Risk Factors for Additional Surgery after Iatrogenic Perforations due to Endoscopic Submucosal Dissection.
Gi Jun KimSung Min ParkJoon Sung KimJeong Seon JiByung Wook KimHwang ChoiPublished in: Gastroenterology research and practice (2017)
Objectives. Endoscopic resection (ER) is commonly performed to treat gastric epithelial neoplasms and subepithelial tumors. The aim of this study was to predict the risk factors for surgery after ER-induced perforation. Methods. We retrospectively reviewed the data on patients who received gastric endoscopic submucosal dissection (ESD) or endoscopic mucosal resection (EMR) between January 2010 and March 2015. Patients who were confirmed to have perforation were classified into surgery and nonsurgery groups. We aimed to determine the risk factors for surgery in patients who developed iatrogenic gastric perforations. Results. A total of 1183 patients underwent ER. Perforation occurred in 69 (5.8%) patients, and 9 patients (0.8%) required surgery to manage the perforation. In univariate analysis, anterior location of the lesion, a subepithelial lesion, two or more postprocedure pain killers within 24 hrs, and increased heart rate within 24 hrs after the procedure were the factors related to surgery. In logistic regression analysis, the location of the lesion at the anterior wall and using two or more postprocedure pain killers within 24 hrs were risk factors for surgery. Conclusion. Most cases of perforations after ER can be managed conservatively. When a patient requires two or more postprocedure pain killers within 24 hrs and the lesion is located on the anterior wall, early surgery should be considered instead of conservative management.
Keyphrases
- minimally invasive
- coronary artery bypass
- endoscopic submucosal dissection
- ejection fraction
- heart rate
- newly diagnosed
- surgical site infection
- chronic pain
- pain management
- heart rate variability
- chronic kidney disease
- breast cancer cells
- estrogen receptor
- patient reported outcomes
- machine learning
- deep learning
- atrial fibrillation
- artificial intelligence