but invasive for routine screening. Non-invasive stiffness measurements with elastography is costly and impractical. Non-elastogarphic tests that use available laboratory and clinical variables are feasible but their performance remains inferior to elastography. Non-invasive, accessible and accurate test is needed. Machine learning methods can be used in this sense to provide better diagnostic performances. We aimed to test the ability of a machine learning model to predict esophageal varices in patients with cirrhosis. Materials and methods: We retrospectively evaluated patients with cirrhosis at the time of their screening upper endoscopies from our institutional database. Demographic, clinical, radiologic, endoscopic and laboratory data was collected. Child-Pugh, APRI, FIB-4, AAR, PCSD tests were calculated for each patient. Gradient boosted machine learning algorithm was constructed for the problem. A logistic regression as well as tests’ and model’s performances with areas under ROCs were compared to detect presence of esophageal varices. Results: Study population consisted of 201 patients whom 105 had esopheageal varices which 33 were higher risk. Patients with varices were older, advanced Child stages, larger splenic diameters and higher MELD-Na scores. Composite scores’ were as follows: FIB-4 0.57 (0.49-0.65), APRI 0.47 (0.38-0.55), PCSD 0.511 (0.42-0.59), AAR 0.481 (0.39-0.56). Machine learning model’s mean AUC to predict varices was 0.68(0.060), F1- score was 0.7 and accuracy was 63%. Conclusions: Machine learning model outperformed non-invasive tests to predict esophageal varices in cirrhotic patients.

Keywords

Supporting Institution

Algomedicus Artificial Intelligence and Medical Simulation Company, Ankara, Turkey

Project Number

AG-21.002

References

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Details

Primary Language

English

Subjects

Gastroenterology and Hepatology

Journal Section

Research Article

Authors

Emir Tekin
0000-0003-4118-8639
Türkiye

Hasan Sahin
0000-0002-6699-8278
Türkiye

Yasemin Hatice Balaban
0000-0002-0901-9192
Türkiye

Publication Date

July 1, 2021

Submission Date

April 27, 2021

Acceptance Date

June 6, 2021

Published in Issue

Year 2021 Volume: 12 Number: 3

DOI

https://doi.org/10.31067/acusaglik.928498

IZ

https://izlik.org/JA27LL32UM

Cite

RIS / Bibtex
EndNote
Şimşek C, Tekin E, Sahin H, Sahin TK, Balaban YH (July 1, 2021) Artificial Intelligence to Predict Esophageal Varices in Patients with Cirrhosis. Acıbadem Üniversitesi Sağlık Bilimleri Dergisi 12 3 625–629.

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Identifying predictors of varices grading in patients with cirrhosis using ensemble learning

Clinical Chemistry and Laboratory Medicine (CCLM)

https://doi.org/10.1515/cclm-2022-0508