Citations

BACKGROUND: Myocardial infarction with nonobstructive coronary arteries (MINOCAs) and angina with nonobstructive coronary arteries (ANOCAs) present diagnostic and therapeutic challenges. We assessed the frequency of coronary microvascular disease (CMD) and its subtypes in MINOCA compared with ANOCA and evaluated long-term cardiovascular outcomes. METHODS: Patients with nonobstructive coronary arteries undergoing invasive coronary function testing from 2012 to 2022 were prospectively enrolled and retrospectively classified as MINOCA or ANOCA (reference group). Myocardial infarction due to atherothrombosis, myocarditis, and Takotsubo syndrome were excluded. Invasive provocation testing was performed. CMD was classified as endothelium-independent (coronary flow reserve <2.5 with adenosine) and endothelium-dependent (<50% increase in coronary blood flow with acetylcholine). Major adverse cardiovascular events (cardiac death, nonfatal myocardial infarction, heart failure hospitalization, urgent revascularization, stroke) were recorded over 8 years. RESULTS: Of 622 patients, 73 (12%) had MINOCA (69% female, median age 56 years) and 549 (88%) ANOCA (72% female, median age 53 years). CMD was common in both groups (71% versus 64%; P =0.271), while endothelium-independent CMD was more frequent in MINOCA (41% versus 21%; P <0.001). Over a median follow-up of 4.5 years, major adverse cardiovascular events occurred more often in MINOCA (40% versus 11%; adjusted hazard ratio, 2.4; P =0.001). CMD predicted higher major adverse cardiovascular events in both MINOCA (hazard ratio, 3.6 [95% CI, 1.1–12.0]; P =0.038) and ANOCA (hazard ratio, 7.9 [95% CI, 2.9–21.8]; P <0.001). CONCLUSIONS: CMD is common in both MINOCA and ANOCA. The higher prevalence of endothelium-independent CMD in MINOCA suggests a potential mechanistic role in infarction. Patients with MINOCA have a worse prognosis than ANOCA, particularly when CMD is present.

BACKGROUND: Randomized trials comparing radial and femoral access report access site crossover rates of 3% to 9% for radial access and 1% to 3% for femoral access. However, contemporary real-world crossover rates are not well characterized. The aim was to evaluate contemporary trends and predictors of vascular access site crossover during percutaneous coronary intervention (PCI). METHODS: We analyzed index PCI procedures recorded in the National Cardiovascular Data Registry CathPCI Registry from 2018 to 2024. Access site crossover was identified using documented access data, with radial-to-femoral crossover inferred when the final successful access site was femoral. Temporal trends and clinical predictors of crossover were examined. Operator experience was assessed using cumulative total and radial PCI volumes from 2011 to 2024 as captured in CathPCI Registry versions 4 and 5. RESULTS: Among 3 861 790 index PCIs, 192 902 (5.0%) involved access site crossover, with a modest increase over time. Radial-to-femoral crossover accounted for 81.2% of crossover events. Factors independently associated with crossover included older age, female sex, higher body mass index, and peripheral vascular disease. Operators with the lowest total PCI volume and the lowest radial PCI volume experienced the highest crossover rates. Notably, operators with low radial PCI volume demonstrated a substantial decline in radial-to-femoral crossover over the study period. CONCLUSIONS: In contemporary clinical practice, access site crossover rates are consistent with those reported in randomized trials. Although crossover occurs more frequently among lower-volume operators, rates decline with increasing operator experience, supporting the presence of a radial access learning curve and improvement in procedural performance over time.

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

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

An ST-segment elevation myocardial infarction is a life-threatening condition within the spectrum of acute coronary syndrome, with well-recognized implications for short-term and long-term health outcomes. Retrospective review of our institutional electronic health records identified patterns of inaccurate acute coronary syndrome diagnostic assignment, including the presence of erroneous ST-segment elevation myocardial infarction diagnoses in discharge records. Such erroneous diagnoses in the durable health record may increase risk to patients through inadequate risk assessment and inappropriate follow-up care. Misapplication of a shortcut in our electronic health record system for rapidly ordering emergent cases was identified as a potential cause of the quality gap. To address this issue, a nurse-led quality improvement project was developed by using the define, measure, analyze, improve, and control framework. The project intervention decreased the percentage of patients with erroneous diagnoses from 24% to 7% and marginally improved nursing staff satisfaction. Our project successfully addressed an institutional quality concern. Our goal of reducing erroneous discharge diagnoses with minimal disruption to our existing practice was achieved, positively affecting our patients. Including both nurse leaders and frontline health care workers ensured the success of our project.

Background: Coronary microvascular dysfunction (CMD) is a common cause of angina with non-obstructive coronary arteries (ANOCA), but its role in patients with a history of myocardial infarction with non-obstructive coronary arteries (MINOCA) is much less understood. We investigated the incidence of major adverse cardiac and cerebrovascular events (MACCE) in patients undergoing invasive assessment for CMD, divided into patients with MINOCA versus patients with ANOCA. We hypothesized that CMD would be common in both groups. Methods: Serial patients undergoing invasive coronary provocation testing between 2012 and 2022 were divided into MINOCA (n=76) or ANOCA (n=561). Doppler study was performed in the left anterior descending artery. CMD was defined as either coronary flow reserve (CFR) <2.5 with adenosine or <50% increase in flow with acetylcholine. Patients were followed prospectively through 2025. Incidence of MACCE (cardiac death, non-fatal MI, hospitalization for heart failure, urgent revascularization, and stroke) was compared between MINOCA and ANOCA and further analyzed by CMD status. Kaplan-Meier curves were constructed and Cox proportional hazards models were used to estimate hazard ratios (HRs). Results: Among patients with MINOCA, 53 of 76 (70%) had CMD, compared to 363 of 561 (65%) with ANOCA. Invasive provocation testing was performed a median of 163 days (IQR 13 – 255 days) after the MI in the MINOCA group. Patients were then followed for a median of 4.4 years (IQR 3.0 – 6.9 years). MACCE-free survival was significantly lower in patients with MINOCA compared to those with ANOCA (HR = 3.57, p < 0.001; Figure 1). The major contributor to MACCE was non-fatal MI in MINOCA (24%) and heart failure hospitalization in ANOCA (6%). The presence of CMD was associated with worse prognosis in both MINOCA (HR = 3.76, p = 0.02) and ANOCA (HR = 8.35, p < 0.001; Figure 2). Conclusions: Patients with MINOCA experience significantly higher long-term risk of major adverse events compared to those with ANOCA. CMD is common in both groups and is independently associated with worse outcomes. Specifically, patients with CMD had higher rates of MACCE over time, regardless of whether they presented with MINOCA or ANOCA. Invasive assessment of CMD in patients with myocardial infarction and non-obstructive coronary arteries may improve risk stratification and inform management strategies in these understudied populations

Background: Coronary microvascular disease (CMD) may be endothelium-independent (coronary flow reserve, CFR < 2.5) or endothelium-dependent (<50% increase in coronary blood flow in response to acetylcholine). CMD has been implicated in abnormal myocardial relaxation and impaired diastolic function. Wave intensity analysis (WIA) is a hemodynamic method that quantifies the energy of pressure-flow waves driving coronary blood flow. The backward expansion wave (BEW) is the dominant distally originating wave, generated by microvascular suction during ventricular relaxation. Thus, the BEW may offer mechanistic insight into the link between CMD and diastolic dysfunction. Hypothesis: We hypothesized that diastolic dysfunction would be more common in patients with CMD, and that the backward expansion wave (BEW) intensity would be lower in those with diastolic dysfunction compared to those without. Methods: Patients with non-obstructive coronary artery disease undergoing invasive coronary physiology assessment of the left anterior descending artery were included (n = 113). Pressure and Doppler flow were recorded at rest, with adenosine, and with acetylcholine for categorization of CMD as endothelium-independent (CMD-Ei) and/or endothelium-dependent (CMD-Ed). Wave intensity analysis was used to calculate cumulative BEW (J×m -2 ) at rest in CMD, CMD-Ei (endothelium-independent), and CMD-Ed (endothelium-dependent) subgroups. Diastolic dysfunction was diagnosed by transthoracic echocardiogram using the 2016 American Society of Echocardiography (ASE) guidelines. Results: Of the 113 study patients, 88 (77.9%) had CMD and 25 (22.1%) did not. Among those with CMD, 39 patients (44.3%) had diastolic dysfunction (Figure 1). In the patients with CMD, the BEW intensity was significantly lower in patients with diastolic dysfunction compared to those without (6.5 vs. 9.0 × 10 3 W×m -2 ×s -1 , p < 0.05). The same trend was observed for the CMD-Ei (7.2 vs. 9.7 × 10 3 W×m -2 ×s -1 , p < 0.05) and CMD-Ed (6.2 vs. 9.1 × 10 3 W×m -2 ×s -1 , p < 0.05) subtypes. Conclusions: Diastolic dysfunction is frequent in patients with invasively diagnosed CMD. Patients with CMD who have diastolic dysfunction have comparatively lower BEW intensity, regardless of endothelial subtype. Since the BEW is the major driver of coronary flow during diastole, this suggests a mechanism by which CMD may lead to diastolic dysfunction.

Background: Coronary wave intensity analysis (WIA) provides a mechanistic view of coronary hemodynamics and has been increasingly used to phenotype coronary microvascular disease (CMD). Standard WIA requires simultaneous pressure and flow measurements within the same vessel, but intra-coronary pressure measurement may be limited by hardware availability. In patients with non-obstructive coronary arteries, aortic pressure may serve as a surrogate for intra-coronary pressure to enable broader feasibility of WIA. We aimed to determine whether wave profiles derived from intra-aortic and intra-coronary pressure measurements are comparable when paired with intracoronary Doppler flow in patients with non-obstructive coronary artery disease. Methods: Patients undergoing invasive coronary physiology assessment of the left anterior descending artery were included. Intracoronary flow velocity and intra-coronary pressure were simultaneously recorded using a ComboWire (Philips Volcano) in the proximal LAD. Aortic pressure was concurrently measured via the guide catheter to allow comparison of pressure sources. In the intra-aortic pressure group, pressure and flow data were aligned using the R wave of the ECG trace. WIA was performed using conventional methods to quantify the dominant waves: forward compression wave (FCW), backward compression wave (BCW), and backward expansion wave (BEW). Agreement between wave intensity was assessed using Bland-Altman analysis and Pearson correlation. Results: A total of 14 paired waveforms were analyzed with a total of 42 coronary waves compared. Wave intensities calculated using intra-coronary versus intra-aortic pressure showed excellent agreement across all wave types. Mean bias between methods was −0.07 x 10 3 W×m -2 ×s -1 with limits of agreement from −0.63 to +0.49. The correlation between intra-coronary and intra-aortic wave intensities was strong (r = 0.997, p < 0.001), with minimal dispersion and consistent agreement across the full range of wave intensities ( Figure 1 ). Conclusions: In patients with non-obstructive coronary arteries, wave intensity analysis using guide-catheter aortic pressure yields results nearly identical to those obtained with intra-coronary pressure. These findings support the use of aortic pressure as a reliable surrogate for intra-coronary pressure in WIA for the calculation of microvascular coronary hemodynamics, reducing technical barriers and enabling broader clinical and research utilization.

Background We tested whether biologic age, as estimated by deficits, functional impairments, or Age‐Gap or their combination, provide improved estimation of cause‐specific death as compared with chronological age. Methods Cardiovascular and noncardiovascular deficits, functional impairments, and Age‐Gap were prospectively collected in 535 patients aged ≥55 years undergoing percutaneous coronary interventions between August 1, 2014, and March 31, 2018. Age‐Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. The full biological age model included deficits, functional impairments, and Age‐Gap >2 SD. A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model. Results The average chronological age was 72.1±9.5 years, and there were 68% of men. During a median follow‐up of 2.61 years, 124 (23%) patients died. There was a modest correlation between Age‐Gap and biological age ( r =0.28 [95% CI, 0.20–0.35]; P <0.001). When modeled with chronologic age as a covariate, Age‐Gap predicted all‐cause (hazard ratio [HR], 1.07 [95% CI, 1.04–1.10]; P <0.001) and cardiovascular (HR, 1.07 [95% CI, 1.04–1.11]; P <0.001) mortality. As compared with chronological age, the full biological age model noted significant improvement in the prediction of long‐term overall (95% CI, 0.65–0.78), cardiovascular (95% CI, 0.69–0.77), and noncardiovascular (95% CI, 0.55–0.86) mortality. In the reduced models, most prognostic information for noncardiovascular mortality (C index: 0.79) was obtained by subjective difficulty in performing tasks, whereas the deficit‐based estimation predicted cardiovascular mortality (C index: 0.72). Conclusions Estimated biological age from deficits and functional impairments was superior to chronological age in predicting long‐term cause‐specific mortality following percutaneous coronary interventions.

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.