Background: When introducing new techniques, attention must be paid to learning curve. Besides quantitative outcomes, qualitative factors of influence should be taken into consideration. This retrospective cohort study describes the quantitative learning curve of complex endovascular aortic repair (EVAR) in a nonhigh-volume academic center and provides qualitative factors that were perceived as contributors to this learning curve. With these factors, we aim to aid in future implementation of new techniques.
Methods: All patients undergoing complex EVAR in the Leiden University Medical Center (LUMC) between July 2013 and April 2021 were included (n = 90). Quantitative outcomes were as follows: operating time, blood loss, volume of contrast, hospital stay, major adverse events (MAE), 30-day mortality, and complexity. Patients were divided into 3 temporal groups (n = 30) for dichotomous outcomes. Regression plots were used for continuous outcomes. In 2017, the treatment team was interviewed by an external researcher. These interviews were reanalyzed for factors that contributed to successful implementation.
Results: Length of hospital stay (P = 0.008) and operating time (P = 0.010) decreased significantly over time. Fewer cardiac complications occurred in the third group (3: 0% vs. 2: 17% vs. 1: 17%, P = 0.042). There was a trend of increasing complexity (P = 0.076) and number of fenestrations (P = 0.060). No significant changes occurred in MAE and 30-day mortality. Qualitative factors that, according to the interviewees, positively influenced the learning curve were as follows: communication, mutual trust, a shared sense of responsibility and collective goals, clear authoritative structures, mutual learning, and team capabilities.
Conclusions: In addition to factors previously identified in the literature, new learning curve factors were found (mutual learning and shared goals in the operating room (OR)) that should be taken into account when implementing new techniques.
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http://dx.doi.org/10.1016/j.avsg.2023.01.044 | DOI Listing |
Sci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Rheumatology and Immunology, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041, China.
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, which is significant for improving patient prognosis. Based on the NHANES for the periods of 2011-2012, 2013-2014, and 2015-2016, the study involved 11,366 participants, of whom 1,434 reported a diagnosis of OA.
View Article and Find Full Text PDFBMC Musculoskelet Disord
January 2025
Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
Background: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.
Methods: Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g.
World Neurosurg
January 2025
Department of Neurology, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, China. Electronic address:
Objective: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning algorithms.
Methods: Employing a retrospective cohort study design, this study collected patients with ruptured IA who received endovascular treatment at Jingzhou First People's Hospital during the inclusion period from September 2022 to December 2023. The entire dataset was randomly split into training and testing dataset with a 7:3 ratio.
J Clin Neurosci
January 2025
Division of Neurosurgery, Department of Surgery, National University Hospital, National University Health System, Singapore.
Ventriculoperitoneal shunt (VPS) insertion is a neurosurgical procedure done routinely for managing hydrocephalus. However, the technique of shunt insertion remains controversial. In this study, we retrospectively compared the accuracy of shunt placement using ultrasound (US) guidance to freehand insertion.
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