TY - JOUR
T1 - Machine Learning-based Development and Validation of a Scoring System for Screening High-Risk Esophageal Varices
AU - Dong, Tien S.
AU - Kalani, Amir
AU - Aby, Elizabeth S.
AU - Le, Long
AU - Luu, K.
AU - Hauer, Meg
AU - Kamath, R.
AU - Lindor, Keith D.
AU - Tabibian, James H.
N1 - Funding Information:
The authors acknowledge the statistical assistance that was provided by the Institute for Digital Research and Education at UCLA. Funding Grant support NIH T32 DK 07180 (T.D.).
Publisher Copyright:
© 2019 AGA Institute
PY - 2019/8
Y1 - 2019/8
N2 - Background & Aims: Many patients with cirrhosis who undergo esophagogastroduodenoscopy (EGD) screening for esophageal varices (EVs) are found to have no or only small EVs. Endoscopic screening for EVs is therefore a potentially deferrable procedure that increases patient risk and healthcare cost. We developed and validated a scoring system, based on readily-available data, to reliably identify patients with EVs that need treatment. Methods: We collected data from 238 patients with cirrhosis undergoing screening EGD from January 2016 through December 2017 at 3 separate hospitals in Los Angeles (training cohort). We abstracted data on patient sex, age, race/ethnicity, platelet counts, and levels of hemoglobin, serum sodium, aspartate aminotransferase, alanine aminotransferase, total bilirubin, international normalized ratio, albumin, urea nitrogen, and creatinine. We also included etiology of cirrhosis, presence of ascites, and presence of hepatic encephalopathy. We used a random forest algorithm to identify factors significantly associated with the presence of EVs and varices needing treatment (VNT) and calculated area under the receiver operating characteristic curve (AUROC). We called the resulting formula the EVendo score. We tested the accuracy of EVendo in a prospective study of 109 patients undergoing screening EGDs at the same medical centers from January 2018 through December 2018 (validation cohort). Results: We developed an algorithm that identified patients with EVs and VNT based on international normalized ratio, level of aspartate aminotransferase, platelet counts, urea nitrogen, hemoglobin, and presence of ascites. The EVendo score identified patients with EVs in the training set with an AUROC of 0.84, patients with EVs in the validation set with and AUROC of 0.82, and EVs in patients with cirrhosis Child-Turcotte-Pugh class A (n = 235) with an AUROC of 0.81. The score identified patients with VNT in the training set with an AUROC of 0.74, VNT in the validation set with and AUROC of 0.75, and VNT in patients with cirrhosis Child-Turcotte-Pugh class A with and AUROC of 0.75. An EVendo score below 3.90 would have spared 30.5% patients from EGDs, missing only 2.8% of VNT. The same cutoff would have spared 40.0% of patients with Child-Turcotte-Pugh class A cirrhosis from EGDs, missing only 1.1% of VNT. Conclusions: We algorithmically developed a formula, called the EVendo score, that can be used to predict EVs and VNT based on readily available data in patients with cirrhosis. This score could help patients at low risk for VNT avoid unnecessary EGDs.
AB - Background & Aims: Many patients with cirrhosis who undergo esophagogastroduodenoscopy (EGD) screening for esophageal varices (EVs) are found to have no or only small EVs. Endoscopic screening for EVs is therefore a potentially deferrable procedure that increases patient risk and healthcare cost. We developed and validated a scoring system, based on readily-available data, to reliably identify patients with EVs that need treatment. Methods: We collected data from 238 patients with cirrhosis undergoing screening EGD from January 2016 through December 2017 at 3 separate hospitals in Los Angeles (training cohort). We abstracted data on patient sex, age, race/ethnicity, platelet counts, and levels of hemoglobin, serum sodium, aspartate aminotransferase, alanine aminotransferase, total bilirubin, international normalized ratio, albumin, urea nitrogen, and creatinine. We also included etiology of cirrhosis, presence of ascites, and presence of hepatic encephalopathy. We used a random forest algorithm to identify factors significantly associated with the presence of EVs and varices needing treatment (VNT) and calculated area under the receiver operating characteristic curve (AUROC). We called the resulting formula the EVendo score. We tested the accuracy of EVendo in a prospective study of 109 patients undergoing screening EGDs at the same medical centers from January 2018 through December 2018 (validation cohort). Results: We developed an algorithm that identified patients with EVs and VNT based on international normalized ratio, level of aspartate aminotransferase, platelet counts, urea nitrogen, hemoglobin, and presence of ascites. The EVendo score identified patients with EVs in the training set with an AUROC of 0.84, patients with EVs in the validation set with and AUROC of 0.82, and EVs in patients with cirrhosis Child-Turcotte-Pugh class A (n = 235) with an AUROC of 0.81. The score identified patients with VNT in the training set with an AUROC of 0.74, VNT in the validation set with and AUROC of 0.75, and VNT in patients with cirrhosis Child-Turcotte-Pugh class A with and AUROC of 0.75. An EVendo score below 3.90 would have spared 30.5% patients from EGDs, missing only 2.8% of VNT. The same cutoff would have spared 40.0% of patients with Child-Turcotte-Pugh class A cirrhosis from EGDs, missing only 1.1% of VNT. Conclusions: We algorithmically developed a formula, called the EVendo score, that can be used to predict EVs and VNT based on readily available data in patients with cirrhosis. This score could help patients at low risk for VNT avoid unnecessary EGDs.
KW - AST
KW - BUN
KW - CTP
KW - Hemoglobin
KW - INR
KW - Risk Analysis
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U2 - 10.1016/j.cgh.2019.01.025
DO - 10.1016/j.cgh.2019.01.025
M3 - Article
C2 - 30708109
AN - SCOPUS:85063339677
SN - 1542-3565
VL - 17
SP - 1894-1901.e1
JO - Clinical Gastroenterology and Hepatology
JF - Clinical Gastroenterology and Hepatology
IS - 9
ER -