Head-to-head comparison of two angiography-derived fractional flow reserve techniques in patients with high-risk acute coronary syndrome: A multicenter prospective study.

FFRangio and QFR are angiography-based technologies that have been validated in patients with stable coronary artery disease. No head-to-head comparison to invasive fractional flow reserve (FFR) has been reported to date in patients with acute coronary syndromes (ACS). This study is a subset of a larger prospective multicenter, single-arm study that involved patients diagnosed with high-risk ACS in whom 30-70% stenosis was evaluated by FFR. FFRangio and QFR - both calculated offline by 2 different and blinded operators - were calculated and compared to FFR. The two co-primary endpoints were the comparison of the Pearson correlation coefficient between FFRangio and QFR with FFR and the comparison of their inter-observer variability. Among 134 high-risk ACS screened patients, 59 patients with 84 vessels underwent FFR measurements and were included in this study. The mean FFR value was 0.82 ± 0.40 with 32 (38%) being ≤0.80. The mean FFRangio was 0.82 ± 0.20 and the mean QFR was 0.82 ± 0.30, with 27 (32%) and 25 (29%) being ≤0.80, respectively. The Pearson correlation coefficient was significantly better for FFRangio compared to QFR, with R values of 0.76 and 0.61, respectively (p = 0.01). The inter-observer agreement was also significantly better for FFRangio compared to QFR (0.86 vs 0.79, p < 0.05). FFRangio had 91% sensitivity, 100% specificity, and 96.8% accuracy, while QFR exhibited 86.4% sensitivity, 98.4% specificity, and 93.7% accuracy. In patients with high-risk ACS, FFRangio and QFR demonstrated excellent diagnostic performance. FFRangio seems to have better correlation to invasive FFR compared to QFR but further larger validation studies are required

Atypical primary epithelioid hemangioendothelioma of the heart.

International audienceWe report an unusual case of primary cardiac epithelioid hemangioendothelioma (EHE) with atypical features, which was treated by orthoptic transplantation with a good outcome for 10 years despite recurrent pulmonary and nodal metastases. EHE is a rare vascular tumor that belongs to the group of malignant proliferations from the new World Health Organization classification of soft tissue tumors. EHE may harbor atypical features that confer a more aggressive course, albeit better than that of conventional angiosarcomas. Histological examination of the primary cardiac tumor revealed a proliferation of large epithelioid tumor cells presenting atypical features and a mitotic index of 3 mitoses per 10 high power fields. In contrast, pulmonary metastases exhibited typical features of EHE, and CD 34 and CD 31 immunostainings strongly stained cytoplasmic vascular lumen. In this report, we illustrate the potential aggressiveness of the atypical variant of EHE and suggest that transplantation might be considered as an alternative therapy in the treatment of EHE of the heart

A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis.

Multidisciplinary Heart Teams (HTs) play a central role in the management of valvular heart diseases. However, the comprehensive evaluation of patients' data can be hindered by logistical challenges, which in turn may affect the care they receive. This study aimed to explore the ability of artificial intelligence (AI), particularly large language models (LLMs), to improve clinical decision-making and enhance the efficiency of HTs. Data from patients with severe aortic stenosis presented at HT meetings were retrospectively analysed. A standardised multiple-choice questionnaire, with 14 key variables, was processed by the OpenAI Chat Generative Pre-trained Transformer (GPT)-4. AI-generated decisions were then compared to those made by the HT. This study included 150 patients, with ChatGPT agreeing with the HT's decisions 77% of the time. The agreement rate varied depending on treatment modality: 90% for transcatheter valve implantation, 65% for surgical valve replacement, and 65% for medical treatment. The use of LLMs offers promising opportunities to improve the HT decision-making process. This study showed that ChatGPT's decisions were consistent with those of the HT in a large proportion of cases. This technology could serve as a failsafe, highlighting potential areas of discrepancy when its decisions diverge from those of the HT. Further research is necessary to solidify our understanding of how AI can be integrated to enhance the decision-making processes of HTs