ABSTRACT
BACKGROUND: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
ABSTRACT
BACKGROUND: Infective endocarditis (IE) management is challenging, usually requiring multidisciplinary collaboration from cardiologists, infectious disease specialists, interventional cardiologists, and cardiovascular surgeons, as more than half of the cases will require surgical procedures. Therefore, it is essential for all healthcare providers involved in managing IE to understand the disease's characteristics, potential complications, and treatment options. While systemic embolization is one of the most frequent complications of IE, the coronary localization of emboli causing acute myocardial infarction (AMI) is less common, with an incidence ranging from 1% to 10% of cases, but it has a much higher rate of morbidity and mortality. There are no guidelines for this type of AMI management in IE. METHODS: This narrative review summarizes the current knowledge regarding septic coronary embolization in patients with IE. Additionally, this paper highlights the diagnosis and management challenges in such cases, particularly due to the lack of protocols or consensus in the field. RESULTS: Data extracted from case reports indicate that septic coronary embolization often occurs within the first two weeks of the disease. The aortic valve is most commonly involved with vegetation, and the occluded vessel is frequently the left anterior descending artery. Broad-spectrum antibiotic therapy followed by targeted antibiotic therapy for infection control is essential, and surgical treatment offers promising results through surgical embolectomy, concomitant with valve replacement or aspiration thrombectomy, with or without subsequent stent insertion. Thrombolytics are to be avoided due to the increased risk of bleeding. CONCLUSIONS: All these aspects should constitute future lines of research, allowing the integration of all current knowledge from multidisciplinary team studies on larger patient cohorts and, subsequently, creating a consensus for assessing the risk and guiding the management of this potentially fatal complication.
ABSTRACT
Water retention and intercompartmental redistribution occur frequently in association with adverse postoperative outcomes, yet the available strategies for non-invasive assessment are limited. One such approach for evaluating body water composition in various circumstances is bio-electrical impedance analysis (BIA). This study aims to appraise the usefulness of the Body Composition Monitor (BCM, Fresenius Medical Care, Germany) in assessing body fluid composition and intercompartmental shifts before and after open major abdominal surgery. This prospective, clinician blinded observational study enrolled all the patients scheduled consecutively for elective major open abdominal surgery during a 1-year period starting from January 1st, 2016. BIA parameters-total body water (TBW), extracellular water (ECW), intracellular water (ICW), absolute fluid overload (AFO), and relative fluid overload (RFO) were measured before and after surgery. The results were compared with fluid balance and outcome parameters such as organ dysfunction, ICU-and hospital length of stay (-LOS). The study population included 71 patients aged 60.2 ± 12 of whom 60.6% men and with a BMI of 26.3 ± 5.1 kg/m2. Postoperative acute kidney injury, respiratory dysfunction, and infections occurred in 14.0%, 19.7% and 28.1% of cases, respectively. The median LOS in ICU was 20 h and the hospital-LOS was 10 days. Positive intraoperative fluid balance (2.4 ± 1.0 L) resulted in a significant increase of TBW (1.4 ± 2.4 L) and of ECW (1.4 ± 1.2 L). Intraoperative fluid balance significantly correlated with TBW change (r = 0.23, p = 0.04) and with AFO change (r = 0.31, p < 0.01). A significant correlation was found between pre- and postoperative AFO and RFO on one hand, and ICU-LOS on the other. BIA may be a useful tool for the perioperative assessment of volume status.