Browsing by Author "Agarunov, Emil"

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    Digestive Manifestations in Patients Hospitalized With Coronavirus Disease 2019
    (Elsevier, 2020-10-01) Elmunzer, B. Joseph; Spitzer, Rebecca L.; Foster, Lydia D.; Merchant, Ambreen A.; Howard, Eric F.; Patel, Vaishali A.; West, Mary K.; Qayed, Emad; Nustas, Rosemary; Zakaria, Ali; Piper, Marc S.; Taylor, Jason R.; Jaza, Lujain; Forbes, Nauzer; Chau, Millie; Lara, Luis F.; Papachristou, Georgios I.; Volk, Michael L.; Hilson, Liam G.; Zhou, Selena; Kushnir, Vladimir M.; Lenyo, Alexandria M.; McLeod, Caroline G.; Amin, Sunil; Kuftinec, Gabriela N.; Yadav, Dhiraj; Fox, Charlie; Kolb, Jennifer M.; Pawa, Swati; Pawa, Rishi; Canakis, Andrew; Huang, Christopher; Jamil, Laith H.; Aneese, Andrew M.; Glamour, Benita K.; Smith, Zachary L.; Hanley, Katherine A.; Wood, Jordan; Patel, Harsh K.; Shah, Janak N.; Agarunov, Emil; Sethi, Amrita; Fogel, Evan L.; McNulty, Gail; Haseeb, Abdul; Trieu, Judy A.; Dixon, Rebekah E.; Yang, Jeong Yun; Mendelsohn, Robin B.; Calo, Delia; Aroniadis, Olga C.; LaComb, Joseph F.; Scheiman, James M.; Sauer, Bryan G.; Dang, Duyen T.; Piraka, Cyrus R.; Shah, Eric D.; Pohl, Heiko; Tierney, William M.; Mitchell, Stephanie; Condon, Ashwinee; Lenhart, Adrienne; Dua, Kulwinder S.; Kanagala, Vikram S.; Kamal, Ayesha; Singh, Vikesh K.; Pinto-Sanchez, Maria Ines; Hutchinson, Joy M.; Kwon, Richard S.; Korsnes, Sheryl J.; Singh, Harminder; Solati, Zahra; Willingham, Field F.; Yachimski, Patrick S.; Conwell, Darwin L.; Mosier, Evan; Azab, Mohamed; Patel, Anish; Buxbaum, James; Wani, Sachin; Chak, Amitabh; Hosmer, Amy E.; Keswani, Rajesh N.; DiMaio, Christopher J.; Bronze, Michael S.; Muthusamy, Raman; Canto, Marcia I.; Gjeorgjievski, V. Mihajlo; Imam, Zaid; Odish, Fadi; Edhi, Ahmed I.; Orosey, Molly; Tiwari, Abhinav; Patwardhan, Soumil; Brown, Nicholas G.; Patel, Anish A.; Ordiah, Collins O.; Sloan, Ian P.; Cruz, Lilian; Koza, Casey L.; Okafor, Uchechi; Hollander, Thomas; Furey, Nancy; Reykhart, Olga; Zbib, Natalia H.; Damianos, John A.; Esteban, James; Hajidiacos, Nick; Saul, Melissa; Mays, Melanie; Anderson, Gulsum; Wood, Kelley; Mathews, Laura; Diakova, Galina; Caisse, Molly; Wakefield, Lauren; Nitchie, Haley; Waljee, Akbar K.; Tang, Weijing; Zhang, Yueyang; Zhu, Ji; Deshpande, Amar R.; Rockey, Don C.; Alford, Teldon B.; Durkalski, Valerie; Medicine, School of Medicine
    Background & Aims The prevalence and significance of digestive manifestations in coronavirus disease 2019 (COVID-19) remain uncertain. We aimed to assess the prevalence, spectrum, severity, and significance of digestive manifestations in patients hospitalized with COVID-19. Methods Consecutive patients hospitalized with COVID-19 were identified across a geographically diverse alliance of medical centers in North America. Data pertaining to baseline characteristics, symptomatology, laboratory assessment, imaging, and endoscopic findings from the time of symptom onset until discharge or death were abstracted manually from electronic health records to characterize the prevalence, spectrum, and severity of digestive manifestations. Regression analyses were performed to evaluate the association between digestive manifestations and severe outcomes related to COVID-19. Results A total of 1992 patients across 36 centers met eligibility criteria and were included. Overall, 53% of patients experienced at least 1 gastrointestinal symptom at any time during their illness, most commonly diarrhea (34%), nausea (27%), vomiting (16%), and abdominal pain (11%). In 74% of cases, gastrointestinal symptoms were judged to be mild. In total, 35% of patients developed an abnormal alanine aminotransferase or total bilirubin level; these were increased to less than 5 times the upper limit of normal in 77% of cases. After adjusting for potential confounders, the presence of gastrointestinal symptoms at any time (odds ratio, 0.93; 95% CI, 0.76–1.15) or liver test abnormalities on admission (odds ratio, 1.31; 95% CI, 0.80–2.12) were not associated independently with mechanical ventilation or death. Conclusions Among patients hospitalized with COVID-19, gastrointestinal symptoms and liver test abnormalities were common, but the majority were mild and their presence was not associated with a more severe clinical course.
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    Digestive Manifestations in Patients Hospitalized With Coronavirus Disease 2019
    (Elsevier, 2021-07) Elmunzer, B. Joseph; Spitzer, Rebecca L.; Foster, Lydia D.; Merchant, Ambreen A.; Howard, Eric F.; Patel, Vaishali A.; West, Mary K.; Qayed, Emad; Nustas, Rosemary; Zakaria, Ali; Piper, Marc S.; Taylor, Jason R.; Jaza, Lujain; Forbes, Nauzer; Chau, Millie; Lara, Luis F.; Papachristou, Georgios I.; Volk, Michael L.; Hilson, Liam G.; Zhou, Selena; Kushnir, Vladimir M.; Lenyo, Alexandria M.; McLeod, Caroline G.; Amin, Sunil; Kuftinec, Gabriela N.; Yadav, Dhiraj; Fox, Charlie; Kolb, Jennifer M.; Pawa, Swati; Pawa, Rishi; Canakis, Andrew; Huang, Christopher; Jamil, Laith H.; Aneese, Andrew M.; Glamour, Benita K.; Smith, Zachary L.; Hanley, Katherine A.; Wood, Jordan; Patel, Harsh K.; Shah, Janak N.; Agarunov, Emil; Sethi, Amrita; Fogel, Evan L.; McNulty, Gail; Haseeb, Abdul; Trieu, Judy A.; Dixon, Rebekah E.; Yang, Jeong Yun; Mendelsohn, Robin B.; Calo, Delia; Aroniadis, Olga C.; LaComb, Joseph F.; Scheiman, James M.; Sauer, Bryan G.; Dang, Duyen T.; Piraka, Cyrus R.; Shah, Eric D.; Pohl, Heiko; Tierney, William M.; Mitchell, Stephanie; Condon, Ashwinee; Lenhart, Adrienne; Dua, Kulwinder S.; Kanagala, Vikram S.; Kamal, Ayesha; Singh, Vikesh K.; Pinto-Sanchez, Maria Ines; Hutchinson, Joy M.; Kwon, Richard S.; Korsnes, Sheryl J.; Singh, Harminder; Solati, Zahra; Willingham, Field F.; Yachimski, Patrick S.; Conwell, Darwin L.; Mosier, Evan; Azab, Mohamed; Patel, Anish; Buxbaum, James; Wani, Sachin; Chak, Amitabh; Hosmer, Amy E.; Keswani, Rajesh N.; DiMaio, Christopher J.; Bronze, Michael S.; Muthusamy, Raman; Canto, Marcia I.; Gjeorgjievski, V. Mihajlo; Imam, Zaid; Odish, Fadi; Edhi, Ahmed I.; Orosey, Molly; Tiwari, Abhinav; Patwardhan, Soumil; Brown, Nicholas G.; Patel, Anish A.; Ordiah, Collins O.; Sloan, Ian P.; Cruz, Lilian; Koza, Casey L.; Okafor, Uchechi; Hollander, Thomas; Furey, Nancy; Reykhart, Olga; Zbib, Natalia H.; Damianos, John A.; Esteban, James; Hajidiacos, Nick; Saul, Melissa; Mays, Melanie; Anderson, Gulsum; Wood, Kelley; Mathews, Laura; Diakova, Galina; Caisse, Molly; Wakefield, Lauren; Nitchie, Haley; Waljee, Akbar K.; Tang, Weijing; Zhang, Yueyang; Zhu, Ji; Deshpande, Amar R.; Rockey, Don C.; Alford, Teldon B.; Durkalski, Valerie; North American Alliance for the Study of Digestive Manifestations of COVID-19; Medicine, School of Medicine
    BACKGROUND & AIMS: The prevalence and significance of digestive manifestations in coronavirus disease 2019 (COVID-19) remain uncertain. We aimed to assess the prevalence, spectrum, severity, and significance of digestive manifestations in patients hospitalized with COVID-19. METHODS: Consecutive patients hospitalized with COVID-19 were identified across a geographically diverse alliance of medical centers in North America. Data pertaining to baseline characteristics, symptomatology, laboratory assessment, imaging, and endoscopic findings from the time of symptom onset until discharge or death were abstracted manually from electronic health records to characterize the prevalence, spectrum, and severity of digestive manifestations. Regression analyses were performed to evaluate the association between digestive manifestations and severe outcomes related to COVID-19. RESULTS: A total of 1992 patients across 36 centers met eligibility criteria and were included. Overall, 53% of patients experienced at least 1 gastrointestinal symptom at any time during their illness, most commonly diarrhea (34%), nausea (27%), vomiting (16%), and abdominal pain (11%). In 74% of cases, gastrointestinal symptoms were judged to be mild. In total, 35% of patients developed an abnormal alanine aminotransferase or total bilirubin level; these were increased to less than 5 times the upper limit of normal in 77% of cases. After adjusting for potential confounders, the presence of gastrointestinal symptoms at any time (odds ratio, 0.93; 95% CI, 0.76-1.15) or liver test abnormalities on admission (odds ratio, 1.31; 95% CI, 0.80-2.12) were not associated independently with mechanical ventilation or death. CONCLUSIONS: Among patients hospitalized with COVID-19, gastrointestinal symptoms and liver test abnormalities were common, but the majority were mild and their presence was not associated with a more severe clinical course.
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    Per-Oral Endoscopic Myotomy for Esophagogastric Junction Outflow Obstruction: A Multicenter Pilot Study
    (Elsevier, 2020) Jacobs, Chelsea C.; Perbtani, Yaseen; Yang, Dennis; Al-Haddad, Mohammad A.; Obaitan, Ite; Othman, Mohamed; Groth, Shawn; Sethi, Amrita; Agarunov, Emil; Repici, Alessandro; Maselli, Roberta; Galtieri, Alessia; Moremen, Jacob; Jenkins, Haley N.; Samarasena, Jason B.; Chang, Kenneth J.; Draganov, Peter V.; Medicine, School of Medicine
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    Radiomics Boosts Deep Learning Model for IPMN Classification
    (Springer, 2023) Yao, Lanhong; Zhang, Zheyuan; Demir, Ugur; Keles, Elif; Vendrami, Camila; Agarunov, Emil; Bolan, Candice; Schoots, Ivo; Bruno, Marc; Keswani, Rajesh; Miller, Frank; Gonda, Tamas; Yazici, Cemal; Tirkes, Temel; Wallace, Michael; Spampinato, Concetto; Bagci, Ulas; Radiology and Imaging Sciences, School of Medicine
    Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.