Postpolypectomy Bleeding Prevention and More Complete Precancerous Colon Polyp Removal With Endoscopic Mucosal Stripping (EMS)

Background and Aims: Postpolypectomy bleeding and incomplete polyp removal are important complication and quality concerns of colonoscopy for colon cancer prevention. We investigated if endoscopic mucosal stripping (EMS) as a technical modification of traditional cold snare polypectomy to avoid submucosal injury during removal of non-pedunculated colon polyps could prevent postpolypectomy bleeding and facilitate complete polyp removal.Methods: This is an Internal Review Board exemption-granted retrospective analysis of 5,142 colonoscopies with snare polypectomy performed by one of the authors (ZJC) at Minnesota Gastroenterology ambulatory endoscopy centers during a 12-year period divided into pre-EMS era (2005–2012, n = 2,973) and EMS era (2013–2016, n = 2169) with systemic adoption of EMS starting 2013. Change in postpolypectomy bleeding rate before and after EMS adoption and EMS polypectomy completeness were evaluated.Results: Zero postpolypectomy bleeding case was found during EMS era (rate 0%) compared with 10 bleeding cases during pre-EMS era (rate 0.336%). This difference was statistically significant (P = 0.0055) and remained so after excluding 2 bleeding cases of pedunculated polyps (P = 0.012). All bleeding cases involved hot snare polypectomy. Histological examination of the involved polyps showed substantial submucosal vascular damage in contrast to a remarkable paucity of submucosa in comparable advanced polyps removed using EMS. Both biopsy and follow-up colonoscopy examination of the polypectomy sites confirmed that EMS more completely removed non-pedunculated advanced polyps.Conclusions: EMS polypectomy was effective in preventing postpolypectomy bleeding and facilitated complete polyp removal

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. METHODS: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). FINDINGS: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. INTERPRETATION: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. FUNDING: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort CollaborativeResearch in context

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438