Background: Intestinal leak is a potentially lethal complication of Roux en-Y gastric bypass (GBP). Identification of patients at high risk for leak may reduce complication rates of surgeons early in the procedure learning curve.
Methods: A total of 3073 patients who underwent GBP were analyzed using univariate and multivariate logistic regression analyses of the following preoperative factors: hypertension (HTN), diabetes mellitus (DM), sleep apnea (SA), age, gender, weight, body mass index (BMI), and surgery type. Multivariate logistic regression analysis was performed for each procedure type.
Results: There were 48 (1.5%) deaths. Independent risk factors for death included leak, weight, procedure type, and HTN. A total of 102 (3.2%) leaks were found. Independent factors for leak included age, male gender, SA, and procedure type.
Conclusion: The data suggests that older, heavier male patients with multiple comorbid conditions are at increased risk for leak and mortality. Surgeons early in their learning curve should avoid these high-risk patients to reduce complications.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00464-003-8926-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Cerebrospinal Fluid Leak Prevention in Intradural Spine Surgery: A Long Series Analysis of Closure with Non-Penetrating Titanium Clips.
Brain Sci
December 2024
Department of Neurosurgery, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy.
: Postoperative cerebrospinal fluid (CSF) fistulas remain a significant concern in spinal neurosurgery, particularly following dural closure. The incidence of dural tears during spinal surgery is estimated between 1.6% and 10%.
View Article and Find Full Text PDFInterpretable deep learning for acoustic leak detection in water distribution systems.
Water Res
December 2024
School of Environment, Tsinghua University, 100084, Beijing, PR China.
Article Synopsis
- Leak detection is essential for water safety and conservation, but current machine learning approaches lack interpretability, impacting their practical credibility.
- The study presents a new model called the multi-channel convolution neural network (MCNN), which outperforms the existing frequency convolutional neural network (FCNN) with a 95.4% accuracy in real-world applications, and includes the use of MGrad-CAM for visualizing decision-making processes.
- Findings reveal that leak acoustic signals can be clustered into patterns, with factors such as pressure and proximity influencing the signal characteristics, ultimately enhancing the model's accuracy in leak detection.
Risk Factors for Anastomotic Leakage: A Comprehensive Single-Center Analysis of Colorectal Anastomoses for Ovarian and Gastrointestinal Cancers.
Ann Surg Oncol
January 2025
Department Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
Background: Anastomotic leakage (AL) is a major complication in colorectal surgery, particularly following rectal cancer surgery, necessitating effective prevention strategies. The increasing frequency of colorectal resections and anastomoses during cytoreductive surgery (CRS) for peritoneal carcinomatosis further complicates this issue owing to the diverse patient populations with varied tumor distributions and surgical complexities. This study aims to assess and compare AL incidence and associated risk factors across conventional colorectal cancer surgery (CRC), gastrointestinal CRS (GI-CRS), and ovarian CRS (OC-CRS), with a secondary focus on evaluating the role of protective ostomies.
View Article and Find Full Text PDFMachine learning based prediction model for bile leak following hepatectomy for liver cancer.
HPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States. Electronic address:
Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.
Risk Factors and Treatment Strategies for Subcutaneous Effusion Secondary to Cerebrospinal Fluid Leakage After Craniotomy.
J Craniofac Surg
January 2025
Department of Neurosurgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objective: To confirm the incidence of subcutaneous effusion secondary to cerebrospinal fluid leakage after craniotomy, analyze the risk factors for cerebrospinal fluid leakage leading to subcutaneous effusion, summarize the underlying causes of its occurrence and explore the corresponding treatment strategies.
Methods: A retrospective analysis was conducted on 757 patients who underwent craniotomy at our hospital from January to December 2023. The authors documented the sex, age, surgical characteristics, and history of chronic diseases for all patients, including those who developed subcutaneous effusion secondary to cerebrospinal fluid leakage.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!