Objective: To assess the perioperative morbidity and early outcome of buccal mucosal graft (BMG) urethroplasty in patients with urethral stricture awaiting renal transplantation.
Methods: Thirteen patients awaiting renal transplantation underwent BMG urethroplasty for long anterior urethral stricture between June 2011 and March 2013. The management issues, complications, and outcome of the BMG urethroplasty in this cohort of patients were studied.
Results: Mean age of the patient was 38.7 ± 12.7 years. History of urethral manipulation was present in 8 patients. Mean stricture length was 6.92 ± 2.90 cm. Mean serum creatinine of the patient was 8.1 ± 3.6 mg%. Three patients required oral exploration for bleeding. Two patients had urinary extravasation, 3 patients had infected hematoma, and 1 patient developed dry gangrene of the glans. One patient had sepsis due to pyonephrosis in the postoperative period and succumbed to it. Mean follow-up of the patients was 34.54 ± 6.46 months. Three patients underwent VIU for recurrence of the stricture in the follow-up. At 3-month follow-up mean Qmax was 23.8 mL/sec, whereas at 6-month and 1-year follow-up, Qmax was 23.6 and 23.4 mL/sec, respectively.
Conclusion: This study shows a relatively higher complication rate of urethroplasty in prerenal transplant patients. Although the number of cases is too small to arrive at any definite conclusion, this study does gives an insight into the management issues, complications, and success of urethroplasty in this group of patients.
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http://dx.doi.org/10.1016/j.transproceed.2015.12.012 | DOI Listing |
JMIR Form Res
January 2025
Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice.
Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions.
JMIR Form Res
January 2025
Northwestern Medicine, Chicago, IL, United States.
Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies.
Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration.
JMIR Serious Games
January 2025
School of Computing, Engineering and Mathematical Sciences, Optus Chair Digital Health, La Trobe University, Melbourne, Australia.
Background: This review explores virtual reality (VR) and exercise simulator-based interventions for individuals with attention-deficit/hyperactivity disorder (ADHD). Past research indicates that both VR and simulator-based interventions enhance cognitive functions, such as executive function and memory, though their impacts on attention vary.
Objective: This study aimed to contribute to the ongoing scientific discourse on integrating technology-driven interventions into the management and evaluation of ADHD.
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
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