Objective: Enhanced Recovery ERP protocols (ERP) have improved surgical outcomes in patients undergoing elective colon cancer (CC) surgery; however, efficacy in different populations may vary. We examined the impact of an ERP in a population with high rates of obesity and multiple comorbidities.
Methods: We performed a retrospective analysis of factors associated with postoperative complications (PoC) and length of stay (LOS) following CC surgery from 2011 to 2019 in a 5-hospital healthcare system which serves a population with higher rates of obesity (body mass index ≥30kg/m) and multi-comorbidities, as compared to published studies. Univariable and multivariable analyses were performed.
Results: A total of 408 elective CC surgery patients with complete oncologic surgical data were identified. Of these, 191 (46.81%) were under ERP. Factors independently associated with PoC included obesity (OR=1.66, P=.029), laparoscopic (OR=.52, P=.020), and hybrid (OR=.38, P=.012) versus open surgery and ASA (American Society of Anesthesiologists) class ≥3 (OR=1.98, P=.006). ERP did not impact PoC but was associated with a reduction in LOS (β=-1.02 days, 95%CI: -1.75 - -.30, P=.006). ERP had an impact on LOS in both the non-obese and obese groups (P<.001 and P=.034, respectively). PoC significantly increased LOS (β=6.67 days, 95%CI: 5.41-7.03, P<.001).
Conclusions: Following elective CC surgery, obesity and medical comorbidities were associated with increased PoC and in turn, as expected, increased LOS. ERP was associated with a reduction in LOS in both obese and non-obese patients. In high-risk populations, application of ERP may be particularly important to optimize surgical outcomes following CC surgery.
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http://dx.doi.org/10.1177/00031348221121540 | DOI Listing |
J Health Organ Manag
January 2025
University of Malta, Msida, Malta.
Purpose: This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase. The aim is to showcase how this fusion can help tackle healthcare inequalities, enhance accessibility and support long-term sustainability.
Design/methodology/approach: Adopting a viewpoint approach, the study leverages existing literature and case studies to analyze the intersection of CSR and AI.
BMC Nurs
January 2025
Department of Nursing, Faculty of Nursing and Midwifery, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Background: Compassion Competence and the ability to strive to understand the suffering of patients in psychiatric ward is essential for nurses to establish effective therapeutic communication in the process of their recovery. Patient Safety Competency is of great importance for nurses to prevent adverse events and minimize errors. This study aimed to investigate the relationship between Compassion Competence and Patient Safety Competency in nurses working in psychiatric wards of Shiraz University of Medical Sciences affiliated hospitals in 2024.
View Article and Find Full Text PDFCommunity Ment Health J
January 2025
School of Psychology and Public Health, La Trobe University, Plenty Road & Kingsbury Drive, Bundoora, VIC, 3086, Australia.
Engagement with traditional mental health services can be particularly challenging for young people experiencing severe and complex mental health problems. Assertive community treatment-based services providing mobile outreach, such as Intensive Mobile Youth Outreach Services (IMYOS), operate across Australia to support these young people's mental health needs in the transition to adulthood. Past research on IMYOS has focused on quantitative outcome measures, and young people's experiences of this type of model are poorly understood.
View Article and Find Full Text PDFSci Rep
January 2025
Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Pediatric Hematology and Oncology, University Hospital Bonn, Bonn, Germany.
This study aims to explore the long-term follow-up needs and motivations of childhood and adolescent cancer survivors and their parents to attend follow-up care in Germany, given the inconsistent adherence to national follow-up guidelines. We developed interview guidelines based on the Theory of Planned Behavior and the stereotype priming model to explore motivations and barriers related to follow-up care. We conducted a total of 36 episodic narrative interviews with adolescent (ages 13-17) and adult (ages 18-45) survivors of pediatric cancer, as well as their parents.
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