Objective: Patient selection for robotically assisted mitral valve repair remains controversial. We assessed outcomes of a conservative screening algorithm developed to select patients with degenerative mitral valve disease for robotic surgery.
Methods: From January 2014 to January 2019, a screening algorithm that included transthoracic echocardiography and computed tomography scanning was rigorously applied by 3 surgeons to assess candidacy of 1000 consecutive patients with isolated degenerative mitral valve disease (age 58 ± 11 years, 67% male) for robotic surgery. Screening results and hospital outcomes of those selected for robotic versus sternotomy approaches were compared.
Results: With application of the screening algorithm, 605 patients were selected for robotic surgery. Common reasons for sternotomy (n = 395) were aortoiliac atherosclerosis (n = 74/292, 25%), femoral artery diameter <7 mm (n = 60/292, 20%), mitral annular calcification (n = 83/390, 21%), aortic regurgitation (n = 100/391, 26%), and reduced left ventricular function (n = 126/391, 32%). Mitral valve repair was accomplished in 996. Compared with sternotomy, patients undergoing robotic surgery had less new-onset atrial fibrillation (n = 144/582, 25% vs n = 125/373, 34%; P = .002), fewer red blood cell transfusions (n = 61/601, 10% vs 69/395, 17%; P < .001), and shorter hospital stay (5.2 ± 2.9 days vs 5.9 ± 2.1 days; P < .001). No hospital deaths occurred, and occurrence of postoperative stroke in the robotic (n = 3/605, 0.50%) and sternotomy (n = 4/395, 1.0%; P = .3) groups was similar.
Conclusions: This conservative screening algorithm qualified 60% of patients with isolated degenerative mitral valve disease for robotic surgery. Outcomes were comparable with those obtained with sternotomy, validating this as an approach to select patients for robotic mitral valve surgery.
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http://dx.doi.org/10.1016/j.jtcvs.2020.12.036 | DOI Listing |
Itching tends to worsen at night in patients with itchy skin diseases, such as atopic dermatitis. Unconscious scratching during sleep can exacerbate symptoms, cause sleep disturbances, or reduce quality of life. Therefore, evaluating nocturnal scratching behaviour is important for better patient care.
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January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
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January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
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January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
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