Cem ŞİMŞEK, Emir TEKİN, Hasan SAHİN, Taha Koray SAHİN, Yasemin Hatice BALABAN

Artificial Intelligence to Predict Esophageal Varices in Patients with Cirrhosis

Öz 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.

Anahtar Kelimeler:

esophageal varices, artificial intelligence, machine learning, screening, prediction

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