A study on the global patterns in the design and development of ventricular assist devices: a visualization approach - Ajay K. Sood; A. K. Prasada Rao (Frontiers in Cardiovascular Medicine)
IntroductionVentricular assist devices (VADs) are lifesavers for people with advanced heart failure. The design of these devices has undergone drastic changes over time with the latest designs being far more efficient, small, lightweight, and more user-friendly. This study aims to analyze publications using bibliometric analysis and see the progress and identify key themes, trends, and collaboration networks.MethodData relevant to this study were obtained from Scopus and Web of Science databases from 1990 to 2023. Data analysis was done using Biblioshiny which is an R-based software and is part of RStudio and Microsoft Excel to analyze collaboration between countries, authors, keyword analysis, trend topics, and evolution of various themes related to this study.ResultsA total of 489 published documents were analyzed, and these documents were from 158 different sources and 1,753 authors. The top contributing journals were Artificial Organs and Asaio Journal with 116 and 81 publications, respectively. The top contributing authors in terms of total documents were Nose Y (35) and Throckmorton A (30) and in terms of total citations were Pagani F (2005) and Mehra M (1952). Top countries include the USA, China, and Germany. The trend topics include miniaturization, machine learning, wireless, shear flow, and fiber-optic sensors.DiscussionThe latest technological advancements in VAD design are making them a more suitable choice for a large number of patients. This bibliometric work will aid in identifying the newest trends and developments in this field and highlight the areas where more research is needed. These data are crucial for driving innovation in this field and for improving the lives of patients who depend on VADs. Future studies can be conducted to explore the use of artificial intelligence and machine learning that can learn from data about patients and then adapt as per the requirements of the patients.