Background: In the last decade, percutaneous coronary intervention (PCI) has evolved toward the treatment of complex disease in patients with multiple comorbidities. Whilst there are several definitions of complexity, it is unclear whether there is agreement between cardiologists in classifying complexity of cases. Inconsistent identification of complex PCI can lead to significant variation in clinical decision-making.
Aim: This study aimed to determine the inter-rater agreement in rating the complexity and risk of PCI procedures.
Method: An online survey was designed and disseminated amongst interventional cardiologists by the European Association of Percutaneous Cardiovascular Intervention (EAPCI) board. The survey presented four patient vignettes, with study participants assessing these cases to classify their complexity.
Results: From 215 respondents, there was poor inter-rater agreement in classifying the complexity level (k = 0.1) and a fair agreement (k = 0.31) in classifying the risk level. The experience level of participants did not show any significant impact on the inter-rater agreement of rating the complexity level and the risk level. There was good level of agreement between participants in terms of rating 26 factors for classifying complex PCI. The top five factors were (1) impaired left ventricular function, (2) concomitant severe aortic stenosis, (3) last remaining vessel PCI, (4) requirement fort calcium modification and (5) significant renal impairment.
Conclusion: Agreement among cardiologists in classifying complexity of PCI is poor, which may lead to suboptimal clinical decision-making, procedural planning as well as long-term management. Consensus is needed to define complex PCI, and this requires clear criteria incorporating both lesion and patient characteristics.
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http://dx.doi.org/10.1002/ccd.30684 | DOI Listing |
Microb Genom
January 2025
GMT Science 75 route de Lyons-La-Foret, Rouen F-76000, France.
Microbiome profiling tools rely on reference catalogues, which significantly affect their performance. Comparing them is, however, challenging, mainly due to differences in their native catalogues. In this study, we present a novel standardized benchmarking framework that makes such comparisons more accurate.
View Article and Find Full Text PDFDiscov Oncol
January 2025
Department of Medical Imaging, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen Longhua District Central Hospital, Shenzhen, 518110, China.
Background: Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.
Methods: This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM.
Curr Microbiol
January 2025
College of Ocean and Earth Sciences, Xiamen University, Fujian, 361005, China.
The fish intestine is a complex ecosystem where microbial communities are dynamic and influenced by various factors. Preservation conditions during field collection can introduce biases affecting the microbiota amplified during sequencing. Therefore, establishing effective, standardized methods for sampling fish intestinal microbiota is crucial.
View Article and Find Full Text PDFJ Gen Virol
January 2025
Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for addressing this challenge, as advances in methods and computational power have dramatically improved the performance of models and led to their widespread adoption across industries and disciplines. Nascent applications of machine learning technology to virus research have now expanded, providing new tools for handling large-scale datasets and leading to a reshaping of existing workflows for phenotype prediction, phylogenetic analysis, drug discovery and more.
View Article and Find Full Text PDFOcul Immunol Inflamm
January 2025
Francis I. Proctor Foundation, University of California, San Francisco, California, USA.
Purpose: To report on the clinical and cytopathological features of metastatic lung adenocarcinoma to the eye masquerading as an intermediate uveitis.
Methods: Retrospective chart review.
Results: A 63-year-old woman with a history of lung adenocarcinoma in remission presented with progressive vision loss and floaters in the right eye.
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