Cem Şimşek, Hasan Şahin, İbrahim Emir Tekin, Taha Koray Şahin, Yasemin Hatice Balaban

Sirozlu Hastalarda Yapay Zeka ile Özofagus Varis Tahmini

Giriş ve amaç: Sirozlu hastalarda özofagus varis taraması, ilişkili mortaliteyi %25’e varan oranlarda azaltmak için en iyi strateji olmaya devam etmektedir. Tanısal üst endoskopi altın standarttır ancak invaziv olması rutin taramayı güçleştirmektedir. Elastografi ile non-invaziv fibrosis ölçümleri maliyetli ve pratik değildir. Mevcut laboratuvar ve klinik değişkenleri kullanan testlerin ise performansları elastografiden daha düşük kalmaktadır. Non-invaziv, erişilebilir ve doğru testler gereklidir. Bu bağlamda varis riskini belirlemek için makine öğrenmesi yöntemleri kullanılabilir. Bu çalışmada, bir makine öğrenme modelinin sirozlu hastalarda özofagus varislerini tahmin etme performansını ve kullanılabilirliğini test etmeyi amaçladık. Gereç ve yöntem: Kliniğimizin veri tabanından üst endoskopi ile varis taraması yapılan sirozlu hastaları geriye dönük olarak değerlendirdik. Demografik, klinik, radyolojik, endoskopik ve laboratuvar verileri toplandı. Her hasta için Child-Pugh, APRI, FIB-4, AAR, PCSD testleri hesaplandı. Problem için gradyan destekli makine öğrenme algoritması oluşturulmuştur. Özofagus varislerinin varlığını tespit etmek için lojistik regresyon ile testlerin ve modelin ROC’lerin altındaki alanlarla olan performansları karşılaştırıldı. Bulgular: Çalışma popülasyonu, 105’i özofagus varisi olan ve 33’ü daha yüksek riskli olan 201 hastadan oluşturuldu. Varisli hastalar daha yaşlı, ileri Child evreleri, daha büyük dalak boyutları ve daha yüksek MELD-Na skorlarına sahipti. Testlerin varis olan hastaları tahmin performanslarının AUC değerleri: 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) şeklindeydi. Makine öğrenimi modelinin varisleri tahmin etmek için ortalama AUC değeri 0.68(0.060), F1- skoru 0.7 ve doğruluk %63 idi. Sonuçlar:Makine öğrenimi modellerinin, sirotik hastalarda özofagus varislerini tahmin etmekteki performansı, invazif olmayan testlerle karşılaştırılabilir düzeydeydi.Anahtar Kelimeler: Karaciğer hastalığı, siroz, özofagus varisleri, yapay zeka, makine öğrenmesi

Artificial Intelligence to Predict Esophageal Varices in Patients with Cirrhosis

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

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