Introduction: focused cardiac ultrasound (FoCUS) can yield valuable information for decision-making. However, it is limited by the skills required to acquire and interpret high-quality images. Machine learning algorithms can help mitigate this gap by providing guidance for optimal image acquisition and interpretation. We aimed to evaluate the impact of an artificial intelligence (AI) assisted, FDA-cleared, FoCUS platform on clinical decision-making. Methods: This was a prospective trial with pre and post sequential allocation, conducted in two internal medicine departments. During the first 2 months, physicians with no formal echocardiography training used a common commercial FoCUS device as a complementary tool for their bedside patient evaluations. Then, during the following 4 months, an AI cloud-based platform was added, providing real-time feedback for image acquisition and AI-based echocardiographic results. The primary outcome was change in care following FoCUS, as reported by physicians after the examination and verified by assessors, which was analyzed by generalized linear mixed model accounting for physician and department effects. Results: 281 patients met the inclusion criteria and underwent FoCUS, 110 (39%) without AI assistance (control) and 171 with the AI. The most common reasons for FoCUS were worsening dyspnea (50%) and chest pain (20%). Non-significant trend was observed in physician-reported new echocardiographic findings towards the AI group (43% vs.34%, p=0.11). The FoCUS led to a change of care more often in the AI group (32% vs. 20%, adjusted OR 1.87, 95% CI 1.05-3.32). The number needed to scan with the AI to have one additional change in care was 9 (95% CI 5-57). In multivariate analysis, AI use was an independent predictor for a FoCUS-led change of care (adjusted OR 2.16, 95% CI 1.18-3.97), an effect that consisted in subgroup analysis and interrupted time-series model. AI also led to a lower rate of inpatient formal echocardiographic examinations (43% vs. 27%, p=0.006) Conclusion: AI-assisted FoCUS led to a higher rate of treatment plan changes, highlighting its potential to enhance bedside cardiac evaluation and optimize patient management.

Objectives: Chest X-ray (CXR) is routinely required for assessing Central Venous Catheter (CVC) tip position after insertion, but there is limited data as to the movement of the tip location during hospitalization. We aimed to assess the migration of Central Venous Catheter (CVC) position, as a significant movement of catheter tip location may challenge some of the daily practice after insertion. Design and settings: Retrospective, single-center study, conducted in the Intensive Care and Cardiovascular Intensive Care Units in Tel Aviv Sourasky Medical Center ’Ichilov’, Israel, between January and June 2019. Patients: We identified 101 patients with a CVC in the Right Internal Jugular (RIJ) with at least two CXRs during hospitalization. Measurements and results: For each patient, we measured the CVC tip position below the carina level in the first and all consecutive CXRs. The average initial tip position was 1.52 (±1.9) cm (mean±SD) below the carina. The maximal migration distance from the initial insertion position was 1.9 (±1) cm (mean±SD). During follow-up of 2 to 5 days, 92% of all subject’s CVCs remained within the range of the Superior Vena Cava to the top of the right atrium, regardless of the initial positioning. Conclusions: CVC tip position can migrate significantly during a patient’s early hospitalization period regardless of primary location, although for most patients it will remain within a wide range of the top of the right atrium and the middle of the Superior Vena Cava (SVC), if accepted as well-positioned.

Aims: Artificial intelligence (AI) has emerged as a promising tool for echocardiographic image analysis, potentially improving efficiency and reducing inter-observer variability. Real-world comparisons between AI-based analysis and human expert interpretation, and their correlation to clinical outcomes, remain limited. This study aimed to evaluate the correlation between AI-based echocardiographic analysis and human expert interpretation and to compare their association with one-year mortality in hospitalized patients. Methods and results: We conducted a retrospective analysis of 889 consecutive hospitalized patients who underwent a clinically indicated echocardiographic exam. All studies were read and analysed by both human echocardiographic experts and by commercially available AI software (Us2.ai). We performed correlation analysis of common echocardiographic variables obtained by human vs. AI and compared their performance in the prediction of 1-year mortality. Of the 889 patients, 731 (82%) patients (mean age 68 ± 16, 46% Females) had sufficient echocardiographic data to be included in the analysis. Most parameters exhibited a strong correlation between human and AI-derived measurements. AI-derived LVEF values were significantly higher than human estimates (mean difference 5.8%, P < 0.001). In a multivariable model, AI- and human-based mortality prediction were comparable (AUC 0.67 vs. 0.66, P = 0.86). When including AI-obtained automated left ventricular strain analysis, the AI-based model was superior to humans in predicting 1-year mortality (AUC 0.73 vs. 0.66, P = 0.048). Conclusion: AI-based echocardiographic analysis shows excellent correlation with human-derived measurements. Incorporating automated strain analysis resulted stronger association with mortality of the AI analysis compared to standard human analysis.

Background: Percutaneous left atrial appendage closure (LAAC) provides an alternative to oral anticoagulation (OAC) in atrial fibrillation (AF) patients who are at high bleeding risk. Prior studies have suggested sex-related differences in procedural outcomes, with women demonstrating higher peri-procedural complication rates. Data on long-term outcomes, however, remain inconsistent. Methods: We analyzed 407 consecutive patients with AF who underwent LAAC between 2010 and 2023 in four Israeli medical centers participating in the LEADER registry. Baseline characteristics, procedural data, and clinical outcomes were compared between men and women. The primary efficacy endpoint was ischemic stroke or systemic embolism at 1 year. The primary safety endpoint was a composite of all-cause mortality, procedural complications, or major bleeding at 1 year. Results: Of 407 patients, 285 (70%) were men and 122 (30%) were women. The mean age was 77 ± 8.4 years with similar CHA₂DS₂-VASc and HAS-BLED scores across sexes. Device implantation exceeded 99% in both sexes. Major peri-procedural complications occurred in 6.4% overall, without significant sex-based differences (men 7.0%, women 4.9%, p = 0.51). At 1-year follow-up, Kaplan–Meier estimates for the primary efficacy endpoint of ischemic stroke/systemic embolism (2.6%), the primary safety endpoint (19.2%), major bleeding (8.9%), and all-cause mortality (9.3%) were comparable between men and women (all p > 0.1). Conclusions: In contrast to prior large registries reporting higher peri-procedural risk in women, this real-world multicenter experience demonstrated no significant sex differences in either peri-procedural or long-term outcomes following LAAC. These findings support LAAC as an effective and safe stroke-prevention strategy in AF, irrespective of sex.

Background: Significant tricuspid regurgitation (TR) leads to right ventricular dysfunction and associated comorbidities. As the receiving chamber for regurgitant volume, the right atrium undergoes significant mechanical remodeling. This study investigated whether right atrial reservoir strain (RArS), a key marker of atrial mechanics, serves as a prognostic indicator for mortality in patients with moderate‐to‐severe TR. Methods: Clinical, laboratory, and echocardiographic data from 289 consecutive patients with TR (grade ≥+3) were analyzed. RArS was evaluated by 2‐dimensional speckle‐tracking echocardiography using semiautomated software (TOMTEC; Philips). The cohort was divided into 2 groups by RArS above or below the median value. Results: A total of 239 patients (mean age 82 years, 55% women) were included in the final study. Median RArS was 11.7%. Compared with patients with higher RArS, patients with RArS below median had a higher rate of atrial fibrillation and worse right ventricular function. Lower RArS was also associated with wider inferior vena cava diameter, abnormal cholestatic liver enzymes, and a higher rate of diuretic use. During a median follow‐up period of 26 months (interquartile range, 8–31), 99 (41%) patients died. Patients with RArS <11.7% had significantly lower survival rates (52% versus 30%; P <0.001). Multivariable analysis showed that lower RArS was independently associated with higher all‐cause mortality (odds ratio, 0.92 [95% CI, 0.85–0.99]; P =0.04). Conclusions: Impaired RArS assessed by echocardiography is independently associated with all‐cause mortality in patients with moderate–severe TR. Serial measurement of RArS can potentially aid in optimizing timing for intervention in patients with TR.