Background: Murray law‐based Quantitative Flow Ratio (μQFR) is an innovative AI‐driven method for evaluating coronary lesions using contrast flow velocity estimates. Unlike traditional approaches, μQFR does not require pressure wires or hyperemia induction. However, its accuracy compared to Fractional Flow Reserve (FFR) for calcified lesions (ACC/AHA classification B2 or C) remains underexplored. Aims: This study aimed to evaluate the diagnostic accuracy of μQFR in detecting hemodynamically significant calcified coronary stenosis, defined as FFR ≤ 0.80. Methods: In this two‐center, investigator‐initiated trial, patients with calcified lesions (B2 or C, 30%–90% diameter stenosis) and FFR assessment during coronary angiography were included. μQFR was calculated by a nurse blinded to the FFR results. Sensitivity and specificity of μQFR were compared with FFR as the gold standard. Results: Among 125 patients, paired μQFR and FFR data were available for 107 patients and 120 lesions. Mean FFR, μQFR, and percent stenosis were 0.775 (95% CI: 0.757–0.793), 0.788 (95% CI: 0.772–0.805), and 56.75% ( ± 10.14%), respectively. μQFR demonstrated a sensitivity of 75% (95% CI: 65.6%–84.6%), a specificity of 77.8% (95% CI: 63.5%–92.0%), a diagnostic precision of 75.8% and an area under the receiver operating characteristic curve of 0.813 (95% CI: 0.714–0.911). The positive predictive value was 88.7%, and the negative predictive value was 57.1%. The positive and negative likelihood ratios were 3.38 and 0.32, respectively. Conclusion: μQFR is simple to compute and offers moderate diagnostic accuracy for calcified coronary stenosis. It provides a valuable alternative when FFR is unavailable or technically challenging.

Background: Accurate operative risk stratification is essential for treatment selection in severe aortic stenosis. We developed an automated workflow using large language models (LLMs) to replicate Heart Team risk assessment. Methods: We retrospectively analyzed 231 consecutive patients with severe aortic stenosis evaluated by multidisciplinary Heart Teams (January 2022–December 2024). An automated system using GPT-4o was developed, comprising the following: (1) structured data extraction from clinical dossiers; (2) EuroSCORE II calculation via two methods (algorithmic vs. LLM-based); (3) clinical vignette generation; and (4) risk stratification comparing EuroSCORE-based thresholds versus guideline-integrated LLM approaches with/without EuroSCORE values. The primary endpoint was the risk stratification accuracy of each method compared to Heart Team decisions. Results: Mean age was 79.5 ± 7.7 years, with 58.4% female. The automated workflow processed patients in 32.6 ± 6.4 s. The LLM-calculated EuroSCORE II showed a lower mean difference from Heart Team values (−1.42%, 95% CI −2.32 to −0.53) versus algorithmic calculation (−1.88%, 95% CI −2.38 to −1.38). For risk stratification, the guideline-integrated LLM without EuroSCORE achieved the highest accuracy (90.0%) and AUC (0.93), outperforming both the EuroSCORE-based (accuracy 50.2% for high-risk, AUC 0.63) and guideline-integrated LLM with EuroSCORE approaches (accuracy 82.4%, AUC 0.76). However, systematic hallucinations occurred for cardiovascular risk factors when data were missing. Conclusions: LLMs accurately calculated EuroSCORE II and achieved 90% concordance with multidisciplinary Heart Team decisions. However, hallucinations, reproducibility concerns, and the absence of clinical outcome validation preclude direct clinical application.

Radial wall strain (RWS) is an angiography-derived biomechanical index that quantifies plaque deformability from routine coronary angiography, offering a novel approach to assess plaque vulnerability beyond conventional anatomical and physiological parameters. This narrative review synthesizes current evidence on the pathophysiological basis, clinical applications, and prognostic implications of RWS in coronary artery disease. Recent studies have demonstrated that elevated RWS values are associated with established markers of plaque vulnerability, including thin-cap fibroatheromas and large lipid cores, as well as increased risk of major adverse cardiovascular events. Furthermore, RWS has emerged as a promising tool for identifying vulnerable plaques in non-flow-limiting lesions and predicting in-stent restenosis, demonstrating incremental prognostic value when combined with angiography-derived physiological indices such as the quantitative flow ratio. By integrating biomechanical assessment with conventional physiological and morphological characterization, RWS may enhance risk stratification and guide clinical decision-making in percutaneous coronary intervention. While current evidence is encouraging, prospective randomized trials are needed to validate RWS-guided management strategies and establish optimal cutoff values for clinical implementation.

Inflammatory myopericardial syndromes (IMPS)-including pericarditis, myocarditis, and overlap phenotypes-frequently present with chest pain and electrocardiographic abnormalities that can mimic occlusion myocardial infarction (OMI). High electrocardiographic specificity is essential to avoid unnecessary catheterization laboratory activation. To evaluate the ECG-level specificity of the OMI-trained PMcardio Queen of Hearts (QoH) AI-ECG model for OMI classification in patients with IMPS. We conducted a single-centre retrospective study including consecutive emergency department patients with adjudicated IMPS (2010-2025) at the University & Hospital of Fribourg. Obstructive coronary artery disease was excluded by coronary angiography or cardiac magnetic resonance imaging according to contemporary guidelines. Index ECGs were analysed using QoH. Specificity was compared with European Society of Cardiology (ESC) STEMI ECG criteria, the 4-variable formula (when applicable), and 3 large language models (LLMs; ChatGPT 5.2, Claude Opus 4.5, and Gemini 3). The primary endpoint was ECG-level specificity. Because only adjudicated IMPS cases were included, the analysis was designed to estimate ECG-level specificity rather than overall diagnostic accuracy. Among 242 IMPS patients, QoH correctly classified 230 as non-OMI (specificity: 95.0%; 95% CI: 91.5-97.1). ESC STEMI ECG criteria showed 78.1% specificity, and the 4-variable formula demonstrated 73.3% specificity in anterior presentations (n = 30). All LLMs exhibited substantially lower specificity (26.9% to 70.2%; all P < 0.001 vs QoH). Discordant ESC-positive/QoH-negative cases (n = 43) predominantly showed diffuse, nonterritorial inflammatory ECG patterns. Specificity was higher in (myo)pericarditis than in (peri)myocarditis (97.7% vs 87.7%, P = 0.004). In IMPS, the OMI-trained QoH model demonstrated high specificity and fewer false-positive OMI classifications than guideline ECG-STEMI criteria, ECG-based scores, and general-purpose LLMs.

Background: Obesity is increasing worldwide and remains a major contributor to cardiovascular morbidity and mortality. It is strongly associated with hypertension, dyslipidemia, diabetes mellitus, endothelial dysfunction, and chronic inflammation, all of which promote coronary artery disease and acute coronary syndrome (ACS). Despite this well-established risk profile, multiple studies have described an “obesity paradox,” suggesting that obese patients may experience better outcomes after percutaneous coronary intervention (PCI) for ACS than normal-weight individuals. Objective: This narrative review aims to discuss the pathophysiological basis of the obesity paradox and to synthesize contemporary evidence regarding the relationship between body mass index (BMI), major adverse cardiovascular events (MACE), and mortality after PCI in patients presenting with ACS. Results: Contemporary observational cohorts consistently suggest a non-linear relationship between BMI and MACE outcomes after PCI. Overweight and mildly obese patients often demonstrate lower crude mortality and fewer MACE, whereas underweight patients consistently show the poorest prognosis. However, after adjustment for age, left ventricular ejection fraction (LVEF), renal function, frailty, and nutritional status, obesity is less consistently associated with improved outcomes. Overweight status appears to be more reproducibly associated with better prognosis than obesity itself. Conclusions: The obesity paradox is likely driven less by a true protective effect of excess adiposity and more by younger age at presentation, preserved physiological reserve, lower frailty burden, and the limitations of BMI as a marker of cardiovascular risk. Underweight status emerges as the strongest predictor of adverse outcomes. Nutritional assessment and body composition should complement BMI in risk stratification after ACS.