Neehar D Parikh, Patricia Jones, Reena Salgia, Irun Bhan, Lauren T Grinspan, Janice H Jou, Kali Zhou, Prasun Jalal, Giorgio Roccaro, Amol S Rangnekar, Jihane N Benhammou, Anjana Pillai, Neil Mehta, Joel Wedd, Ju Dong Yang, Amy K Kim, Andres Duarte-Rojo, Omobonike O Oloruntoba, Amit Tevar, Jennifer S Au, Yamile Blain, Sanjana Rao, Onofrio A Catalano, Sara Lewis, Mishal Mendiratta-Lala, Kevin King, Lekha Sachdev, Edward W Lee, Jill Bruno, Ihab Kamel, Celestina Tolosa, Karissa Kao, Tarek Badawi, Eric M Przybyszewski, Lisa Quirk, Piyush Nathani, Brandy Haydel, Emily Leven, Nicole Wong, Robert Albertian, Ariana Chen, Fuad Z Aloor, Islam B Mohamed, Ahmed Elkheshen, Charles Marvil, Gerard Issac, Joseph W Clinton, Stephanie M Woo, Jung Yum, Erin Rieger, Alan L Hutchison, Don A Turner, Manaf Alsudaney, Perla Hernandez, Ziyi Xu, Abdullah Khalid, Bethany Barrick, Bo Wang, Elliot B Tapper, Wei Hao, Amit G Singal
{"title":"用于检测无法切除的肝癌患者高风险静脉曲张的无创模型的开发与验证","authors":"Neehar D Parikh, Patricia Jones, Reena Salgia, Irun Bhan, Lauren T Grinspan, Janice H Jou, Kali Zhou, Prasun Jalal, Giorgio Roccaro, Amol S Rangnekar, Jihane N Benhammou, Anjana Pillai, Neil Mehta, Joel Wedd, Ju Dong Yang, Amy K Kim, Andres Duarte-Rojo, Omobonike O Oloruntoba, Amit Tevar, Jennifer S Au, Yamile Blain, Sanjana Rao, Onofrio A Catalano, Sara Lewis, Mishal Mendiratta-Lala, Kevin King, Lekha Sachdev, Edward W Lee, Jill Bruno, Ihab Kamel, Celestina Tolosa, Karissa Kao, Tarek Badawi, Eric M Przybyszewski, Lisa Quirk, Piyush Nathani, Brandy Haydel, Emily Leven, Nicole Wong, Robert Albertian, Ariana Chen, Fuad Z Aloor, Islam B Mohamed, Ahmed Elkheshen, Charles Marvil, Gerard Issac, Joseph W Clinton, Stephanie M Woo, Jung Yum, Erin Rieger, Alan L Hutchison, Don A Turner, Manaf Alsudaney, Perla Hernandez, Ziyi Xu, Abdullah Khalid, Bethany Barrick, Bo Wang, Elliot B Tapper, Wei Hao, Amit G Singal","doi":"10.1016/j.cgh.2024.07.008","DOIUrl":null,"url":null,"abstract":"<p><strong>Background & aims: </strong>Noninvasive variceal risk stratification systems have not been validated in patients with hepatocellular carcinoma (HCC), which presents logistical barriers for patients in the setting of systemic HCC therapy. We aimed to develop and validate a noninvasive algorithm for the prediction of varices in patients with unresectable HCC.</p><p><strong>Methods: </strong>We performed a retrospective cohort study in 21 centers in the United States including adult patients with unresectable HCC and Child-Pugh A5-B7 cirrhosis diagnosed between 2007 and 2019. We included patients who completed an esophagogastroduodonoscopy (EGD) within 12 months of index imaging but before HCC treatment. We divided the cohort into a 70:30 training set and validation set, with the goal of maximizing negative predictive value (NPV) to avoid EGD in low-risk patients.</p><p><strong>Results: </strong>We included 707 patients (median age, 64.6 years; 80.6% male; 74.0% White). Median time from HCC diagnosis to EGD was 47 (interquartile range, 114) days, with 25.0% of patients having high-risk varices. A model using clinical variables alone achieved an NPV of 86.3% in the validation cohort, whereas a model integrating clinical and imaging variables had an NPV 97.4% in validation. The clinical and imaging model would avoid EGDs in more than half of low-risk patients while misclassifying 7.7% of high-risk patients.</p><p><strong>Conclusions: </strong>A model incorporating clinical and imaging data can accurately predict the absence of high-risk varices in patients with HCC and avoid EGD in many low-risk patients before the initiation of systemic therapy, thus expediting their care and avoiding treatment delays.</p>","PeriodicalId":10347,"journal":{"name":"Clinical Gastroenterology and Hepatology","volume":null,"pages":null},"PeriodicalIF":11.6000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of a Noninvasive Model for the Detection of High-Risk Varices in Patients with Unresectable HCC.\",\"authors\":\"Neehar D Parikh, Patricia Jones, Reena Salgia, Irun Bhan, Lauren T Grinspan, Janice H Jou, Kali Zhou, Prasun Jalal, Giorgio Roccaro, Amol S Rangnekar, Jihane N Benhammou, Anjana Pillai, Neil Mehta, Joel Wedd, Ju Dong Yang, Amy K Kim, Andres Duarte-Rojo, Omobonike O Oloruntoba, Amit Tevar, Jennifer S Au, Yamile Blain, Sanjana Rao, Onofrio A Catalano, Sara Lewis, Mishal Mendiratta-Lala, Kevin King, Lekha Sachdev, Edward W Lee, Jill Bruno, Ihab Kamel, Celestina Tolosa, Karissa Kao, Tarek Badawi, Eric M Przybyszewski, Lisa Quirk, Piyush Nathani, Brandy Haydel, Emily Leven, Nicole Wong, Robert Albertian, Ariana Chen, Fuad Z Aloor, Islam B Mohamed, Ahmed Elkheshen, Charles Marvil, Gerard Issac, Joseph W Clinton, Stephanie M Woo, Jung Yum, Erin Rieger, Alan L Hutchison, Don A Turner, Manaf Alsudaney, Perla Hernandez, Ziyi Xu, Abdullah Khalid, Bethany Barrick, Bo Wang, Elliot B Tapper, Wei Hao, Amit G Singal\",\"doi\":\"10.1016/j.cgh.2024.07.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background & aims: </strong>Noninvasive variceal risk stratification systems have not been validated in patients with hepatocellular carcinoma (HCC), which presents logistical barriers for patients in the setting of systemic HCC therapy. We aimed to develop and validate a noninvasive algorithm for the prediction of varices in patients with unresectable HCC.</p><p><strong>Methods: </strong>We performed a retrospective cohort study in 21 centers in the United States including adult patients with unresectable HCC and Child-Pugh A5-B7 cirrhosis diagnosed between 2007 and 2019. We included patients who completed an esophagogastroduodonoscopy (EGD) within 12 months of index imaging but before HCC treatment. We divided the cohort into a 70:30 training set and validation set, with the goal of maximizing negative predictive value (NPV) to avoid EGD in low-risk patients.</p><p><strong>Results: </strong>We included 707 patients (median age, 64.6 years; 80.6% male; 74.0% White). Median time from HCC diagnosis to EGD was 47 (interquartile range, 114) days, with 25.0% of patients having high-risk varices. A model using clinical variables alone achieved an NPV of 86.3% in the validation cohort, whereas a model integrating clinical and imaging variables had an NPV 97.4% in validation. The clinical and imaging model would avoid EGDs in more than half of low-risk patients while misclassifying 7.7% of high-risk patients.</p><p><strong>Conclusions: </strong>A model incorporating clinical and imaging data can accurately predict the absence of high-risk varices in patients with HCC and avoid EGD in many low-risk patients before the initiation of systemic therapy, thus expediting their care and avoiding treatment delays.</p>\",\"PeriodicalId\":10347,\"journal\":{\"name\":\"Clinical Gastroenterology and Hepatology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.6000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Gastroenterology and Hepatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cgh.2024.07.008\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Gastroenterology and Hepatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.cgh.2024.07.008","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
Development and Validation of a Noninvasive Model for the Detection of High-Risk Varices in Patients with Unresectable HCC.
Background & aims: Noninvasive variceal risk stratification systems have not been validated in patients with hepatocellular carcinoma (HCC), which presents logistical barriers for patients in the setting of systemic HCC therapy. We aimed to develop and validate a noninvasive algorithm for the prediction of varices in patients with unresectable HCC.
Methods: We performed a retrospective cohort study in 21 centers in the United States including adult patients with unresectable HCC and Child-Pugh A5-B7 cirrhosis diagnosed between 2007 and 2019. We included patients who completed an esophagogastroduodonoscopy (EGD) within 12 months of index imaging but before HCC treatment. We divided the cohort into a 70:30 training set and validation set, with the goal of maximizing negative predictive value (NPV) to avoid EGD in low-risk patients.
Results: We included 707 patients (median age, 64.6 years; 80.6% male; 74.0% White). Median time from HCC diagnosis to EGD was 47 (interquartile range, 114) days, with 25.0% of patients having high-risk varices. A model using clinical variables alone achieved an NPV of 86.3% in the validation cohort, whereas a model integrating clinical and imaging variables had an NPV 97.4% in validation. The clinical and imaging model would avoid EGDs in more than half of low-risk patients while misclassifying 7.7% of high-risk patients.
Conclusions: A model incorporating clinical and imaging data can accurately predict the absence of high-risk varices in patients with HCC and avoid EGD in many low-risk patients before the initiation of systemic therapy, thus expediting their care and avoiding treatment delays.
期刊介绍:
Clinical Gastroenterology and Hepatology (CGH) is dedicated to offering readers a comprehensive exploration of themes in clinical gastroenterology and hepatology. Encompassing diagnostic, endoscopic, interventional, and therapeutic advances, the journal covers areas such as cancer, inflammatory diseases, functional gastrointestinal disorders, nutrition, absorption, and secretion.
As a peer-reviewed publication, CGH features original articles and scholarly reviews, ensuring immediate relevance to the practice of gastroenterology and hepatology. Beyond peer-reviewed content, the journal includes invited key reviews and articles on endoscopy/practice-based technology, health-care policy, and practice management. Multimedia elements, including images, video abstracts, and podcasts, enhance the reader's experience. CGH remains actively engaged with its audience through updates and commentary shared via platforms such as Facebook and Twitter.