Addetia, Karima

Abstract Background and Aims Accurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for echocardiography-based CA detection was assessed. Methods Utilizing a multisite, multiethnic dataset (n = 2612, 52% CA), a convolutional neural network was trained to differentiate CA from phenotypic controls using transthoracic apical four-chamber video clips. External validation was conducted globally across 18 sites including 597 CA cases and 2122 controls. Classification accuracy was assessed on the entire external validation dataset, and subgroup analyses were performed both on technetium pyrophosphate scintigraphy referrals, and individuals matched for age, sex, and wall thickness. Model accuracy was also compared with the transthyretin CA score and the increased wall thickness score within a subset of older heart failure with preserved ejection fraction patients with increased wall thickness. Results Cardiac amyloidosis patients and controls displayed similar age, sex, race, and comorbidities. After the removal of uncertain AI predictions (13%), model discrimination and classification were excellent for the entire external validation dataset [area under the receiver operating characteristic curve (AUROC) 0.93, sensitivity 85%, specificity 93%], irrespective of CA subtype (sensitivity: light-chain = 84%, wild-type transthyretin = 85%, and hereditary transthyretin = 86%). Performance was maintained in subgroup analysis in patients clinically referred for technetium pyrophosphate scintigraphy imaging (AUROC 0.86, sensitivity 77%, specificity 86%) and matched patients (AUROC 0.92, sensitivity 84%, specificity 91%). The AI model (AUROC 0.93) also outperformed transthyretin CA score (AUROC 0.73) and increased wall thickness (AUROC 0.80) scores. Conclusions This AI screening model—using only an apical four-chamber view—effectively differentiated CA from other causes of increased left ventricular wall thickness.

BackgroundCardiac infiltration is an important cause of death in sarcoidosis. Transthoracic echocardiography (TTE) has limited sensitivity for the detection of cardiac sarcoidosis (CS). Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is used to diagnose CS but has limitations of cost and availability. We sought to determine whether TTE‐derived global longitudinal strain (GLS) may be used to identify individuals with CS, despite preserved left ventricular ejection fraction (LVEF), and whether abnormal GLS is associated with major cardiovascular events (MCE).MethodsWe studied 31 patients with biopsy‐proven extra‐cardiac sarcoidosis, LVEF>50% and LGE on CMR (CS+ group), and 31 patients without LGE (CS− group), matched by age, sex, and severity of lung disease. GLS was measured using vendor‐independent speckle tracking software. Parameters of left and right ventricular systolic and diastolic function were also studied. Receiver‐operating characteristic curves were used to identify GLS cutoff for CS detection, and Kaplan–Meier plots to determine the ability of GLS to predict MCE.ResultsLGE was associated with reduced GLS (−19.6±1.9% in CS− vs −14.7±2.4% in CS+, P<.01) and with reduced E/A ratio (1.1±0.3 vs 0.9±0.3, respectively, P =.01). No differences were noted in other TTE parameters. GLS magnitude inversely correlated with LGE burden (r=−.59). GLS cutoff of −17% showed sensitivity and specificity 94% for detecting CS. Patients who experienced MCE had worse GLS than those who did not (−13.4±0.9% vs −17.7±0.4%, P=.0003).ConclusionsCS is associated with significantly reduced GLS in the presence of preserved LVEF. GLS measurements may become part of the TTE study performed to screen for CS.

Background— Cardiac sarcoidosis is associated with an increased risk of heart failure and sudden death, but its risk in patients with preserved left ventricular ejection fraction is unknown. Using cardiovascular magnetic resonance in patients with extracardiac sarcoidosis and preserved left ventricular ejection fraction, we sought to (1) determine the prevalence of cardiac sarcoidosis or associated myocardial damage, defined by the presence of late gadolinium enhancement (LGE), (2) quantify their risk of death/ventricular tachycardia (VT), and (3) identify imaging-based covariates that predict who is at greatest risk of death/VT. Methods and Results— Parameters of left and right ventricular function and LGE burden were measured in 205 patients with left ventricular ejection fraction >50% and extracardiac sarcoidosis who underwent cardiovascular magnetic resonance for LGE evaluation. The association between covariates and death/VT in the entire group and within the LGE+ group was determined using Cox proportional hazard models and time-dependent receiver–operator curves analysis. Forty-one of 205 patients (20%) had LGE; 12 of 205 (6%) died or had VT during follow-up; of these, 10 (83%) were in the LGE+ group. In the LGE+ group (1) the rate of death/VT per year was >20× higher than LGE− (4.9 versus 0.2%, P <0.01); (2) death/VT were associated with a greater burden of LGE (14±11 versus 5±5%, P <0.01) and right ventricular dysfunction (right ventricular EF 45±12 versus 53±28%, P =0.04). LGE burden was the best predictor of death/VT (area under the receiver-operating characteristics curve, 0.80); for every 1% increase of LGE burden, the hazard of death/VT increased by 8%. Conclusions— Sarcoidosis patients with LGE are at significant risk for death/VT, even with preserved left ventricular ejection fraction. Increased LGE burden and right ventricular dysfunction can identify LGE+ patients at highest risk of death/VT.