Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy.

BACKGROUND: Myocardial scar burden quantified using late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR), has important prognostic value in hypertrophic cardiomyopathy (HCM). However, nearly 50% of HCM patients have no scar but undergo repeated gadolinium-based CMR over their life span. We sought to develop an artificial intelligence (AI)-based screening model using radiomics and deep learning (DL) features extracted from balanced steady state free precession (bSSFP) cine sequences to identify HCM patients without scar. METHODS: We evaluated three AI-based screening models using bSSFP cine image features extracted by radiomics, DL, or combined DL-Radiomics. Images for 759 HCM patients (50 ± 16 years, 66% men) in a multi-center/vendor study were used to develop and test model performance. An external dataset of 100 HCM patients (53 ± 14 years, 70% men) was used to assess model generalizability. Model performance was evaluated using area-under-receiver-operating curve (AUC). RESULTS: The DL-Radiomics model demonstrated higher AUC compared to DL and Radiomics in the internal (0.83 vs 0.77, p = 0.006 and 0.78, p = 0.05; n = 159) and external (0.74 vs 0.64, p = 0.006 and 0.71, p = 0.27; n = 100) datasets. The DL-Radiomics model correctly identified 43% and 28% of patients without scar in the internal and external datasets compared to 42% and 16% by Radiomics model and 42% and 23% by DL model, respectively. CONCLUSIONS: A DL-Radiomics AI model using bSSFP cine images outperforms DL or Radiomics models alone as a scar screening tool prior to gadolinium administration. Despite its potential, the clinical utility of the model remains limited and further investigation is needed to improve the accuracy and generalizability.

Genetics of Anthracycline-Associated Cardiotoxicity.

Anthracyclines are a major component of chemotherapies used in many pediatric and adult malignancies. Anthracycline-associated cardiotoxicity (ACT) is a dose-dependent adverse effect that has substantial impact on morbidity and mortality. Therefore, the identification of genetic variants associated with increased risk of ACT has the potential for significant clinical impact to improve patient care. The goal of this review is to summarize the current evidence supporting genetic variants associated with ACT, identify gaps and limitations in current knowledge, and propose future directions for incorporating genetics into clinical practice for patients treated with anthracyclines. We will discuss mechanisms of ACT that could be illuminated by genetics and discuss clinical applications for the cardiologist/cardio-oncologist.

An Explainable Machine Learning Approach Reveals Prognostic Significance of Right Ventricular Dysfunction in Nonischemic Cardiomyopathy.

OBJECTIVES: The authors implemented an explainable machine learning (ML) model to gain insight into the association between cardiac magnetic resonance markers and adverse outcomes of cardiovascular hospitalization and all-cause death (composite endpoint) in patients with nonischemic dilated cardiomyopathy (NICM). BACKGROUND: Risk stratification of patients with NICM remains challenging. An explainable ML model has the potential to provide insight into the contributions of different risk markers in the prediction model. METHODS: An explainable ML model based on extreme gradient boosting (XGBoost) machines was developed using cardiac magnetic resonance and clinical parameters. The study cohorts consist of patients with NICM from 2 academic medical centers: Beth Israel Deaconess Medical Center (BIDMC) and Brigham and Women's Hospital (BWH), with 328 and 214 patients, respectively. XGBoost was trained on 70% of patients from the BIDMC cohort and evaluated based on the other 30% as internal validation. The model was externally validated using the BWH cohort. To investigate the contribution of different features in our risk prediction model, we used Shapley additive explanations (SHAP) analysis. RESULTS: During a mean follow-up duration of 40 months, 34 patients from BIDMC and 33 patients from BWH experienced the composite endpoint. The area under the curve for predicting the composite endpoint was 0.71 for the internal BIDMC validation and 0.69 for the BWH cohort. SHAP analysis identified parameters associated with right ventricular (RV) dysfunction and remodeling as primary markers of adverse outcomes. High risk thresholds were identified by SHAP analysis and thus provided thresholds for top predictive continuous clinical variables. CONCLUSIONS: An explainable ML-based risk prediction model has the potential to identify patients with NICM at risk for cardiovascular hospitalization and all-cause death. RV ejection fraction, end-systolic and end-diastolic volumes (as indicators of RV dysfunction and remodeling) were determined to be major risk markers.

Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important. PURPOSE: To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF-hospitalization. STUDY TYPE: Retrospective. POPULATION: A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%). FIELD STRENGTH: A 1.5 T, balanced steady-state free precession (bSSFP) sequence. ASSESSMENT: Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI-based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF-hospitalization. STATISTICAL TESTS: ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P-value <0.05 was considered statistically significant. RESULTS: During follow-up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI-based ML model using the XGBoost algorithm provided a significantly superior prediction of HF-hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and -15%, respectively. DATA CONCLUSIONS: Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Diagnostic Accuracy of Dobutamine Stress Echocardiography for Detection of Cardiac Allograft Vasculopathy in Orthotopic Heart Transplant Patients.

OBJECTIVE: Cardiac allograft vasculopathy is one of the leading causes of late graft failure and subsequent death in orthotopic heart transplant. Although invasive coronary angiography is the gold standard modality for detection of cardiac allograft vasculopathy, dobutamine stress echocardiography has been recently frequently used as an alternative. Our aim was to evaluate the diagnostic performance of dobutamine stress echocardiography for detection of cardiac allograft vasculopathy in transplant patients. METHODS: A retrospective analysis was conducted using a total of 150 dobutamine stress echocardiographic exams that were performed on 99 patients in our institution, with paired coronary angiogram and no acute rejection, within a median of 538 [interquartile range 371-816] days. Sensitivity and specificity of dobutamine echocardiography to detect allograft vasculopathy was evaluated. Allograft vasculopathy was defined as Grade 1 or higher based on ISHLT criteria. A positive dobutamine stress echo result was defined by new or worsening wall motion abnormality. RESULTS: Median age of the population at transplant was 34 [interquartile range 22-46] years; 76 (77%) patients were male. Allograft vasculopathy was present in 31 (20.6%) out of 150 coronary angiograms. Only 7 (4.6%) of that number were positive on dobutamine stress echocardiography. Sensitivity and specificity for allograft vasculopathy detection was 3% and 94%, respectively. Out of 7 false positive dobutamine stress echocardiograms, two were in patients with myocardial bridging. Two patients with mild acute rejection had both negative dobutamine stress echo. CONCLUSIONS: Overall, positivity of dobutamine stress echocardiography in patients after heart transplant is low. It has high specificity, but very low sensitivity for detection of cardiac allograft vasculopathy. Dobutamine stress echocardiography should only be cautiously used as an alternative to coronary angiography.

Association Between Left Ventricular Mechanical Deformation and Myocardial Fibrosis in Nonischemic Cardiomyopathy.

Background In patients with nonischemic cardiomyopathy, nonischemic fibrosis detected by late gadolinium enhancement (LGE) cardiovascular magnetic resonance is related to adverse cardiovascular outcomes. However, its relationship with left ventricular (LV) mechanical deformation parameters remains unclear. We sought to investigate the association between LV mechanics and the presence, location, and extent of fibrosis in patients with nonischemic cardiomyopathy. Methods and Results We retrospectively identified 239 patients with nonischemic cardiomyopathy (67% male; 55±14 years) referred for a clinical cardiovascular magnetic resonance. LGE was present in 109 patients (46%), most commonly (n=52; 22%) in the septum. LV deformation parameters did not differentiate between LGE-positive and LGE-negative groups. Global longitudinal, radial, and circumferential strains, twist and torsion showed no association with extent of fibrosis. Patients with septal fibrosis had a more depressed LV ejection fraction (30±12% versus 35±14%; P=0.032) and more impaired global circumferential strain (-7.9±3.5% versus -9.7±4.4%; P=0.045) and global radial strain (10.7±5.2% versus 13.3±7.7%; P=0.023) than patients without septal LGE. Global longitudinal strain was similar in both groups. While patients with septal-only LGE (n=28) and free wall-only LGE (n=32) had similar fibrosis burden, the septal-only LGE group had more impaired LV ejection fraction and global circumferential, longitudinal, and radial strains (all P<0.05). Conclusions There is no association between LV mechanical deformation parameters and presence or extent of fibrosis in patients with nonischemic cardiomyopathy. Septal LGE was associated with poor global LV function, more impaired global circumferential and radial strains, and more impaired global strain rates.

First study of pattern of anaphylaxis in a large tertiary care hospital in Saudi Arabia.

BACKGROUND: Anaphylaxis is a serious allergic reaction that may cause death. The signs and symptoms of anaphylaxis have not been examined in the Saudi population before. OBJECTIVE: The present study examined the signs, symptoms, triggers, and demographic patterns of patients treated for anaphylaxis at a large tertiary care hospital in Riyadh, Saudi Arabia. METHODS: All the patients who were prescribed new prescriptions of adrenaline auto-injectors (AAs) between February 1, 2010 and December 31, 2011 were included in this study. Information was collected using a standardized form. RESULTS: There were 238 patients who were analyzed. The median age at the time of first AA prescription was 15.5 years. Female to male ratio was 52:48 and 54% of the subjects were more than 18 years of age. There were some differences in the presenting signs and symptoms observed in our study compared with similar studies from around the world. Urticaria and angioedema were the most common at about 70% across all ages, followed by shortness of breath at 28%. Some triggers were found to be more common in our region. Food was the commonest trigger for anaphylaxis including tree nuts, egg, and sesame. Drug allergy was also a common trigger, with penicillins and nonsteroidal anti-inflammatory drugs being the commonest. Regarding insect allergy, samsam ant was the commonest trigger in our study. CONCLUSION: To our knowledge, this is the first study on anaphylaxis in Saudi Arabia. Some of the manifestations of anaphylaxis are significantly different in our population study compared to previously published data from other parts of the world. While managing anaphylaxis, we should be mindful of these differences. This improved understanding should help reduce the morbidity and mortality associated with anaphylaxis in our region.