Objective: To determine the relationship between obesity and the risk of AKI after cardiac surgery (CS-AKI) in a cohort study.
Methods: A total of 1,601 patients undergoing cardiac surgery were collected and their incidence of CS-AKI was recorded. They were divided into underweight, normal weight, overweight, and obese groups. Logistic regression was used to estimate the association between BMI (body mass index) and CS-AKI risk. Then, a meta-analysis of published cohort studies was conducted to confirm this result using PubMed and Embase databases.
Results: A significant association was observed in this independent cohort after adjusting age, gender, hypertension and New York Heart Association classification (NYHA) class. Compared with normal BMI group (18.5 ≤ BMI < 24.0), the individuals with aberrant BMI level had an increased AKI risk (OR: 1.68, 95% CI: 1.01-2.78) for BMI < 18.5 group and (OR: 1.43, 95% CI: 0.96-2.15) for BMI ≥ 28.0. Interestingly, the U-shape curve showed the CS-AKI risk reduced with the increasing of BMI when BMI ≤ 24.0. As BMI increases with BMI > 24.0, the risk of developing CS-AKI increased significantly. In the confirmed meta-analysis, compared with normal weight, overweight group with cardiac surgery had higher AKI risk (OR: 1.28, 95% CI: 1.16-1.41, = 0.49). The similar association was found in obesity subgroup (OR: 1.79, 95% CI: 1.57-2.03, = 0.42).
Conclusion: In conclusion, the results suggested that abnormal BMI was a risk factor for CS-AKI independently.
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http://dx.doi.org/10.3389/fendo.2020.534294 | DOI Listing |
Several techniques for the surgical correction of congenital supravalvular aortic stenosis have been devised. We describe the step-by-step surgical approach of a slide aortoplasty to correct localized supravalvular aortic stenosis in a 3-year-old child with Williams syndrome.
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Key Laboratory of Drug Targets and Translational Medicine for Cardio-cerebrovascular Diseases, Key Laboratory of Targeted Intervention of Cardiovascular Disease, Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Jiangsu, China (X.T., X.L., X.S., Y. Zhang, Y. Zu, Q.F., L.H., S.S., F.C., L.X., Y.J.).
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Metabolic and Immune Diseases Department, Biomedical Research Institute Sols-Morreale (IIBM), National Research Council (CSIC), Autonoma University of Madrid, Spain (T.A.-G., S.M.-T., R.C.-M., S.U.-B., S.M.-P.).
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Service de néphrologie, Département de médecine, Hôpitaux universitaires de Genève, Genève 14.
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Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
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