Lerman, Amir

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

Introduction: Photoplethysmography-based (PPG) devices represent a significant advancement in noninvasive cardiovascular monitoring. These cuffless technologies enable assessment of hemodynamic parameters with improved comfort, usability and compliance. Modern PPG systems have expanded their capabilities to include advanced metrics such as systemic vascular resistance (SVR) and cardiac output (CO). Many antihypertensive drugs lower blood pressure by reducing SVR through vasodilatation of the systemic and renal vasculature. Limited data is available on SVR during ambulatory monitoring. PPG devices become more common as they allow us to assess SVR in real life, in ambulatory blood pressure monitoring (ABPM) patients. Aim: To evaluate and compare SVR using the Biobeat (WP613) device across patient groups. Methods: Adults from the Hypertension Clinic at University of Miami, Florida, from Aug. 2023–Jan. 2025 underwent 24-h ABPM monitoring using Biobeat device. Patient data was matched with Biobeat. PPG data was available every 15 minutes; The raw data included SBP and DBP, the heart rate, stroke volume, CO and SVR. Results: A total of 539 patients underwent PPG ABPM during the study period. The median was 62 (IQR 54, 71) years, with 57% females (Tab 1) . Patients were defined as hypertensive per AHA 2017 guidelines [SBP≥130 and DBP≥80 mmHg]. Overall, 265 patients were classified as having normal BP, 95 as having isolated systolic hypertension, and 179 as having mixed hypertension. Within the Mix-HTN group, 42 patients had isolated diastolic hypertension and the remaining 137 systolic and diastolic hypertension (Fig 2) . In subgroup analysis (Fig 3); SVR was significantly different between the groups: lowerst in No/C-HTN and highest in Mix-HTN. particularly among the mixed-hypertension group, SVR was higher in the Systolic and Diastolic HTN compared to the Isolated diastolic HTN group. Among the No/C-HTN group, 60 patients were not hypertensives and 205, were hypertensives on at least 1 drug. The SVR was not statistically significant between these 2 subgroups, suggesting that SVR may be a helpful target biomarker used to up-titrate antihypertensives. Conclusion: PPG devices offer a valuable enhancement to conventional ABPM by providing additional physiological measurements such as SVR that can help differentiate between various hypertension phenotypes and potentially provide personalized therapy that target the driver of hypertension.

Abstract Introduction Acoustic analysis of speech has discriminated decompensated acute heart failure (HF). Speech rate (SR) and cepstral peak prominence (CPP) variation are among features previously evaluated. However, the association between SR and CPP and chronic stable HF with and without pulmonary hypertension (PH) as well as PH alone have not been previously studied. Methods Patients evaluated for HF and/or PH in the outpatient setting recorded a standardized text read out loud from which a sentence was extracted and analysed to extract pre-specified acoustic features including SR and CPP calculated for voiced speech (CPP-V) and in all speech (CPP-All). Patients were grouped depending on the presence or absence of disease (HF and/or PH) and symptoms. Linear regression models were fitted to determine the association between each acoustic feature and disease status. Results In total, 2153 patients were included: age 65.32 ± 17.18 years; male n = 1246 (57.9%); 879 had HF (40.8%), 542 had PH (25.2%) and 777 had no disease and no symptoms (36.1%). After adjustment for age and sex, SR was significantly lower in patients with PH only [estimated coefficient, 95% confidence interval (CI): −0.14, −0.21 to −0.06, P = 0.0006], HF only (−0.11, −0.17 to −0.05, P = 0.0002) and HF with PH (−0.17, −0.24 to −0.10, P &lt; 0.0001) compared with no disease. CPP-V differed in patients with PH only (0.37, 0.16–0.57, P = 0.0004) and CPP-All differed significantly compared with patients without disease (0.23, 0.08–0.38, P = 0.0025). Conclusions SR is significantly slower in patients with HF alone, PH alone and HF and PH combined compared with patients without disease. CPP also differs significantly in patients with PH compared with controls. These findings suggest that acoustic analysis may be useful in discriminating chronic stable HF and PH, offering promise for the development of non-invasive screening methods for HF and PH.

Introduction: Patients with atherosclerotic carotid artery disease are at high risk of mortality in the long-term follow-up after carotid endarterectomy (CEA), partly due to dysrhythmia. Atrial fibrillation (AF) is a common cardiac dysrhythmia linked to stroke and cardiovascular events. An artificial intelligence-based electrocardiogram (ECG) AF algorithm (AI-AF) can effectively identify silent AF. We aimed to assess the effectiveness of AI-AF in predicting mortality in patients with carotid artery disease undergoing CEA. Methods: Patients with carotid artery disease undergoing CEA at Mayo Clinic from 2002-2023 were included if they had >30 days follow-up and an ECG within one year before CEA. The ECG closest to the CEA date was used to calculate AI-AF (probability of AF ranging from 0 to 1). All-cause mortality rates after CEA were obtained from clinical records. The optimal AI-AF cut-off value for predicting mortality was calculated using a receiver operating characteristic curve and the Youden index. Long-term survival after CEA was depicted with a Kaplan Meier plot and compared with the Log-rank test. Univariate and multivariate Cox Regression models were used to assess the predictive value of AI-AF for mortality, adjusting for age, sex, body mass index, diabetes mellitus, hypertension, and hyperlipidemia. Results: A total of 636 patients with a median age of 72 [IQR: 66, 78] years and 414 (65.1%) males were included. The median AI-AF was 0.056 [IQR: <0, 0.171]. Over a median follow-up time of 7.1 [IQR: 2.7, 11.5] years, mortality occurred in 326 (51.3%) patients. AI-AF significantly predicted mortality in univariate and multivariate Cox regression analysis (HR: 3.47, 95%CI: 2.34-5.16, P<0.001; HR: 2.30, 95%CI: 1.44-3.68, P<0.001, respectively). The best cut-off value for AI-AF for mortality prediction was 0.025 (area under the curve: 0.608). High and low AI-AF were defined as AI-AF≥ 0.025 and <0.025, respectively. Mortality was significantly higher in high AI-AF compared to low AI-AF patients (P<0.001, Figure 1A,1B), and high AI-AF was a significant predictor of mortality (univariate: Figure 2; multivariate Cox regression: HR: 2.11, 95%CI: 1.56-2.84, P<0.001). Conclusion: AI-AF can identify patients with a higher risk of mortality after CEA. This finding supports a role for AI algorithms in risk stratification of patients with carotid artery disease following CEA. Future studies are needed to elucidate the contribution of AF to all-cause mortality.

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.

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). Endothelium-independent CMD has been subclassified into structural and functional groups based on hyperemic microvascular resistance (hMR, mmHg/cm/s). We hypothesized that endothelium-dependent CMD also has these subtypes. Methods: Patients with non-obstructive coronary artery disease who underwent coronary catheterization of the left anterior descending artery were included (n=112). Pressure and flow were recorded at rest, with adenosine, and with acetylcholine. Patients were classified by endothelial type and functional CMD (hMR<2.5) or structural CMD (hMR>2.5). Wave intensity analysis was performed and cumulative separated wave intensity (Joules/m 2 ) was calculated by standard formulae. Three dominant waves were analyzed (forward compression wave, FCW; backward compression wave, BCW; backward expansion wave, BEW). Results: Seventy-four of 109 patients had CMD, of which 59 had endothelium-independent and 38 had endothelium-dependent CMD (23 had both). Functional and structural subgroups were identified (Figure 1). All CMD subtypes had significantly different wave intensities at rest, adenosine, and acetylcholine (p<0.05). Comparing subtypes, there was no difference in wave intensities at rest. However, wave intensities significantly differed between all CMD subtypes with adenosine and acetylcholine, with higher energy waves in the endothelium-independent and functional groups (p<0.05, Figure 2). Conclusions: CMD is a heterogenous condition with distinct subtypes. Functional and structural groups exist in both endothelium-dependent and endothelium-independent CMD. These subgroups have distinct hemodynamic profiles as measured by wave intensity. A patient-specific approach to first line medical therapy may be improved by subgroup classification.