Franchi, Francesco

Spontaneous coronary artery dissection (SCAD) and Takotsubo syndrome (TTS) are two conditions presenting as acute coronary syndromes that predominantly affect women and are frequently triggered by emotional or physical stress. While traditionally considered distinct entities, emerging evidence suggests that these conditions may coexist more frequently than previously recognized, sharing common pathophysiological mechanisms centered on catecholamine-mediated pathways, vascular vulnerability, and hormonal influences. Their overlapping presentations create significant diagnostic challenges that may lead to misdiagnosis and suboptimal management. This narrative review examines the pathophysiological convergence between SCAD and TTS, addresses the diagnostic complexity arising when these entities coexist, and proposes a novel stepwise diagnostic algorithm integrating clinical risk stratification, coronary angiography, intracoronary imaging, and cardiac magnetic resonance imaging. The review also provides scenario-dependent management guidance and outlines priorities for future research.

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

Introduction: Identifying patients at risk for ischemic events after percutaneous coronary intervention (PCI) relies on traditional analysis of limited clinical and imaging variables. Machine learning (ML) has shown promise in effectively predicting cardiovascular risk in population studies. While existing ML models mainly predict mortality and incorporate clinical variables, there is a lack of tools that have utilized genetic data and that predict ischemic events. Aims: This study aims to develop and validate a ML model incorporating genotyping and clinical data to enhance prediction of ischemic outcomes for PCI patients utilizing large prospectively derived diverse datasets. Methods: Patients from the TAILOR-PCI trial (n=5302) were utilized for model development. 50% of the sample was utilized for Boruta feature selection and 50% for training and testing using cross validation. Features included demographics, medical history, medications, PCI characteristics, and genetic data (specifically, CYP2C19 *2, *3, *17 alleles). The primary endpoint was a composite of cardiovascular death, myocardial infarction, stroke, stent thrombosis, and severe recurrent ischemia at 12 months. Multiple ML classification algorithms, including Support Vector Machine (SVM) polynomial, Random Forest, Light gradient boost, XG Boost, among others, were benchmarked for their prediction performance on rare events. The top performing classifiers were externally validated on an independent dataset from the PRECISION PCI study (n=3,745). Results: Mean participant age of the training set was 64.2 ± 11.0 years, with 75.4% being male. During follow-up of 12 months, among 4,572 patients in the entire cohort 343 (7.5%) met the primary outcome. The SVM polynomial model demonstrated the highest area under the curve (AUC) of 0.67 for predicting the primary outcome with test dataset. The sensitivity, specificity, precision, and recall were 0.87, 0.28, 0.07, and 0.87 respectively (Figure). Peripheral arterial disease, body mass index, and age were among the top variables by feature importance. Conclusion: ML models incorporating both clinical and genetic data are feasible and highly promising in predicting major adverse cardiac events that may help guide use of anti-platelet drug therapy. The AUC values are reasonable given imbalances and misclassifications in datasets, and further model optimization with prospective utilization of the model will be paramount.

Antiplatelet therapy is the mainstay of pharmacologic treatment to prevent thrombotic or ischemic events in patients with coronary artery disease treated with percutaneous coronary intervention and those treated medically for an acute coronary syndrome. The use of antiplatelet therapy comes at the expense of an increased risk of bleeding complications. Defining the optimal intensity of platelet inhibition according to the clinical presentation of atherosclerotic cardiovascular disease and individual patient factors is a clinical challenge. Modulation of antiplatelet therapy is a medical action that is frequently performed to balance the risk of thrombotic or ischemic events and the risk of bleeding. This aim may be achieved by reducing (ie, de-escalation) or increasing (ie, escalation) the intensity of platelet inhibition by changing the type, dose, or number of antiplatelet drugs. Because de-escalation or escalation can be achieved in different ways, with a number of emerging approaches, confusion arises with terminologies that are often used interchangeably. To address this issue, this Academic Research Consortium collaboration provides an overview and definitions of different strategies of antiplatelet therapy modulation for patients with coronary artery disease, including but not limited to those undergoing percutaneous coronary intervention, and consensus statements on standardized definitions.

Abstract Aims Guidelines recommend the use of potent P2Y12 inhibitors over clopidogrel for the reduction of ischaemic events in patients with acute coronary syndrome (ACS). However, this comes at the expense of increased bleeding. A guided selection of P2Y12 inhibiting therapy has the potential to overcome this limitation. We aimed at evaluating the comparative safety and efficacy of guided vs. routine selection of potent P2Y12 inhibiting therapy in patients with ACS. Methods and results We performed a network meta-analysis of randomized controlled trials (RCTs) comparing different oral P2Y12 inhibitors currently recommended for the treatment of patients with ACS (clopidogrel, prasugrel, and ticagrelor). RCTs including a guided approach (i.e. platelet function or genetic testing) vs. standard selection of P2Y12 inhibitors among patients with ACS were also included. Incidence rate ratios (IRR) and associated 95% confidence intervals (CIs) were estimated. P-scores were used to estimate hierarchies of efficacy and safety. The primary efficacy endpoint was major adverse cardiovascular events (MACE) and the primary safety endpoint was all bleeding. A total of 61 898 patients from 15 RCTs were included. Clopidogrel was used as reference treatment. A guided approach was the only strategy associated with reduced MACE (IRR: 0.80, 95% CI: 0.65–0.98) without any significant trade-off in all bleeding (IRR: 1.22, 95% CI: 0.96–1.55). A guided approach and prasugrel were associated with reduced myocardial infarction. A guided approach, prasugrel, and ticagrelor were associated with reduced stent thrombosis. Ticagrelor was also associated with reduced total and cardiovascular mortality. Prasugrel was associated with increased major bleeding. Prasugrel and ticagrelor were associated with increased minor bleeding. The incidence of stroke did not differ between treatments. Conclusion In patients with an ACS, compared with routine selection of potent P2Y12 inhibiting therapy (prasugrel or ticagrelor), a guided selection of P2Y12 inhibiting therapy is associated with the most favourable balance between safety and efficacy. These findings support a broader adoption of guided approach for the selection of P2Y12 inhibiting therapy in patients with ACS. Study registration number This study is registered in PROSPERO (CRD42021258603). Key Question A guided selection of P2Y12 inhibiting therapy using platelet function or genetic testing improves outcomes among patients undergoing percutaneous coronary intervention. Nevertheless, the comparative safety and efficacy of a guided versus routine selection of potent P2Y12-inhibiting therapy in acute coronary syndrome has not been explored. Key Finding In a comprehensive network meta-analysis including the totality of available evidence and using clopidogrel as treatment reference, a guided approach was the only strategy associated with reduced major adverse cardiovascular events without any significant trade-off in bleeding. Prasugrel and ticagrelor increased bleeding and only ticagrelor reduced mortality. Take Home Message A guided selection of P2Y12-inhibiting therapy represents the strategy associated with the most favourable balance between safety and efficacy. These findings support a broader adoption of guided P2Y12 inhibiting therapy in patients with acute coronary syndrome.