Citations

AbstractIntroductionAlthough prior studies indicate that a QTc > 500 ms on a single baseline 12‐lead electrocardiogram (ECG) is associated with significantly increased risk of arrhythmic events in long QT syndrome (LQTS), less is known about the risk of persistent QT prolongation. We sought to determine QTc persistence and its prognostic effect on breakthrough cardiac events (BCEs) among pediatric patients treated for LQTS.MethodsWe performed a retrospective analysis of 433 patients with LQTS evaluated, risk‐stratified, and undergoing active guideline‐based LQTS treatment between 1999 and 2019. BCEs were defined as arrhythmogenic syncope/seizure, sudden cardiac arrest (SCA), appropriate VF‐terminating ICD shock, and sudden cardiac death (SCD).ResultsDuring the median follow‐up of 5.5 years (interquartile range [IQR] = 3–9), 32 (7%) patients experienced a total of 129 BCEs. A maximum QTc threshold of 520 ms and median QTc threshold of 490 ms were determined to be strong predictors for BCEs. A landmark analysis controlling for age, sex, genotype, and symptomatic status demonstrated models utilizing both the median QTc and maximum QTc demonstrated the highest discriminatory value (c‐statistic = 0.93–0.95). Patients in the high‐risk group (median QTc > 490 ms and maximum QTc > 520 ms) had a significantly lower BCE free survival (70%–81%) when compared to patients in both medium‐risk (93%–97%) and low‐risk (98%–99%) groups.ConclusionsThe risk of BCE among patients treated for LQTS increases not only based upon their maximum QTc, but also their median QTc (persistence of QTc prolongation). Patients with a maximum QTc > 520 ms and median QTc > 490 ms over serial 12‐lead ECGs are at the highest risk of BCE while on guideline‐directed medical therapy.

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT < 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values < 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (< 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for >10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for < 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-04.

Background Symptomatic limitations in apical hypertrophic cardiomyopathy may occur because of diastolic dysfunction with resultant elevated left ventricular filling pressures, cardiac output limitation to exercise, pulmonary hypertension (PH), valvular abnormalities, and/or arrhythmias. In this study, the authors aimed to describe invasive cardiac hemodynamics in a cohort of patients with apical hypertrophic cardiomyopathy. Methods and Results Patients presenting to a comprehensive hypertrophic cardiomyopathy center with apical hypertrophic cardiomyopathy were identified (n=542) and those who underwent invasive hemodynamic catheterization (n=47) were included in the study. Of these, 10 were excluded due to postmyectomy status or incomplete hemodynamic data. The mean age was 56±18 years, 16 (43%) were women, and ejection fraction was preserved (≥50%) in 32 (91%) patients. The most common indication for catheterization was dyspnea (48%) followed by suspected PH (13%), and preheart transplant evaluation (10%). Elevated left ventricular filling pressures at rest or exercise were present in 32 (86%) patients. PH was present in 30 (81%) patients, with 6 (20%) also having right‐sided heart failure. Cardiac index was available in 25 (86%) patients with elevated resting filling pressures. Of these, 19 (76%) had reduced cardiac index and all 6 with right‐sided heart failure had reduced cardiac index. Resting hemodynamics were normal in 8 of 37 (22%) patients, with 5 during exercise; 3 of 5 (60%) patients had exercise‐induced elevation in left ventricular filling pressures. Conclusions In patients with apical hypertrophic cardiomyopathy undergoing invasive hemodynamic cardiac catheterization, 86% had elevated left ventricular filling pressures at rest or with exercise, 81% had PH, and 20% of those with PH had concomitant right‐sided heart failure.

Introduction: A significant portion of patients with aortic stenosis (AS) have low-gradient AS (LGAS) defined as aortic valve area ≤ 1.0 cm 2 and a trans-aortic mean systolic gradient and peak velocity < 40 mmHg and < 4 m/s, respectively. LGAS has been shown previously to have worse mortality compared to high-gradient AS (HGAS). Etiologies for LGAS include diastolic dysfunction, heart failure, other significant valvular disease, and atrial fibrillation (AF). AF is associated with worse outcomes compared to sinus rhythm (SR) in HGAS. AF is common in LGAS, but whether AF is associated with worse outcomes versus SR in LGAS has not been elucidated. Hypothesis: We hypothesize that AF is associated with higher mortality compared to SR in patients with LGAS. Methods: We retrospectively identified and reviewed charts of patients who were diagnosed with LGAS on transthoracic echocardiogram (TTE) from 2010-2020 at Mayo Clinic. Patient were classified according to rhythm at the time of TTE and history of AF was ascertained among those in SR. Patents were divided into 3 groups; SR without history of AF (group 1), SR with history of AF (group 2), and AF during TTE (group 3). Primary endpoints of overall mortality and cardiac mortality were ascertained. Results: A total of 3400 patients were included: n=2036 in group 1, n=519 in group 2, and n=845 in group 3. After adjusting for age, sex, and the Charlson index, group 2 and group 3 patient had significantly higher overall mortality (HR 1.22 and HR 1.52, respectively, p<0.0001 for both) and cardiac mortality (HR 1.37 and HR 2.05, respectively, p<0.0001 for both), Figure . Conclusions: In LGAS, AF is a significant prognostic indicator of overall mortality and cardiac mortality. LAGS associated with AF may need different management strategies given the higher mortality associated with the presence of AF.

Introduction: Aldosterone (Aldo) increases sodium and water reabsorption and mediates inflammatory processes. Its relationship to co-morbidities has not been well defined in the general population. Hypothesis: We addressed the hypothesis that plasma aldosterone concentration (PAC) is associated with metabolic and cardiorenal diseases in the general community. Methods: We determined PAC in a general community-based cohort from Olmsted County, MN (n=1,674), and defined correlations with cardiorenal, metabolic diseases and myocardial function and structure. Results: PAC was detected in all subjects and ranged from 2.5 to 91 ng/dl (mean value 6.7 ng/dl, +/- SD 6.6). Multivariate analysis adjusted for age, gender, BMI, ANP and BNP showed highly significant correlations between PAC and hypertension (HTN) (p<.0001, OR=2.28, CI:1.91-2.71), atrial fibrillation (p<.01), chronic kidney disease (CKD) (p<.0001, OR=1.64, CI:1.33-2.02), central obesity (p<.0001, OR=1.61, CI:1.27-2.05), hypertriglyceridemia (p<.003), prevalence of metabolic syndrome (MetS) (p<.0007, OR=1.40, CI=1.15-1.71), obesity (p<.0001, OR=1.48, CI:1.27-1.73), and concentric left ventricular hypertrophy (cLVH) (p=.01). Recognizing that ANP and BNP are known inhibitors of Aldo, we defined the relationship between these cardiac hormones (NTpro-ANP and NT-proBNP) and PAC and observed highly significant inverse relationships with NTpro-ANP (p=0.0009), NTpro-BNP (p=0.0003) in the general population. Conclusions: In conclusion, in the general community, PAC is strongly correlated with renal, metabolic and cardiovascular diseases as well as cardiac remodeling. Specifically, increasing PAC is strongly associated with HTN, CKD, obesity, MetS, cLVH, and lower levels of natriuretic peptides. Our data suggest a key role of Aldo as a possible biomarker and/or mediator of human cardiorenal and metabolic diseases even in the general adult population, prior to overt disease. This study also supports a possible counter regulatory relationship between cardiac natriuretic peptides and PAC. Further studies are warranted to evaluate possible therapeutic interventions targeting Aldo as a strategy to delay disease progression.

Background: Non-cardiac surgery (NCS) in patients with severe aortic stenosis has traditionally been associated with high perioperative mortality. Aortic valve replacement is recommended prior to elective NCS, but the impact of this strategy has not been systematically assessed. Methods: A retrospective review was performed of all patients with severe aortic stenosis undergoing major NCS at Mayo Clinic between 2000 and 2013. Patients undergoing aortic valve intervention (AVI) prior to NCS were compared to those undergoing surgery without prior AVI (control). Patients undergoing emergency surgery were excluded. Primary endpoint was occurrence of major adverse cardiac events (MACE; combination of death, stroke, myocardial infarction, new/worsening heart failure, ventricular arrhythmia) within 30 days of NCS; secondary endpoints were mortality at 30 days and 1 year after NCS. Results: 180 patients (age 74.5 ± 10.5 years, 64% male) who underwent NCS within 12 months after AVI, were compared to 232 patients (age 76.6 ± 10.7 years, 56% male) in the control group. Patients undergoing AVI were more often symptomatic than controls (41% vs 26%, p = 0.002). MACE was significantly higher in the control group (21.6% vs 5.6%, p < 0.001), mainly due to the increased risk of heart failure in the postoperative period in this group (18.5% vs 2.2%, p < 0.001). There was no significant difference in the rate of myocardial infarction (1.7% vs 0.6%, p = 0.39), ventricular arrhythmia (0.9 % vs 0 %, p = 0.51) or stroke (0.9 % vs 0 %, p = 0.51). Kaplan-Meier assessment demonstrated no significant difference at 30 days but a reduction in 1 year mortality in the AVI group compared to the control group (Figure). Conclusions: Pre-operative AVI for severe aortic stenosis results in reduced heart failure and improved 1 year survival. However, other MACE and 30 day mortality appears unchanged. Pre-operative AVI should be considered in symptomatic patients, where the benefit of AVI is demonstrated to be greatest.

Introduction: While left ventricular assist devise (LVAD) support improves the prognosis of end-stage heart failure (HF) patients, the long-term outcomes remains suboptimal. Improved outcomes may occur with organ protection including reverse myocardial remodeling. Cenderitide (CDNP) is a Mayo engineered designer natriuretic peptide (NP) that co-activates particulate guanylyl cyclase A and B receptors and functions via the second messenger cGMP. We reported a NP deficient state in the heart with LVAD support which lays the rationale for NP replacement therapy with Cenderitide which has cardiorenal protective actions in experimental HF. We sought to determine if Cenderitide co-therapy would be safe and activate cGMP in patients with LVAD support. Hypothesis: Acute single dose of subcutaneous (SQ) Cenderitide will be safe and activate cGMP in patients with LVAD support. Methods: Stable HF patients with LVAD support (Heartmate II, all males) were enrolled in a cross-over design comparing SQ placebo and low (Low-C, 5 ug/kg, n=8) or high dose (High-C, 10 ug/kg, n=8) Cenderitide (Capricor Therapeutics) administration. Vital signs, plasma C(D)NP, cGMP, Aldosterone (Aldo), and NT-proBNP levels were measured before or after drug administration over 24 hours. All parameters in Cenderitide group were adjusted by those in placebo. Results: Both Low-C and High-C group demonstrated significantly increased plasma C(D)NP and cGMP levels for 2 hours after drug injection and those in High-C group showed more prolonged and higher increases than Low-C group. Mean arterial pressure (MAP) in Low-C did not show any changes but in High-C group tended to be lower for 18 hours but this was not significant. Plasma Aldo and NT-proBNP levels in Low-C group did not show any change but those in High-C group tended to be lower after drug injection s similar to the change in MAP. No adverse events were observed in the study. Conclusions: SQ Cenderitide was successfully absorbed, activated cGMP and well tolerated in patients with LVAD support. High dose Cenderitide may have cardiac unloading effects suggested by decreases in NT-proBNP. Further clinical trial for efficacy is warranted to clarify cardiorenal protective effects of Cenderitide in the LVAD population to improve outcomes.

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer‐assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. Methods and Results Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle–brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all‐cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow‐up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78–3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49–2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43–22.39]) at 5 years. Conclusions An artificial intelligence–enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.