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

BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.

ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story?

Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic worldwide triggering extensive debate about its predictive power, its potential uses, and its wider implications. Recent publications have demonstrated that ChatGPT can correctly answer questions from undergraduate exams such as the United States Medical Licensing Examination. We challenged it to answer questions from a more demanding, post-graduate exam-the European Exam in Core Cardiology (EECC), the final exam for the completion of specialty training in Cardiology in many countries. Our results demonstrate that ChatGPT succeeds in the EECC.