Citations

Introduction: Circulating biomarkers may improve prediction of cardiovascular (CV) events in chronic coronary disease (CCD). The incremental value of biomarkers in CCD after adjustment for clinical factors, inducible ischemia and coronary artery disease (CAD) severity is uncertain. Methods: 756 ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) participants with site-determined moderate-severe ischemia and 9 assayed biomarkers (Figure) were included. Among participants with interpretable stress tests for ischemia and CAD by computed coronary tomography angiography (CCTA) (N=507), associations of biomarkers with the primary (CV death, myocardial infarction (MI), or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest) or secondary endpoints (CV death or MI) were evaluated using sequentially adjusted multivariable Cox proportional hazards models (Model 1=unadjusted, Model 2=clinical features, Model 3=Model 2 + multivessel CAD by CCTA, and Model 4=Model 3 + severe ischemia). Results: Median (IQR) participant age was 64 (57-70) years, 19% were female, 81% white, and 5% Hispanic. Hypertension (85%), diabetes (46%) and obesity (45%) were common, 28% had an estimated glomerular filtration rate <60 ml/min/1.73m 2 . Severe ischemia was present in 44%, and 39% had multivessel CAD (≥70% stenosis) by CCTA. Over 3.5 (2.4-4.7) years of follow-up there were 146 (19%) primary and 128 (17%) secondary endpoints. Soluble CD40L, GDF-15, NT-proBNP, hsTnT were associated with the primary and secondary endpoints in univariable and multivariable (adjusting for clinical features, multivessel CAD and severe ischemia) models. Conclusion: Biomarkers of inflammation (GDF-15), thrombosis (sCD40L), myocardial stretch (NT-proBNP) and injury (hs-troponin) were associated with CV endpoints in the ISCHEMIA trial independent of clinical features, multivessel CAD and severe ischemia.

Background: Long QT syndrome (LQTS) is a genetic heart disease affecting about 1 in 2000 persons. Around 40% of patients with LQTS can have a nondiagnostic, normal QTc at rest. The treadmill exercise stress test (TEST) can help unmask patients with concealed LQTS with a characteristic maladaptive, paradoxical increase in QTc ( 460 ms) during the recovery phase. In fact, the recovery phase QTc value has been added to the latest diagnostic score for LQTS. Objective: To characterize the TEST-derived QTc profile of untreated patients with LQTS and determine its diagnostic and prognostic utility. Methods: Retrospective analysis was performed on TEST data derived from 249 patients with LQTS (129 LQT1, 64 LQT2 and 56 LQT3) untreated at the time of their TEST compared with 236 controls. The Bazett’s corrected QTc was calculated at rest (sitting, supine, and standing), during exercise at 1 min, 3 mins, and peak exercise, and during recovery phase at 1, 3 and 5 mins. Results: The mean resting QTc was significantly higher in LQTS patients (457 ± 30 ms) compared to controls (420 ± 24 ms; p<0.0001) as well as at every stage during the TEST (p<0.001). For LQT1 patients, highest QTc was observed at peak exercise (mean 496 ± 38 ms) and during the third minute of recovery phase (mean 500 ± 40 ms). In contrast, LQT2 patients showed the lowest QTc (mean 452 ± 48 ms) during peak exercise but the greatest QTc latency (QTc at 5 min - QTc at 1 min recovery; mean QTc latency 33 ± 77 ms; p=0.005) compared to controls. For LQT3, the mean QTc was greatest at rest compared to controls (p<0.001), and there was no difference during exercise and recovery phases. Among 162 LQT1 and LQT2 patients that presented with concealed LQTS, 119 (73%) showed a maladaptive QT response. ROC analysis showed that in patients with LQT1, a maladaptive QTc > 523 ms at 3 min of recovery was highly predictive of breakthrough cardiac events (AUC 0.84; sensitivity 100%, specificity 72%); no such association was identified for LQT2 or 3. Conclusions: This comprehensive characterization of the QTc response before, during, and after a TEST in the largest untreated cohort of patients with LQTS provides important genotype-specific characteristics and can be used as a predictor for potential LQT1-triggered cardiac events after diagnosis and treatment.

Introduction: Deep-learning artificial intelligence (AI) algorithms can identify hypertrophic cardiomyopathy (HCM) from the electrocardiogram (ECG). Preliminary data indicate that AI-ECG may also track treatment response in HCM patients (pts) receiving mavacamten (Mava). In this placebo-controlled analysis, we investigated the performance of AI-ECG in HCM detection and its changes during Mava treatment compared to placebo in the phase 3, EXPLORER-HCM trial. Methods: Pts with non-paced ECGs enrolled in EXPLORER-HCM, evaluating Mava over 30 weeks of treatment, were included. All pts had a 12-lead ECG pre-treatment, at each study visit, and 8 weeks after treatment end. Two independently developed AI-ECG convolutional neural network algorithms from the Mayo Clinic and the University of California, San Francisco (UCSF) were applied to all ECGs suitable for analysis. Serial AI-ECG-predicted scores, which correlate with HCM likelihood (range 0-1), were examined between Mava and placebo groups. Results: A total of 101 Mava and 110 placebo pts were included (mean age 58.6, female 40%). At baseline, the mean Mayo and UCSF AI-ECG scores for HCM were 0.82 and 0.65, with 94% and 88% of pts exceeding the respective diagnostic thresholds of 0.11 (Mayo) and 0.4 (UCSF). During 30 weeks of follow-up in the Mava group, the algorithms showed a mean AI-ECG score reduction of 0.32 (Mayo) and 0.17 (UCSF) in all pts between start and end of treatment. In comparison, AI-ECG scores decreased by 0.05 (Mayo) and increased by 0.02 (UCSF) in the placebo group (Mava vs placebo, t-test p-values <0.001 for both algorithms; Figure ) Conclusion: In the EXPLORER-HCM trial population, two independent deep-learning AI-ECG algorithms correctly identified most HCM pts and demonstrated significant reductions in predicted HCM scores after treatment with Mava when compared to placebo. This relative normalization of the ECG suggests disease-modifying properties of Mava, deserving further investigation.

Introduction: Obstructive sleep apnea (OSA) is common in heart failure, although the impact of OSA treatment remains unclear. We assessed the association between adherence to positive airway pressure (PAP) therapy and healthcare resource utilization in patients with OSA and heart failure with reduced ejection fraction (HFrEF). Methods: Administrative insurance claims data linked with objective PAP therapy usage data from patients with OSA and HFrEF were used to measure the association of PAP adherence and a composite clinical outcome of hospitalization and emergency room (ER) visits. US Medicare defines PAP compliance as usage of the device for at least 4 hours/night for 70% of nights in a consecutive 30-day period over 90 days. PAP adherent patients met these criteria for all 4 90-day periods, while PAP non-adherent patients did not meet these criteria for any 90-day period over one year. Propensity score methods were used to create well matched groups with differing PAP adherence levels. Results: The study cohort consisted of 3,182 eligible patients (69.9% male, mean age 59.7 years) of whom 39% were considered adherent to PAP therapy (29% intermediate-adherent, 31% non-adherent). In the year after PAP prescription, after propensity score matching, PAP adherent patients had fewer hospital visits than non-adherent patients, driven by a 24% reduction in ER visits for adherent patients (Table 1a). These results remained consistent with inverse probability weighting and furthermore showed there were no statistically significant differences between intermediate-adherent and non-adherent patients for any outcome (Table 1b). Conclusions: Treating OSA with PAP therapy in patients with HFrEF is associated with a reduction in the composite outcome of hospitalization and ER visits, driven primarily by a reduction in ER visits. Consistent adherence to PAP therapy is required to realize these benefits.

Introduction: Atrial natriuretic peptide (ANP) is the most potent endogenous activator of the guanylyl cyclase A receptor (GC-A) mediating cardiorenal protective actions by increasing cGMP production. To test the hypothesis that acute decompensated heart failure (ADHF) is associated with increased circulating ANP but with reduced GC-A potency, we developed an ex-vivo precision medicine assay to quantify the potency of circulating ANP in plasma from healthy and ADHF subjects. Further, we assessed the ex-vivo potency of MANP, a novel ANP analog with greater cGMP activation than ANP, also using plasma of healthy and ADHF subjects. Methods: For the potency assay, we engineered HEK293 cells to overexpress human GC-A. Plasma from individual healthy (n=4) and ADHF (n=4) subjects was incubated in the assay and cGMP was assessed. The endogenous ANP derived cGMP was evaluated against the cGMP response of equimolar synthetic ANP (S ANP ) to assess potency of ANP from each cohort. MANP mediated cGMP generation was also assessed in the assay using healthy and ADHF plasma. Results: Healthy plasma, in which plasma ANP was 26±5 pg/mL, generated 10.3±0.7 pmol/mL of cGMP. S ANP added to the assay to mimic endogenous ANP levels in healthy subjects produced 7.8±0.5 pmol/mL of cGMP. ADHF plasma with markedly elevated ANP (350±57pg/mL) generated 23.5±3.1 pmol/mL of cGMP. S ANP mimicking plasma ANP in ADHF produced 59.7±13.8 pmol/mL of cGMP, which was greater than cGMP generated from ADHF patient-derived plasma (p<0.05), consistent with reduced potency of endogenous ANP in ADHF. Low treatment dose of MANP (LD MANP ; 10 -8 M) in healthy plasma increased cGMP to 45±5.5 pmol/ml while high treatment dose (HD MANP ; 10 -6 M) increased cGMP to 167.1±14.1 pmol/mL. In markedly elevated ANP ADHF plasma, LD MANP increased cGMP to 60.4±9.7 pmol/mL while HD MANP increased cGMP to 200.8±5.4 pmol/mL (p<0.03 for both) demonstrating similar cGMP production by MANP in healthy and ADHF plasma. Conclusions: Employing a novel ex-vivo precision medicine ANP/GC-A/cGMP assay, we conclude that ADHF is characterized by elevated ANP yet with impaired potency in GC-A activation and cGMP generation. MANP overcomes this impairment and serves as a potential efficacious GC-A activating therapeutic in heart failure.

Introduction: Valve-in-valve transcatheter mitral valve replacement (ViV-TMVR) and redo surgical mitral valve replacement (redo-SMVR) are two treatment strategies for patients with bioprosthetic mitral valve dysfunction. We conducted a systematic review and meta-analysis to compare the outcomes of ViV-TMVR versus redo-SMVR. Methods: We searched PubMed, EMBASE, Cochrane, and Google Scholar for studies comparing outcomes of ViV-TMVR versus redo-SMVR in degenerated bioprosthetic mitral valves. We used a random effects model to calculate risk ratios (RRs) with 95% confidence intervals (CIs). Outcomes included in-hospital, 30-day, 1-year, and 2-year mortality, stroke, bleeding and vascular complications, acute kidney injury (AKI), arrhythmias, and hospital length of stay (LOS). Results: Six observational studies with a total of 1,339 subjects were included. Median follow-up was 2 years. Compared to redo-SMVR, ViV-TMVR was associated with significantly lower in-hospital (RR 0.42; 95% CI 0.25-0.70; p=0.001) and 30-day mortality (RR 0.49; 95% CI 0.24-0.98; p=0.04) but similar 1-year (RR 0.97; 95% CI 0.64-1.47; p=0.89) and 2-year mortality (RR 1.14; 95% CI 0.71-1.81; p=0.59). Stroke, bleeding, AKI, and arrhythmias were less frequent and hospital LOS was shorter with ViV-TMVR, however vascular complications were similar between both strategies. Conclusion: In patients with degenerated bioprosthetic mitral valves, ViV-TMVR is associated with better outcomes compared to redo-SMVR including lower in-hospital and 30-day mortality, lower complication rates, and shorter hospital LOS. Given these findings and the ongoing advances in transcatheter therapeutics, ViV-TMVR may be preferred over redo-SMVR, particularly in individuals at intermediate-to-high surgical risk.

Introduction: Persons who conceived through Assisted Reproductive Technology (ART) have an increased risk of obstetric-associated complications. Hypothesis: ART is associated with increased acute cardiovascular (CV) complications during delivery admissions. Methods: We used data from the National Inpatient Sample (2008-2019) while utilizing ICD-9 or ICD-10 codes to identify delivery hospitalizations and ART procedure. Results: A total of 45,867,086 weighted delivery cases were identified, of which 0.24% were among individuals who conceived through ART (n=108,542). Persons with an ART history were older at the time of delivery (median 35 vs. 28 years, p<0.01) and had higher prevalence of hypertension, gestational diabetes, and dyslipidemia (all, p<0.01). After adjustment for age, race/ethnicity, comorbidities, multiple gestation, insurance, and income, ART remained an independent predictor of peripartum CV complications including preeclampsia (aOR 1.48 [1.45-1.51], p<0.01), heart failure (aOR 1.94 [1.10-3.40], p<0.01), and cardiac arrhythmias (aOR 1.39 [1.30-1.48], p<0.01) compared to natural conception. Likewise, the risk of acute kidney injury (aOR 2.57 [2.25-2.92], p<0.01), stroke (aOR 1.57 [1.23-2.01], p<0.01), pulmonary edema (aOR 2.29 [2.02-2.61], p<0.01), and venous thromboembolism (aOR 1.92 [1.63-2.25], p<0.01) were higher with ART. However, higher odds of developing peripartum cardiomyopathy or acute coronary syndrome were not associated with ART. Length of stay (3 vs. 2 days, p<0.01) and cost of hospitalization ($5,903 vs. $3,922, p<0.01) were higher for deliveries among persons with history of ART. Conclusions: Persons who conceived with ART had higher risk of preeclampsia, heart failure, cardiac arrhythmias, pulmonary edema, AKI, stroke, and VTE during their delivery hospitalizations. This may in part contribute to increased resource utilization seen in patients conceiving with ART

Background: We developed a deep learning algorithm to predict elevated coronary artery calcium (CAC) score from 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that this CAC-ECG algorithm will independently predict long-term survival. Methods: We leveraged a historical cohort of 43,210 consecutive patients who from the years 1997-2020 underwent clinically indicated ECG-gated unenhanced chest computed tomography (CT) to identify and quantify CAC and had an ECG within 1 year of the CT. Data on cardiovascular risk (CV) factors, and to calculate the Pooled Cohort Equation (PCE) for ASCVD was collected as part of preventive cardiology or general medical evaluations. We used the oldest CAC in record and excluded those taking statins at the time of the CAC. The algorithm was trained in 60% of the cohort, and the association between CAC-ECG and survival was evaluated with multivariate cox proportional hazard models in 40% of the remaining observations. Results: Of the 17,284 evaluated patients, mean ± SD age 55.9 ± 9.9, 33% female, 3,714 (21%) had elevated CAC ≥ 300. During an average of 15±5.9 years follow-up, 848 (5%) patients died. The algorithm’s area under the receiving operating characteristics curve (ROC), sensitivity, specificity, and accuracy to detect a CAC ≥ 300 were 0.83, 0.90%, 0.56%, 0.60%. Those with elevated CAC had a nearly two-fold risk of death, a value similar to those deemed positive by the ECG-CAC algorithm ( Figure A ). Risk of death increased with CAC-ECG probability quartiles ( Figure A ). The CAC-ECG algorithm enhanced predicted capabilities of the PCE across all ASCVD risk subgroups ( Figure B ), as well as in those with no CAC and elevated CAC ( Figure C ), all p for trend <0.001. Conclusions: A deep learning-enabled CAC-ECG algorithm was independently associated with long-term survival and enhanced current risk prediction paradigms. The CAC-ECG algorithm could help identify individuals at risk in primary prevention of CV disease.

Case Presentation: A 69-year-old right-handed man presented with new left-sided weakness and recurrent ischemic strokes. His had history of coronary artery disease, diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease stage 3, obstructive sleep apnea and obesity. Plasma low-density lipoprotein was 127 mg/dL. Brain MRI showed diffusion restriction in the left caudate head, left corona radiata, left anterior lentiform nucleus, and left periventricular white matter from acute to subacute infarction. CT angiogram showed diffuse atheromatous disease in the anterior and posterior circulation, bilateral mild cervical internal carotid artery stenosis, and calcifications in the aortic arch ( Figure 1 ). Transthoracic echocardiogram (TTE) showed mild generalized left ventricular hypokinesis (ejection fraction 45%) without thrombus. Transesophageal echocardiogram (TEE) showed complex mobile atherosclerotic plaques in the aortic arch ( Figure 2 ) after which Apixaban & Clopidogrel were started and statins increased. At 1-year follow-up there was no recurrent stroke. Discussion: This case highlights the role of 3D TEE in the detection of complex aortic arch atherosclerotic plaque with mobile components as the etiology of recurrent embolic stroke. 2D TTE & TEE and CT chest were unable to show mobile components in the aortic arch plaque. Many patients identified as having “cryptogenic stroke” after 2D TTE & TEE may in fact have mobile aortic arch plaques which can only be delineated by 3D TEE, thus reducing the need for further workup and expediting appropriate treatment.

Introduction: Polygenic risk scores (PRS) for coronary heart disease (CHD) are associated with incident CHD events. We assessed whether the strength of this association differs over the life course. Methods: We studied 7,958 genotyped participants of European-ancestry without CHD enrolled in the Atherosclerosis Risk in Communities Study (ARIC). Participants were 45-64 years old at the baseline visit occurring 1987-89 with follow up available through 2015. A genome-wide PRS for CHD composed of 1.7 million variants was computed for each participant. Incident CHD was defined as clinically adjudicated myocardial infarction including silent infarction, coronary revascularization, or fatal coronary event. An extended Cox model was constructed with age as the model time scale. The association of PRS with incident CHD was assumed to be log-linear and further modeled as a function of age assuming differing piecewise constant effect sizes across the intervals ≤55, 55-65, 65-80, and ≥80 years. The regression model also included the following covariates: sex, systolic blood pressure, total and HDL cholesterol, history of diabetes, antihypertensive medication use, and smoking status. Results: In the ARIC cohort (median enrollment age 54, interquartile range [IQR] 49-59, female 52%), over a median of 24.5 years (IQR 14.4-28.1), there were 1,872 incident CHD events. In comparison to average polygenic risk for CHD (50th percentile of PRS distribution), harboring high polygenic risk (95th percentile) was associated with a 2.9-fold increased risk of incident CHD (95% CI 2.12-3.95) up to age 55 years, independent of conventional risk factors. The strength of association was attenuated later in life (Hazard ratio, age 55-65, 2.5 [2.14-2.84]; age 65-80, 1.87 [1.68-2.07]; age ≥80, 1.78 [1.38-2.31]) Conclusions: A genome-wide PRS for CHD is more strongly associated with incident coronary events at younger age. The strength of association declined later in life while remaining significant. The age-dependent association of PRS with CHD may need to be accounted for in risk prediction models.