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PurposeHCM is the most common inherited cardiomyopathy. Historically, there has been poor correlation between genotype and phenotype. However, CMR has the potential to more accurately assess disease phenotype. We characterized phenotype with CMR in a cohort of patients with confirmed HCM and high prevalence of genetic testing.MethodsPatients with a diagnosis of HCM, who had undergone contrast-enhanced CMR were identified. Left ventricular mass index (LVMI) and volumes were measured from steady-state free precession sequences. Late gadolinium enhancement (LGE) was quantified using the full width, half maximum method. All patients were prospectively followed for the development of septal reduction therapy, arrhythmia or death.ResultsWe included 273 patients, mean age 51.2 ± 15.5, 62.9% male. Of those patients 202 (74.0%) underwent genetic testing with 90 pathogenic, likely pathogenic, or rare variants and 13 variants of uncertain significance identified. Median follow-up was 1138 days. Mean LVMI was 82.7 ± 30.6 and 145 patients had late gadolinium enhancement (LGE). Patients with beta-myosin heavy chain (MYH7) mutations had higher LV ejection fraction (68.8 vs 59.1, pConclusionsWe found that patients with MYBPC3 mutations were more likely to have impaired ventricular function and may be more prone to arrhythmic events. Larger studies using CMR phenotyping may be capable of identifying additional characteristics associated with less frequent genetic causes of HCM.

Abstract Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Abstract Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in

Heart transplantation is a life-saving procedure for children affected by end-stage heart failure. However, despite recent improvements in long-term outcomes, 1-year post-transplantation mortality has remained relatively high. Accurate prediction of post-transplantation mortality is crucial to evaluating risks related to recipient-donor matches. Machine learning techniques can potentially improve the current allocation system through the integration of a larger set of features. In this work, we improve 1-year mortality prediction after pediatric heart transplantation using a new self-training approach, based on generating artificial recipient-donor pairs as synthetic unlabeled observations. We tested and compared our approach to several baselines using the cohort of pediatric patients in the UNOS database. Our study suggests that augmenting the dataset with proper synthetic observations can improve the prediction of 1-year mortality after pediatric heart transplantation. Our findings have implications for the future of heart transplantation in children, offering a potential path to refine recipient-donor matching and improve survival rates. This study contributes to the growing field of advanced machine learning techniques applied to medical decision-making, specifically in the context of organ transplantation.

Abstract Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction – in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Background The Diamond‐Forrester model was used extensively to predict obstructive coronary artery disease (CAD) but overestimates probability in current populations. Coronary artery calcium (CAC) is a useful marker of CAD, which is not routinely integrated with other features. We derived simple likelihood tables, integrating CAC with age, sex, and cardiac chest pain to predict obstructive CAD. Methods and Results The training population included patients from 3 multinational sites (n=2055), with 2 sites for external testing (n=3321). We determined associations between age, sex, cardiac chest pain, and CAC with the presence of obstructive CAD, defined as any stenosis ≥50% on coronary computed tomography angiography. Prediction performance was assessed using area under the receiver‐operating characteristic curves (AUCs) and compared with the CAD Consortium models with and without CAC, which require detailed calculations, and the updated Diamond‐Forrester model. In external testing, the proposed likelihood tables had higher AUC (0.875 [95% CI, 0.862–0.889]) than the CAD Consortium clinical+CAC score (AUC, 0.868 [95% CI, 0.855–0.881]; P=0.030) and the updated Diamond‐Forrester model (AUC, 0.679 [95% CI, 0.658–0.699]; P

Abstract Background Transthyretin amyloidosis cardiomyopathy (ATTR-CM) patients are often older and may be at risk for obstructive epicardial coronary artery disease (oeCAD). While ATTR-CM may cause small vessel coronary disease, the prevalence and clinical significance of oeCAD is not well described. Methods and results The prevalence and incidence of oeCAD and its association with all-cause mortality and hospitalization among 133 ATTR-CM patients with ≥ 1-year follow-up was evaluated. The mean age was 78 ± 9 years, 119 (89%) were male, 116 (87%) had wild-type and 17 (13%) had hereditary subtypes. Seventy-two (54%) patients underwent oeCAD investigations, with 30 (42%) receiving a positive diagnosis. Among patients with a positive oeCAD diagnosis, 23 (77%) were diagnosed prior to ATTR-CM diagnosis, 6 (20%) at the time of ATTR-CM diagnosis, and 1 (3%) after ATTR-CM diagnosis. Baseline characteristics between patients with and without oeCAD were similar. Among patients with oeCAD, only 2 (7%) required additional investigations, intervention or hospitalization after ATTR-CM diagnosis. After a median follow-up of 27 months there were 37 (28%) deaths in the study population, including 5 patients with oeCAD (17%). Fifty-six (42%) patients in the study population required hospitalization, including 10 patients with oeCAD (33%). There was no significant difference in the rates of death or hospitalization among ATTR-CM patients with and without oeCAD, and oeCAD was not significantly associated with either outcome by univariable regression analysis. Conclusions While oeCAD is prevalent in ATTR-CM patients, this diagnosis is frequently known at time of ATTR-CM diagnosis and characteristics are similar to patients without oeCAD.

Abstract Background We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver disease [NAFLD] and epicardial adipose tissue [EAT] measures) with long-term risk of major adverse cardiovascular events (MACE) in asymptomatic individuals. Methods This was a post-hoc analysis of the prospective EISNER (Early-Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) study of participants who underwent baseline coronary artery calcium (CAC) scoring CT and 14-year follow-up for MACE (myocardial infarction, late revascularization, or cardiac death). EAT volume (cm3) and attenuation (Hounsfield units [HU]) were quantified from CT using fully automated deep learning software (

Background Administrative data have limited sensitivity for case finding of atrial fibrillation/atrial flutter (AF/AFL). Linkage with clinical repositories of interpreted ECGs may enhance diagnostic yield of AF/AFL. Methods and Results We retrieved 369 ECGs from the institutional Marquette Universal System for Electrocardiography (MUSE) repository as validation samples, with rhythm coded as AF (n=49), AFL (n=50), or other competing rhythm diagnoses (n=270). With blinded, duplicate review of ECGs as the reference comparison, we compared multiple MUSE coding definitions for identifying AF/AFL. We tested the agreement between MUSE diagnosis and reference comparison, and calculated the sensitivity and specificity. Using a data set linking clinical registries, administrative data, and the MUSE repository (n=11 662), we assessed the incremental diagnostic yield of AF/AFL by incorporating ECG data to administrative data‐based algorithms. The agreement between MUSE diagnosis and reference comparison depended on the coding definitions applied, with the Cohen κ ranging from 0.57 to 0.75. Sensitivity ranged from 60.6% to 79.1%, and specificity ranged from 93.2% to 98.0%. A coding definition with AF/AFL appearing in the first 3 ECG statements had the highest sensitivity (79.1%), with little loss of specificity (94.5%). Compared with the algorithms with only administrative data, incorporating ECG data increased the diagnostic yield of preexisting AF/AFL by 14.5% and incident AF/AFL by 7.5% to 16.1%. Conclusions Routine ECG interpretation using MUSE coding is highly specific and moderately sensitive for AF/AFL detection. Inclusion of MUSE ECG data in AF/AFL case identification algorithms can identify cases missed using administrative data‐based algorithms alone.

Background The strong linear relation between mean (MPAP) and systolic (SPAP) pulmonary arterial pressure (eg, SPAP=1.62×MPAP) has been mainly reported in precapillary pulmonary hypertension. This study sought to quantify the influence of pulmonary arterial wedge pressure (PAWP), heart rate, and age on the MPAP‐SPAP relation. Methods and Results An allometric equation relating invasive MPAP and SPAP was developed in 1135 patients with pulmonary arterial hypertension, advanced lung disease, chronic thromboembolic pulmonary hypertension, or left heart failure. The equation was validated in 60 885 patients from the United Network for Organ Sharing (UNOS) database referred for heart and/or lung transplant. The MPAP/SPAP longitudinal stability was assessed in pulmonary arterial hypertension with repeated right heart catheterization. The equation obtained was SPAP=1.39×MPAP×PAWP−0.07×(60/heart rate)0.12×age0.08 (P

Background: Angiotensin-converting enzyme inhibitors/angiotensin receptor blocker (ACE-I/ARB) improve outcomes in patients with heart failure and reduced left-ventricular (LV) systolic function. However, these medications can cause a rise in serum creatinine and their benefits in patients with HF accompanied by kidney disease are less certain. Objective: To characterize associations between estimated glomerular filtration rate (eGFR), patterns of ACE-Is and ARBs use, and 1-year survival following hospitalization for heart failure (HF). Design: We formed a retrospective cohort study of patients admitted with HF and followed HF medication prescriptions using the pharmaceutical information network, stratified by discharge eGFR. Setting: Cardiology services in 3 centers in Southern Alberta, Canada. Patients: The study cohort included patients admitted to hospital with a clinical diagnosis of HF. Measurements: eGFR was determined from inpatient laboratory data prior to discharge. Outpatient prescription data prior to and following the index hospitalization was obtained using the Pharmaceutical Information Network of Alberta and survival was determined from provincial vital statistics. Methods: Characteristics of the HF cohort were obtained from the Admissions Module of the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. Multivariable Cox proportional hazards models were used to evaluate the association between time-varying ACE-I/ARB use, and mortality, and to test whether eGFR modified this association. Results: Totally, 1404 patients were included. Within the first 3 months following discharge, ACE-I/ARBs were used in 71%, 67%, 62%, and 52% for those with eGFR > 90, 45-89, 30-44, and < 30 mL/min/1.73 m 2 , respectively, with differences in use persisting after 1 year of follow-up. Patients with eGFR < 45 mL/min/1.73 m 2 had significantly lower rates of ACE-I/ARB use following hospitalization. In adjusted models, ACE-I/ARB use following discharge was associated with 25% lower risk of mortality (Hazard Ratio [HR]: 0.75, 95% confidence interval [CI]: 0.61-0.92; P < 0.01), without evidence that this association differed by eGFR ( P = 0.75). Limitations: LV function measurements were not available for the cohort. Due to the observation design of the study, treatment-selection bias may be present. Conclusion: Patients with HF and reduced eGFR at time of hospital discharge were less likely to receive ACE-I/ARB despite these medications being associated with lower mortality independent of eGFR. These findings demonstrate the need for further research on strategies for safe use of ACE-I and ARB in patients with HF and kidney disease.