Oh, Jae K.

Background: Ventricular-arterial coupling (VAC) refers to the ratio of afterload (effective arterial elastance) to contractility (left ventricular (LV) end-systolic elastance) as an integrated marker of cardiac performance. Echocardiography might be used to evaluate VAC through a simple time-efficient bedside evaluation. Hypothesis: VAC is useful for mortality risk stratification in an unselected cardiac intensive care unit (CICU) cohort. Aims: We sought to determine whether the VAC ratio, defined using the ratio of LV end-systolic volume (LVESV) to stroke volume (SV), predicted mortality in the CICU. Methods: Consecutive patients admitted to the Mayo Clinic CICU between 2007 and 2018 were included. The simplified VAC ratio was calculated as the LVESV calculated from 2D measurements using the Teichholz formula divided by the SV calculated from the LV outflow tract pulsed wave Doppler measurement, with values >1 reflecting ventricular-arterial uncoupling. The main outcome was all-cause in-hospital mortality. Results: We included 4685 patients with a median age of 69 (58-79) years; 37% were females. The median VAC ratio was 1.0 (0.7 to 1.8) and 21% of patients had a VAC ratio >2. Most clinical and echocardiographic variables differed in patients with higher VAC ratio, reflecting greater severity of illness with worse cardiac systolic and diastolic function. A total of 329 (7.0%) patients died during hospitalization. An incremental association was observed between a higher VAC and in-hospital mortality (unadjusted OR 1.22, 95% CI 1.16-1.29, p<0.001). Patients with a VAC ratio >1 (unadjusted OR 2.85, 95% CI 2.22-3.69, p <0.001) and especially those with VAC ratio >2 (unadjusted OR 4.28, 95% CI 3.24-5.69, p<0.001) had higher in-hospital mortality overall and in each admission diagnosis subgroup of interest ( Figure ). After multivariable adjustment, patients with a VAC ratio >2 remained at higher risk of in-hospital mortality (adjusted OR 1.63, 95% CI 1.17-2.28, p = 0.01). Conclusions: Ventricular-arterial uncoupling was associated with worse outcomes in an unselected CICU cohort. The echocardiographic VAC ratio is a simple non-invasive bedside measure that can be used for risk prediction in the CICU.

Background: AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed. Aim: To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone. Methods: 286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs. Results: AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD. Conclusions: Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.

Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD by AI-ECG is associated with in-hospital and one-year mortality in CICU patients. Methods: In this retrospective cohort study, we included consecutive unique adults admitted to the Mayo Clinic CICU from 2007 to 2018 with an admission AIECG, which AI assigned LVDD grade (0 to 3). Medial mitral E/e’ ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. AIECG and TTE assessment of LVDD were used to assign patients to four groups based on TTE as gold standard: true and false negative, and true and false positive. In-hospital mortality was evaluated using multivariable logistic regression. One-year survival was evaluated using Kaplan-Meier curves and multivariable Cox proportional-hazards. Results: We included 11,868 patients (median age 69.5 years, 37.7% females); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grades were: grade 0 (normal), 33%; grade 1, 7%; grade 2, 39%; grade 3, 21%. In-hospital (adjusted OR) and one-year (adjusted HR) mortality increased in each higher AIECG LVDD grade (Figure A/B), before and after adjustment including TTE measurements. Patients with grade 2-3 LVDD by AIECG and medial mitral E/e’ ratio >15 by TTE (true positive) had the highest in-hospital (adjusted OR 2.5 [1.7-3.9]) and one-year (adjusted HR 1.9 [1.5-2.5]) mortality (Figure C/D), and mortality was elevated similarly in patients with either grade 2-3 LVDD by AIECG (false positive) or medial mitral E/e’ ratio >15 by TTE (false negative). Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures (true positive) were at highest risk.

Abstract Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for LVDD detection has opened new avenues. Yet, whether AI-ECG-detected LVDD correlates with long-term AF risk remains unexplored. Methods We performed a retrospective study among all patients with a comprehensive LVDD assessment from the test population of the AI-ECG LVDD study between September 2001 and June 2022. Patients with prior documented AF according to health records or previous ECG were excluded. Patients were classified by AI-ECG as normal diastolic function (DF), grade 1 (G1), grade 2 (G2), or grade 3 (G3) LVDD. We assessed the risk of AF across AI-ECG LVDD grade, with normal DF as reference, with competing risk models. Results Of 97,023 patients free of AF at baseline, 63,533 (65.4%), 10,776 (11.1%), 18,940 (19.5%), and 3,774 (3.9%) were classified as normal DF, G1-LVDD, G2-LVDD, and G3-LVDD, respectively. Worse AI-ECG LVDD was associated with older age, lower left ventricular ejection fraction (LVEF), and higher rates of HF, diabetes, hypertension, chronic pulmonary disease, renal failure, and cardiovascular disease (p-of-trend&lt;0.001 for all). Over a median follow-up of 4.8 years (interquartile range 1.8-9.2 years), new-onset AF occurred in 6,795 (7.0%) patients. In multivariable survival analysis, adjusted to multiple risk factors including LVEF and clinical HF, G2 and G3 LVDD, representing patients with high filling pressures, were independently associated with a higher AF-risk [G2-LVDD: adjusted hazard ratio (aHR) 1.64 (95% confidence interval (95%CI) 1.52-1.73), G3-LVDD: aHR 2.24 (95%CI 2.02-2.48)]. Conclusion The AI-ECG LVDD algorithm independently predicts long-term AF risk, regardless of LVEF and clinical HF status, highlighting its potential for identifying high-risk populations and guiding proactive monitoring and management.

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (&gt;moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with &gt;moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Aarhus University Dagmar Marshalls Fond. Background The assessment of left ventricular (LV) function through transthoracic echocardiography (TTE) is essential for guiding diagnosis and treatment in cases of cardiogenic shock (CS). Myocardial global longitudinal strain (GLS) provides an objective and reproducible measure of systolic function, independently predicting outcomes in acute heart failure, regardless of left ventricular ejection fraction (LVEF). However, whether assessment of GLS can predict mortality in patients with CS remains unknown. Purpose In this study, we aimed to evaluate the prognostic significance of GLS in patients with CS. Methods Data from cardiac intensive care unit (CICU) patients admitted between 2007 and 2018 with CS who received vasoactive agents and/or mechanical circulatory support were analysed. GLS (as absolute values) and biplane LVEF were measured via TTE performed within 24 hours of CICU admission. Patients with CS were categorised based on the GLS quartile: mild (GLS &gt;9.7%), moderate (7.0% &lt; GLS &lt;9.6%), severe (5.0% &lt; GLS &lt; 6.9%), and very severe (GLS ≤ 4.9%). CICU and in-hospital mortality were compared across GLS categories, and the relationship between GLS and mortality was assessed using multivariable logistic regression. Results The study included 626 patients with a median age of 68.6 years, a median LVEF of 31% (IQR 24 to 40%), and a median GLS of 7.0% (IQR 5.0 to 9.7%). In-hospital mortality was 29.1%. As GLS deteriorated, there was an increasing need for vasopressors (P&lt;0.001). Patients with progressively lower GLS exhibited significantly higher CICU and in-hospital mortality across the GLS quartiles (Figure A; P&lt;0.001). Patients in the severe (adjusted OR 2.67; p&lt;0.001) and very severe category (adjusted OR 3.54; P&lt;0.001) had significantly increased risk of in-hospital death compared with mild category. In multivariable analysis, every 1% decrease in GLS corresponded to a 14% higher risk of in-hospital mortality (P&lt;0.001). Finally, GLS outperformed LVEF for discrimination of in-hospital mortality (P=0.002), and when both measurements were included in the same multivariable model, only GLS remained significant. When patients were divided according to whether they had GLS and/or LVEF below the median value, patients with low GLS had higher mortality irrespective of the LVEF (Figure B). Conclusion In patients with CS, GLS assessment surpasses LVEF in its prognostic value and improves risk stratification. Routine evaluation of GLS should be considered for patients with CS.Figure AFigure B

Abstract OBJECTIVES The objective of this analysis was to assess the normal haemodynamic performance of contemporary surgical aortic valves at 1 year postimplant in patients undergoing surgical aortic valve replacement for significant valvular dysfunction. By pooling data from 4 multicentre studies, this study will contribute to a better understanding of the effectiveness of surgical aortic valve replacement procedures, aiding clinicians and researchers in making informed decisions regarding valve selection and patient management. METHODS Echocardiograms were assessed by a single core laboratory. Effective orifice area, dimensionless velocity index, mean aortic gradient, peak aortic velocity and stroke volume were evaluated. RESULTS The cohort included 2958 patients. Baseline age in the studies ranged from 70.1 ± 9.0 to 83.3 ± 6.4 years, and Society of Thoracic Surgeons risk of mortality was 1.9 ± 0.7 to 7.5 ± 3.4%. Twenty patients who had received a valve model implanted in fewer than 10 cases were excluded. Ten valve models (all tissue valves; n = 2938 patients) were analysed. At 1 year, population mean effective orifice area ranged from 1.46 ± 0.34 to 2.12 ± 0.59 cm2, and dimensionless velocity index, from 0.39 ± 0.07 to 0.56 ± 0.15. The mean gradient ranged from 8.6 ± 3.4 to 16.1 ± 6.2 mmHg with peak aortic velocity of 1.96 ± 0.39 to 2.65 ± 0.47 m/s. Stroke volume was 75.3 ± 19.6 to 89.8 ± 24.3 ml. CONCLUSIONS This pooled cohort is the largest to date of contemporary surgical aortic valves with echocardiograms analysed by a single core lab. Overall haemodynamic performance at 1 year ranged from good to excellent. These data can serve as a benchmark for other studies and may be useful to evaluate the performance of bioprosthetic surgical valves over time. Clinical trial registration number NCT02088554, NCT02701283, NCT01586910 and NCT01531374.

Background: CP/R-CP may be associated with subtle features of ventricular dysfunction which are not easily detectable with the use of conventional echocardiographic modalities. The aim of this study was to evaluate myocardial mechanics by 2D-STE (2 Dimensional speckle-tracking echocardiography) between CP and R-CP. We hypothesized that strain/ strain rate (S/SR) would be worse in the R-CP patients due to radiation-induced myocardial injury. Methods: We analyzed 180 patients (62% male, age 62±13 years, 127 CP and 53 R-CP) with suitable echocardiographic images and normal left ventricular ejection fraction (LVEF>55%). The two groups were similar in terms of age, cardiovascular symptoms, risk factor profile and LVEF (Table). Selected images were analyzed off-line using Velocity Vector Imaging technology. The diagnosis of CP was based on clinical parameters as well as echocardiography and/or hemodynamic cardiac catheterization. Patients in whom radiation therapy was the primary etiology of CP were classified as R-CP. Global and segmental circumferential, radial, longitudinal, systolic and early diastolic strain and strain rate were analyzed in all patients. Results: Global and segmental radial (GRadS), global longitudinal (GLS), global basal (GBS), global mid-segment (GMS), septal wall (SWS), and anterior wall (AWS) strain was significantly reduced in the R-CP patients compared with the CP patients (Table). Antero-septal and anterior wall diastolic strain and strain rates (AS + AW Sre, SR) were reduced in the R-CP group. The best differentiators between the two groups were antero-septal (SAS), basal anterior (BAS) and mid anterior (MAS) segmental strain, based on receiver operating characteristic (ROC) and area under the curve (AUC) analysis. ROC calculations were AUC: SAS 0.67, SBA 0.69, and SMA 0.71. Conclusion: The study showed that compared with CP, R-CP is associated more with myocardial dysfunction, likely due to radiation-induced myocardial injury.

Background: The aim of this study was to determine whether adding DE-CMR and/or TTE based parameters beyond known clinical factors provides incremental improvement of predicting long-term outcomes in STEMI patients who undergo acute reperfusion therapy. Methods and Results: One hundred twenty patients with first time STEMI who underwent PCI (74%), fibrinolysis (3%), and both (19%), were studied by both delayed enhancement cardiac magnetic resonance (DE-CMR) and transthoracic echocardiogram (TTE) during the acute phase of myocardial infarction. The primary endpoint was major adverse cardiac events (MACE). There were 28 events (6 deaths, 13 recurrent ischemia, and 9 heart failure) over a median follow-up of 4 years. We defined incremental increase in prognostic value by increase in Cox proportional hazards global chi square (χ 2 ) and/or increase in c-index with the addition of TTE and/or CMR parameters after adjusting for clinical variables. Among the clinical data, heart rate (HR: 1.02, p=0.047) and troponin (HR: 1.13, p=0.013) predicted MACE (χ 2 : 12.4, P=0.002; C index 0.69). After adjusting for these clinical factors, adding TTE parameters (left ventricular end systolic volume index [HR: 1.05, p<0.0001] and mitral valve deceleration time [HR: 0.99, p=0.036]), increased the global χ 2 to 24.6 and C index to 0.74 (p<0.0001). When CMR parameter (infarct size [HR: 2.92, p<0.0001]) was added to the clinical data alone, there was greater increase in global χ 2 to 36.8 and C index to 0.83 (p<0.0001). Addition of DE- CMR to clinical data combined with TTE measures showed significant incremental value with increased χ 2 difference of 17.6 (p<0.0001). There was no significant incremental value of adding TTE to clinical data combined with DE-CMR (p>0.05). Conclusion: DE-CMR provides significant incremental value for prognosticating adverse cardiac events over clinical data alone (or when in combination with TTE) in STEMI patients who undergo reperfusion therapy.