ABSTRACT
BACKGROUND: Emergency general surgery (EGS) encompasses a wide range of acute surgical conditions that pose significant risks to patient life and well-being. Understanding the factors that contribute to short-term complications in geriatric patients undergoing EGS is crucial for improving patient outcomes. This retrospective single-center cohort study aimed to evaluate the impact of various variables on short-term complications in geriatric patients undergoing EGS. METHODS: A total of 212 patients aged 65 and above who underwent emergency abdominal surgery between 2017 and 2018 were included in the study. The analysis focused on several variables, including age, sex, body mass index (BMI), beta-blocker use, open abdomen treatment, blood transfusions, anticoagulant therapy, and vasopressor use. Univariate and multivariate analyses were conducted to assess the association between these variables and short-term complications. RESULTS: Among the analyzed variables, blood transfusions and vasopressor use demonstrated a statistically significant association with short-term complications. Patients who received blood transfusions had a significantly higher risk of complications, with an odds ratio (OR) of 3.01 (95% confidence interval, CI: 1.28-7.06, p-value = 0.011). Similarly, the use of vasopressors was strongly correlated with increased short-term complications, with an OR of 14.61 (95% CI: 4.86-43.89, p-value < 0.001). CONCLUSION: These findings emphasize the importance of minimizing blood transfusions and careful consideration of vasopressor use in geriatric patients undergoing EGS to reduce the risk of short-term complications. Further research is warranted to explore additional factors and optimize perioperative management strategies to improve outcomes in this vulnerable patient population.
ABSTRACT
Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students' skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.
