Automatic quantitative measurement of left atrial pressure using mitral regurgitation spectrum: clinical study on comparison with floating catheter.

INTRODUCTION: To explore how to measure LAPEq accurately and quantitatively, that is, the left atrial pressure (LAP) measured and calculated by equation method using mitral regurgitation spectrum. METHODS: The mitral regurgitation spectrum, pulmonary arteriolar wedge pressure (PAWP) and invasive arterial systolic pressure of radial artery of 28 patients were collected simultaneously, including 3 patients with rheumatic heart disease, 15 patients with mitral valve prolapse and 10 patients with coronary artery bypass grafting, patients with moderate or above aortic stenosis were excluded. LAPBp (Doppler sphygmomanometer method), LAPEq (Equation method) and LAPC (Catheter method) were measured synchronously, and the measurement results of the three methods were compared and analyzed. A special intelligent Doppler spectrum analysis software was self-designed to accurately measure LAPEq. This study had been approved by the ethics committee of the Northern Theater General Hospital (K-2019-17), and applied for clinical trial (No. Chictr 190023812). RESULTS: It was found that there was no significant statistical difference between the measurement results of LAPC and LAPEq (t = 0.954, P = 0.348), and significant correlation between the two methods [r = 0.908(0.844, 0.964), P < 0.001]. Although the measurement results of LAPC and LAPBP are consistent in the condition of non-severe eccentric mitral regurgitation, there are significant differences in the overall case and weak correlation between the two methods [r = 0.210, (-0.101, 0.510), P = 0.090]. In MVP patients with P1 or P3 prolapse, the peak pressure difference of MR was underestimated due to the serious eccentricity of MR, which affected the accuracy of LAPBP measurement. CONCLUSIONS: It was shown that there is a good correlation between LAPEq and LAPC, which verifies that the non-invasive and direct quantitative measurement of left atrial pressure based on mitral regurgitation spectrum is feasible and has a good application prospect.

Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education.

The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors. Yet, graduate education remains largely focused on individual achievement within a single scientific domain, with little training in team-based, interdisciplinary data-oriented approaches designed to translate scientific data into new solutions to today's critical challenges. In this article, we propose a new pedagogy for graduate education: data-centered learning for the domain-data scientist. Our approach is based on four tenets: (1) Graduate training must incorporate interdisciplinary training that couples the domain sciences with data science. (2) Graduate training must prepare students for work in data-enabled research teams. (3) Graduate training must include education in teaming and leadership skills for the data scientist. (4) Graduate training must provide experiential training through academic/industry practicums and internships. We emphasize that this approach is distinct from today's graduate training, which offers training in either data science or a domain science (e.g., biology, sociology, political science, economics, and medicine), but does not integrate the two within a single curriculum designed to prepare the next generation of domain-data scientists. We are in the process of implementing the proposed pedagogy through the development of a new graduate curriculum based on the above four tenets, and we describe herein our strategy, progress, and lessons learned. While our pedagogy was developed in the context of graduate education, the general approach of data-centered learning can and should be applied to students and professionals at any stage of their education, including at the K-12, undergraduate, graduate, and professional levels. We believe that the time is right to embed data-centered learning within our educational system and, thus, generate the talent required to fully harness the potential of big data.