Background: Optimal stent deployment in left main (LM) bifurcation is paramount, and incomplete stent apposition may cause major adverse cardiac events (MACE). Bench studies show that the proximal optimization technique (POT) provides the best stent apposition.Aims: We aimed to investigate the impact of POT on clinical outcomes in patients treated for unprotected LM (ULM) disease at our institution.Methods: We identified 162 patients who underwent percutaneous coronary intervention (PCI) for ULM coronary disease in the Cardio‐FR database. Out of these, 99 (61%) had undergone POT, while 63 patients were treated without POT. The primary outcome was the bifurcation‐oriented composite endpoint (BOCE) of cardiac death, target‐bifurcation myocardial infarction and target‐bifurcation revascularization at maximal follow‐up.Results: Mean age was 76 years, and 69% presented with acute coronary syndrome. Mean follow‐up was 2.25 years (822 days). The BOCE occurred in 43 (27%) of which 20 (20%) in the POT group and 23 (37%) in the no‐POT group (p = 0.009). Cardiac death occurred in 15 (15%) patients in the POT‐ and 17 (27%) in no‐POT group (p = 0.26). Target bifurcation revascularization occurred in 4 (4%) patients in the POT‐ and 6 (10%) patients in the no‐POT group (p = 0.19). POT In the multivariate analysis, POT was the strongest parameter and was associated with BOCE, cardiac death, occurrence of any revascularization and all‐cause mortality.Conclusion: The POT improves clinical outcomes. These findings strongly support the systematic use of POT in patients undergoing ULM‐PCI.

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.

Background: Large language models (LLMs) are increasingly accessible to healthcare providers and patients for clinical decision support, yet their ability to perform precise mathematical calculations required for validated risk scores remains unexplored, and errors could compromise patient safety. The EuroSCORE II requires complex multivariable computation that could reveal fundamental limitations in LLM computational capabilities. Methods: We evaluated four publicly available chatbots (ChatGPT (GPT-4o, OpenAI), Claude (Sonnet 4.5, Anthropic), Gemini (2.5 Flash, Google) and Deepseek (V3)) in calculating EuroSCORE II for 105 patients from the CARDIO-FR registry with gold standard heart team calculations. Each model was tested using two approaches: direct calculation from clinical parameters alone and formula-based calculation with the explicit EuroSCORE II algorithm provided. Performance was assessed through mean absolute error (MAE), correlation coefficients, and clinical agreement within ±2% of gold standard values. Results: Direct LLM calculations demonstrated poor accuracy (MAE range: 3.3–6.4%) with the best performer (Gemini) achieving only 50.5% clinical agreement. Formula provision improved performance in three of four models, with ChatGPT formula achieving the lowest MAE (2.9%) and highest clinical agreement (51.4%), followed by Claude formula (MAE 3.2%, agreement 48.6%). Adding the formula to the prompt significantly improved performance and reduced bias. All methods exhibited significant systematic biases (p < 0.05 for 7/8 strategies). Conclusions: Publicly available LLM chatbots cannot reliably calculate EuroSCORE II for clinical use. Adding the formula to the prompt significantly improved performance, but was not sufficient to reach the clinically required threshold of 90% agreement. Clinicians should rely on validated risk calculation tools rather than LLM chatbots for quantitative clinical risk assessments.