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From modelling to meaning: Contextualising pelvic sepsis risk after robotic TME—An external lens on the EUREKA benchmark.

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Titel: From modelling to meaning: Contextualising pelvic sepsis risk after robotic TME—An external lens on the EUREKA benchmark.
Autoren: Cheng, Yanqi1,2 (AUTHOR), Miao, Jia2 (AUTHOR), Yan, Yuehua2 (AUTHOR) yanyuehua@njucm.edu.cn
Quelle: Colorectal Disease. Sep2025, Vol. 27 Issue 9, p1-2. 2p.
Schlagwörter: *PREDICTION algorithms, *SURGICAL anastomosis, *MEDICAL research, *TREATMENT effectiveness, *SURGERY, *NEOADJUVANT chemotherapy, *PELVIC inflammatory disease
Abstract: The article discusses the findings of the multicentre EUREKA study, which provides contemporary estimates of pelvic sepsis (14%) and anastomotic leakage (AL, 7%) following robotic total mesorectal excision (R-TME) after the learning curve. It highlights the construction of a prediction model aimed at individualizing risk counseling but raises concerns regarding residual confounding from neoadjuvant therapy sequencing, limited generalizability to high-BMI populations, missing data on anastomotic level and perfusion assessment, and the need for more dynamic, procedure-specific risk calculators. The authors suggest that addressing these issues could enhance the model's clinical utility and reduce complications associated with R-TME. [Extracted from the article]
Datenbank: Academic Search Index
Beschreibung
Abstract:The article discusses the findings of the multicentre EUREKA study, which provides contemporary estimates of pelvic sepsis (14%) and anastomotic leakage (AL, 7%) following robotic total mesorectal excision (R-TME) after the learning curve. It highlights the construction of a prediction model aimed at individualizing risk counseling but raises concerns regarding residual confounding from neoadjuvant therapy sequencing, limited generalizability to high-BMI populations, missing data on anastomotic level and perfusion assessment, and the need for more dynamic, procedure-specific risk calculators. The authors suggest that addressing these issues could enhance the model's clinical utility and reduce complications associated with R-TME. [Extracted from the article]
ISSN:14628910
DOI:10.1111/codi.70215