Predictive Factors for Anastomotic Leakage after Laparoscopic and Open Total Gastrectomy: A Systematic Review.
Umberto BracaleRoberto PeltriniMarcello De LucaMariangela IlardiMaria Michela Di NuzzoAlberto SartoriMaurizio SodoMichele DanziFrancesco CorcioneCarlo De WerraPublished in: Journal of clinical medicine (2022)
The aim of this systematic review is to identify patient-related, perioperative and technical risk factors for esophago-jejunal anastomotic leakage (EJAL) in patients undergoing total gastrectomy for gastric cancer (GC). A comprehensive literature search of PubMed/MEDLINE, Embase and Scopus databases was performed. Studies providing factors predictive of EJAL by uni- and multivariate analysis or an estimate of association between EJAL and related risk factors were included. All studies were assessed for methodological quality, and a narrative synthesis of the results was performed. A total of 16 studies were included in the systematic review, with a total of 42,489 patients who underwent gastrectomy with esophago-jejunal anastomosis. Age, BMI, impaired respiratory function, prognostic nutritional index (PNI), alcohol consumption, chronic renal failure, diabetes and mixed-type histology were identified as patient-related risk factors for EJAL at multivariate analysis. Likewise, among operative factors, laparoscopic approach, anastomosis type, additional organ resection, blood loss, intraoperative time and surgeon experience were found to be predictive factors for the development of EJAL. In clinical setting, we are able to identify several risk factors for EJAL. This can improve the recognition of higher-risk patients and their outcomes.
Keyphrases
- systematic review
- end stage renal disease
- patients undergoing
- risk factors
- chronic kidney disease
- alcohol consumption
- ejection fraction
- newly diagnosed
- meta analyses
- type diabetes
- prognostic factors
- peritoneal dialysis
- case report
- body mass index
- rectal cancer
- patient reported outcomes
- machine learning
- skeletal muscle
- mass spectrometry
- deep learning
- insulin resistance
- big data
- liquid chromatography