Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have significant intervendor variability. Creating an automated, open-source, vendor-agnostic method to retrospectively measure global longitudinal strain (GLS) from standard echocardiography B-mode images would greatly improve post hoc research applications and may streamline patient analyses. OBJECTIVES: This study was seeking to develop an automated deep learning strain (DLS) analysis pipeline and validate its performance across multiple applications and populations. METHODS: Interobserver/-vendor variation of traditional GLS, and simulated effects of variation in contour on speckle-tracking measurements were assessed. The DLS pipeline was designed to take semantic segmentation results from EchoNet-Dynamic and derive longitudinal strain by calculating change in the length of the left ventricular endocardial contour. DLS was evaluated for agreement with GLS on a large external dataset and applied across a range of conditions that result in cardiac hypertrophy. RESULTS: In patients scanned by 2 sonographers using 2 vendors, GLS had an intraclass correlation of 0.29 (95% CI: -0.01 to 0.53, P = 0.03) between vendor measurements and 0.63 (95% CI: 0.48-0.74, P < 0.001) between sonographers. With minor changes in initial input contour, step-wise pixel shifts resulted in a mean absolute error of 3.48% and proportional strain difference of 13.52% by a 6-pixel shift. In external validation, DLS maintained moderate agreement with 2-dimensional GLS (intraclass correlation coefficient [ICC]: 0.56, P = 0.002) with a bias of -3.31% (limits of agreement: -11.65% to 5.02%). The DLS method showed differences (P < 0.0001) between populations with cardiac hypertrophy and had moderate agreement in a patient population of advanced cardiac amyloidosis: ICC was 0.64 (95% CI: 0.53-0.72), P < 0.001, with a bias of 0.57%, limits of agreement of -4.87% to 6.01% vs 2-dimensional GLS. CONCLUSIONS: The open-source DLS provides lower variation than human measurements and similar quantitative results. The method is rapid, consistent, vendor-agnostic, publicly released, and applicable across a wide range of imaging qualities.

Recurrent Myocarditis Treated with Intravenous Immune Globulin and Steroids.

BACKGROUND Myocarditis is an inflammatory process that can present as acute or chronic with either focal or diffuse involvement of the myocardium. Its incidence is approximately 1.5 million cases per year worldwide. In the United States, viral infection is the most common cause of myocarditis. Most of the reported cases are singular and self-limiting in nature. We present the case of severe recurrent myocarditis in a young adult who was transferred to the Intensive Care Unit. CASE REPORT An 18-year-old man presented with chest pressure and troponin I 33 ng/mL. He had presented to another hospital with similar symptoms 3 months prior and was diagnosed with myocarditis that had resolved with colchicine. As part of his workup during this admission, coronary angiogram was normal and biopsy obtained without evidence of an inflammatory process; however, cardiac magnetic resonance imaging (MRI) was consistent with myocarditis and Coxsackie B titers indicated prior infection, leading to a diagnosis of clinically suspected recurrent viral myocarditis. He was treated with intravenous immunoglobulin (IV Ig) and a steroid taper, with rapid improvement in symptoms over the ensuing weeks without evidence of further recurrence or sequelae. CONCLUSIONS We present a case of recurrent Coxsackie B myocarditis based on presentation and imaging. Myocarditis is an important diagnosis to consider when a young, healthy individual presents with chest pain mimicking acute coronary syndrome, especially during the COVID pandemic. If there is evidence of myocarditis on MRI or endomyocardial biopsy, immunosuppressive therapy should be considered in patients with recurrent and severe presentations.

Cardiac microstructural alterations in immune-inflammatory myocardial disease: a retrospective case-control study.

BACKGROUND: Immune-inflammatory myocardial disease contributes to multiple chronic cardiac processes, but access to non-invasive screening is limited. We have previously developed a method of echocardiographic texture analysis, called the high-spectrum signal intensity coefficient (HS-SIC) which assesses myocardial microstructure and previously associated with myocardial fibrosis. We aimed to determine whether this echocardiographic texture analysis of cardiac microstructure can identify inflammatory cardiac disease in the clinical setting. METHODS: We conducted a retrospective case-control study of 318 patients with distinct clinical myocardial pathologies and 20 healthy controls. Populations included myocarditis, atypical chest pain/palpitations, STEMI, severe aortic stenosis, acute COVID infection, amyloidosis, and cardiac transplantation with acute rejection, without current rejection but with prior rejection, and with no history of rejection. We assessed the HS-SIC's ability to differentiate between a broader diversity of clinical groups and healthy controls. We used Kruskal-Wallis tests to compare HS-SIC values measured in each of the clinical populations with those in the healthy control group and compared HS-SIC values between the subgroups of cardiac transplantation rejection status. RESULTS: For the total sample of N = 338, the mean age was 49.6 ± 20.9 years and 50% were women. The mean ± standard error of the mean of HS-SIC were: 0.668 ± 0.074 for controls, 0.552 ± 0.049 for atypical chest pain/palpitations, 0.425 ± 0.058 for myocarditis, 0.881 ± 0.129 for STEMI, 1.116 ± 0.196 for severe aortic stenosis, 0.904 ± 0.116 for acute COVID, and 0.698 ± 0.103 for amyloidosis. Among cardiac transplant recipients, HS-SIC values were 0.478 ± 0.999 for active rejection, 0.594 ± 0.091 for prior rejection, and 1.191 ± 0.442 for never rejection. We observed significant differences in HS-SIC between controls and myocarditis (P = 0.0014), active rejection (P = 0.0076), and atypical chest pain or palpitations (P = 0.0014); as well as between transplant patients with active rejection and those without current or prior rejection (P = 0.031). CONCLUSIONS: An echocardiographic method can be used to characterize tissue signatures of microstructural changes across a spectrum of cardiac disease including immune-inflammatory conditions.

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning.

IMPORTANCE: Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis. OBJECTIVE: To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTS: This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES: The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS: The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCE: In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.

Assessment of atherosclerosis in chronic granulomatous disease.

BACKGROUND: Patients with chronic granulomatous disease (CGD) experience immunodeficiency because of defects in the phagocyte NADPH oxidase and the concomitant reduction in reactive oxygen intermediates. This may result in a reduction in atherosclerotic injury. METHODS AND RESULTS: We prospectively assessed the prevalence of cardiovascular risk factors, biomarkers of inflammation and neutrophil activation, and the presence of magnetic resonance imaging and computed tomography quantified subclinical atherosclerosis in the carotid and coronary arteries of 41 patients with CGD and 25 healthy controls in the same age range. Univariable and multivariable associations among risk factors, inflammatory markers, and atherosclerosis burden were assessed. Patients with CGD had significant elevations in traditional risk factors and inflammatory markers compared with control subjects, including hypertension, high-sensitivity C-reactive protein, oxidized low-density lipoprotein, and low high-density lipoprotein. Despite this, patients with CGD had a 22% lower internal carotid artery wall volume compared with control subjects (361.3±76.4 mm(3) versus 463.5±104.7 mm(3); P<0.001). This difference was comparable in p47(phox)- and gp91(phox)-deficient subtypes of CGD and independent of risk factors in multivariate regression analysis. In contrast, the prevalence of coronary arterial calcification was similar between patients with CGD and control subjects (14.6%, CGD; 6.3%, controls; P=0.39). CONCLUSIONS: The observation by magnetic resonance imaging and computerized tomography of reduced carotid but not coronary artery atherosclerosis in patients with CGD despite the high prevalence of traditional risk factors raises questions about the role of NADPH oxidase in the pathogenesis of clinically significant atherosclerosis. Additional high-resolution studies in multiple vascular beds are required to address the therapeutic potential of NADPH oxidase inhibition in cardiovascular diseases. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT01063309.

Delayed pneumothorax after bronchoscopy in a lung transplant patient.

Lung transplant patients commonly undergo transbronchial biopsy to evaluate for rejection. Post-biopsy radiographs are used to exclude pneumothorax, one of the most common major complications. We report a lung transplant patient who developed a pneumothorax 5 months after transbronchial biopsy, with multiple intervening chest computed tomograms documenting that the pneumothorax developed from the biopsy site. This case illustrates that in transplant patients transbronchial biopsy can evolve to pneumothorax several months later, despite normal post-biopsy radiographs.