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.
Supporting Institution
Algomedicus Artificial Intelligence and Medical Simulation Company, Ankara, Turkey
Project Number
AG-21.002
References
- 1. Augustin S, Pons M, Maurice JB, Bureau C, Stefanescu H, Ney M, et al. Expanding the Baveno VI criteria for the screening of varices in patients with compensated advanced chronic liver disease. Hepatology. 2017;66(6):1980-8.
- 2. Garcia-Tsao G, Abraldes JG, Berzigotti A, Bosch J. Portal Hypertensive Bleeding in Cirrhosis: Risk Stratification, Diagnosis, and Management: 2016 Practice Guidance by the American Association for the Study of Liver Diseases (vol 65, pg 310, 2017). Hepatology. 2017;66(1):304-5.
- 3. Kamath PS, Wiesner RH, Malinchoc M, Kremers W, Therneau TM, Kosberg CL, et al. A model to predict survival in patients with end-stage liver disease. Hepatology. 2001;33(2):464-70.
- 4. Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg. 1973;60(8):646-9.
- 5. Lin ZH, Xin YN, Dong QJ, Wang Q, Jiang XJ, Zhan SH, et al. Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology. 2011;53(3):726-36.
- 6. Giannini EG, Botta F, Borro P, Dulbecco P, Testa E, Mansi C, et al. Application of the platelet count/spleen diameter ratio to rule out the presence of oesophageal varices in patients with cirrhosis: a validation study based on follow-up. Dig Liver Dis. 2005;37(10):779-85.
- 7. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-25.
- 8. Deng H, Qi XS, Guo XZ. Diagnostic Accuracy of APRI, AAR, FIB-4, FI, King, Lok, Forns, and FibroIndex Scores in Predicting the Presence of Esophageal Varices in Liver Cirrhosis A Systematic Review and Meta-Analysis. Medicine. 2015;94(42).
- 9. Tafarel JR, Tolentino LHL, Correa LM, Bonilha DR, Piauilino P, Martins FP, et al. Prediction of esophageal varices in hepatic cirrhosis by noninvasive markers. Eur J Gastroen Hepat. 2011;23(9):754-8.
- 10. Stefanescu H, Grigorescu M, Lupsor M, Maniu A, Crisan D, Procopet B, et al. A New and Simple Algorithm for the Noninvasive Assessment of Esophageal Varices in Cirrhotic Patients Using Serum Fibrosis Markers and Transient Elastography. J Gastrointest Liver. 2011;20(1):57-64.
- 11. Wang JH, Chuah SK, Lu SN, Hung CH, Chen CH, Kee KM, et al. Transient elastography and simple blood markers in the diagnosis of esophageal varices for compensated patients with hepatitis B virus-related cirrhosis. J Gastroen Hepatol. 2012;27(7):1213-8.
- 12. B CL-GN, De Vinatea-Serrano L, Piscoya A, Segura ER. [Performance of the FIB-4 index in esophageal varices screening in patients with the diagnosis of liver cirrhosis]. Rev Gastroenterol Peru. 2020;40(1):29-35.
- 13. Dong TS, Kalani A, Aby ES, Le L, Luu K, Hauer M, et al. Machine Learning-based Development and Validation of a Scoring System for Screening High-Risk Esophageal Varices. Clinical Gastroenterology and Hepatology. 2019;17(9):1894-901.e1.
Abstract
Project Number
AG-21.002
References
- 1. Augustin S, Pons M, Maurice JB, Bureau C, Stefanescu H, Ney M, et al. Expanding the Baveno VI criteria for the screening of varices in patients with compensated advanced chronic liver disease. Hepatology. 2017;66(6):1980-8.
- 2. Garcia-Tsao G, Abraldes JG, Berzigotti A, Bosch J. Portal Hypertensive Bleeding in Cirrhosis: Risk Stratification, Diagnosis, and Management: 2016 Practice Guidance by the American Association for the Study of Liver Diseases (vol 65, pg 310, 2017). Hepatology. 2017;66(1):304-5.
- 3. Kamath PS, Wiesner RH, Malinchoc M, Kremers W, Therneau TM, Kosberg CL, et al. A model to predict survival in patients with end-stage liver disease. Hepatology. 2001;33(2):464-70.
- 4. Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg. 1973;60(8):646-9.
- 5. Lin ZH, Xin YN, Dong QJ, Wang Q, Jiang XJ, Zhan SH, et al. Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology. 2011;53(3):726-36.
- 6. Giannini EG, Botta F, Borro P, Dulbecco P, Testa E, Mansi C, et al. Application of the platelet count/spleen diameter ratio to rule out the presence of oesophageal varices in patients with cirrhosis: a validation study based on follow-up. Dig Liver Dis. 2005;37(10):779-85.
- 7. Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-25.
- 8. Deng H, Qi XS, Guo XZ. Diagnostic Accuracy of APRI, AAR, FIB-4, FI, King, Lok, Forns, and FibroIndex Scores in Predicting the Presence of Esophageal Varices in Liver Cirrhosis A Systematic Review and Meta-Analysis. Medicine. 2015;94(42).
- 9. Tafarel JR, Tolentino LHL, Correa LM, Bonilha DR, Piauilino P, Martins FP, et al. Prediction of esophageal varices in hepatic cirrhosis by noninvasive markers. Eur J Gastroen Hepat. 2011;23(9):754-8.
- 10. Stefanescu H, Grigorescu M, Lupsor M, Maniu A, Crisan D, Procopet B, et al. A New and Simple Algorithm for the Noninvasive Assessment of Esophageal Varices in Cirrhotic Patients Using Serum Fibrosis Markers and Transient Elastography. J Gastrointest Liver. 2011;20(1):57-64.
- 11. Wang JH, Chuah SK, Lu SN, Hung CH, Chen CH, Kee KM, et al. Transient elastography and simple blood markers in the diagnosis of esophageal varices for compensated patients with hepatitis B virus-related cirrhosis. J Gastroen Hepatol. 2012;27(7):1213-8.
- 12. B CL-GN, De Vinatea-Serrano L, Piscoya A, Segura ER. [Performance of the FIB-4 index in esophageal varices screening in patients with the diagnosis of liver cirrhosis]. Rev Gastroenterol Peru. 2020;40(1):29-35.
- 13. Dong TS, Kalani A, Aby ES, Le L, Luu K, Hauer M, et al. Machine Learning-based Development and Validation of a Scoring System for Screening High-Risk Esophageal Varices. Clinical Gastroenterology and Hepatology. 2019;17(9):1894-901.e1.
Details
| Primary Language | English |
|---|---|
| Subjects | Gastroenterology and Hepatology |
| Journal Section | Research Articles |
| Authors | |
| Project Number | AG-21.002 |
| Publication Date | July 1, 2021 |
| Submission Date | April 27, 2021 |
| Published in Issue | Year 2021 |