Faculty Bibliography

OBJECTIVE:Patients with peripheral artery disease (PAD) undergo lower extremity revascularization (LER) for symptomatic relief or limb salvage. Despite LER, patients remain at increased risk of platelet-mediated complications, such as major adverse cardiac and limb events (MACLEs). Platelet activity is associated with cardiovascular events, yet little is known about the dynamic nature of platelet activity over time. We, therefore, investigated the change in platelet activity over time and its association with long-term cardiovascular risk. METHODS:Patients with PAD undergoing LER were enrolled into the multicenter, prospective Platelet Activity and Cardiovascular Events study. Platelet aggregation was assessed by light transmission aggregometry to submaximal epinephrine (0.4 μmol/L) immediately before LER, and on postoperative day 1 or 2 (POD1 or POD2) and 30 (POD30). A hyperreactive platelet phenotype was defined as >60% aggregation. Patients were followed longitudinally for MACLEs, defined as the composite of death, myocardial infarction, stroke, major lower extremity amputation, or acute limb ischemia leading to reintervention. RESULTS:Among 287 patients undergoing LER, the mean age was 70 ± 11 years, 33% were female, 61% were White, and 89% were on baseline antiplatelet therapy. Platelet aggregation to submaximal epinephrine induced a bimodal response; 15.5%, 16.8%, and 16.4% of patients demonstrated a hyperreactive platelet phenotype at baseline, POD1, and POD30, respectively. Platelet aggregation increased by 18.5% (P = .001) from baseline to POD1, which subsequently returned to baseline at POD30. After a median follow-up of 19 months, MACLEs occurred in 165 patients (57%). After adjustment for demographics, clinical risk factors, procedure type, and antiplatelet therapy, platelet hyperreactivity at POD1 was associated with a significant hazard of long-term MACLE (adjusted hazard ratio, 4.61; 95% confidence interval, 2.08-10.20; P < .001). CONCLUSIONS:Among patients with severe PAD, platelet activity increases after LER. Platelet hyperreactivity to submaximal epinephrine on POD1 is associated with long-term MACLE. Platelet activity after LER may represent a modifiable biomarker associated with excess cardiovascular risk.

BACKGROUND/UNASSIGNED:In-hospital mortality risk prediction is an important tool for benchmarking quality and patient prognostication. Given changes in patient characteristics and treatments over time, a contemporary risk model for patients with acute myocardial infarction (MI) is needed. METHODS/UNASSIGNED:Data from 313 825 acute MI hospitalizations between January 2019 and December 2020 for adults aged ≥18 years at 784 sites in the National Cardiovascular Data Registry Chest Pain-MI Registry were used to develop a risk-standardized model to predict in-hospital mortality. The sample was randomly divided into 70% development (n=220 014) and 30% validation (n=93 811) samples, and 23 separate registry-based patient characteristics at presentation were considered for model inclusion using stepwise logistic regression with 1000 bootstrapped samples. A simplified risk score was also developed for individual risk stratification. RESULTS/UNASSIGNED:The mean age of the study cohort was 65.3 (SD 13.1) years, and 33.6% were women. The overall in-hospital mortality rate was 5.0% (n=15 822 deaths). The final model included 14 variables, with out-of-hospital cardiac arrest, cardiogenic shock, and ST-segment elevation MI as the strongest independent predictors of mortality. The model also included age, comorbidities (dyslipidemia, diabetes, prior percutaneous coronary intervention, cerebrovascular disease, and peripheral artery disease), heart failure on admission, heart rate, systolic blood pressure, glomerular filtration rate, and hemoglobin. The model demonstrated excellent discrimination (C-statistic, 0.868 [95% CI 0.865-0.871]) and good calibration, with similar performance across subgroups based on MI type, periods before and during the COVID-19 pandemic, and hospital volume. The simplified risk score included values from 0 to 25, with mortality risk ranging from 0.3% with a score of 0 to 1 up to 49.4% with a score >11. CONCLUSIONS/UNASSIGNED:This contemporary risk model accurately predicts in-hospital mortality for patients with acute MI and can be used for risk standardization across hospitals and at the bedside for patient prognostication.

BACKGROUND:Myocardial infarction with nonobstructive coronary arteries (MINOCA) occurs in 6-15 % of MI patients. Cardiac magnetic resonance (CMR) imaging identifies MINOCA etiologies, but access may be limited. METHODS:We assessed associations between the index electrocardiogram (ECG) and CMR in MINOCA. Women with MI and < 50 % angiographic stenosis in all vessels were prospectively enrolled at 16 sites. CMR (median 6d from MI) was analyzed for late gadolinium enhancement (LGE), myocardial edema, and wall motion. We assessed ECGs for T-wave inversions (TWI), Q-waves (QW), ST-elevations (STE), ST-depressions (STD), and fragmented QRS complexes (fQRS). We calculated the DETERMINE score (# leads TWI + # fQRS +2*[# QW], excluding aVR, V1). RESULTS:Among 112 women with interpretable ECG, 81.3 % (91/112) had abnormal ECG; 50 % (56/112) had ≥1 TWI. CMR was abnormal in 74.1 % (83/112), with LGE in 49.1 % (55/112) and myocardial edema in 61.6 % (69/112). DETERMINE score ≥ 3 was associated with abnormal CMR (adjusted odds ratio [aOR] aOR 6.06 [1.89, 24.6], p = 0.002) and LGE (aOR 3.10 [1.26, 8.00], p = 0.013), but not edema (aOR 1.86 [0.80, 4.43], p = 0.152). TWI was also associated with abnormal CMR and LGE after adjustment (aOR 3.13 [1.08, 10.1], p = 0.036, aOR 3.23 [1.27, 8.63], p = 0.013, respectively), but not edema (aOR 1.26 [0.54, 2.96], p = 0.589). Specificity for abnormal CMR was 0.83 for DETERMINE score ≥ 3 and 0.75 for TWI. No other ECG findings were associated with CMR abnormality. CONCLUSION/CONCLUSIONS:DETERMINE score ≥ 3 and the presence of any TWI were associated with abnormal CMR and with LGE in MINOCA. Our findings demonstrate that the index ECG can provide insight on CMR findings but without sensitivity or specificity required to forgo the CMR. We reaffirm the central role of CMR in elucidating MINOCA pathophysiology.

Platelets are key mediators of atherothrombosis, yet, limited tools exist to identify individuals with a hyperreactive platelet phenotype. In this study, we investigate the association of platelet hyperreactivity and cardiovascular events, and introduce a tool, the Platelet Reactivity ExpreSsion Score (PRESS), which integrates platelet aggregation responses and RNA sequencing. Among patients with peripheral artery disease (PAD), those with a hyperreactive platelet response (>60% aggregation) to 0.4 µM epinephrine had a higher incidence of the 30 day primary cardiovascular endpoint (37.2% vs. 15.3% in those without hyperreactivity, adjusted HR 2.76, 95% CI 1.5-5.1, p = 0.002). PRESS performs well in identifying a hyperreactive phenotype in patients with PAD (AUC [cross-validation] 0.81, 95% CI 0.68 -0.94, n = 84) and in an independent cohort of healthy participants (AUC [validation] 0.77, 95% CI 0.75 -0.79, n = 35). Following multivariable adjustment, PAD individuals with a PRESS score above the median are at higher risk for a future cardiovascular event (adjusted HR 1.90, CI 1.07-3.36; p = 0.027, n = 129, NCT02106429). This study derives and validates the ability of PRESS to discriminate platelet hyperreactivity and identify those at increased cardiovascular risk. Future studies in a larger independent cohort are warranted for further validation. The development of a platelet reactivity expression score opens the possibility for a personalized approach to antithrombotic therapy for cardiovascular risk reduction.

Ischemic heart disease (IHD) affects more than 20 million adults in the United States. Although classically attributed to atherosclerosis of the epicardial coronary arteries, nearly half of patients with stable angina and IHD who undergo invasive coronary angiography do not have obstructive epicardial coronary artery disease. Ischemia with nonobstructive coronary arteries is frequently caused by microvascular angina with underlying coronary microvascular dysfunction (CMD). Greater understanding the pathophysiology, diagnosis, and treatment of CMD holds promise to improve clinical outcomes of patients with ischemic heart disease.

Coronary microvascular dysfunction (CMD) can cause myocardial ischemia in patients presenting with angina without obstructive coronary artery disease (ANOCA). Evaluating for CMD by using the thermodilution technique offers a widely accessible means of assessing microvascular resistance. Through this technique, 2 validated indices, namely coronary flow reserve and the index of microcirculatory resistance, can be computed, facilitating investigation of the coronary microcirculation. The index of microcirculatory resistance specifically estimates minimum achievable microvascular resistance within the coronary microcirculation. We aim to review the bolus thermodilution method, outlining the fundamental steps for conducting measurements and introducing an algorithmic approach (CATH CMD) to systematically evaluate the coronary microcirculation. Embracing a standardized approach, exemplified by the CATH CMD algorithm, will facilitate adoption of this technique and streamline the diagnosis of CMD.

BACKGROUND:Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US with the morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in cardiac intensive care unit (ICU). METHODS:We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock. We prepared a cardiac ICU dataset using MIMIC-III database by annotating with physician adjudicated outcomes. This dataset that consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database that was also annotated with physician adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. RESULTS:CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792-0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717-0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top ten predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, Blood urea nitrogen, Systolic blood pressure, Serum chloride, Serum sodium, and Arterial blood pH. CONCLUSIONS:The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for the millions of patients who suffer from myocardial infarction and heart failure.