Appleton, Christopher

Aims Increased LV wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4 chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). Conclusion The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Background— β-Blockers improve survival and reduce hospitalization in chronic heart failure (CHF) by biologically improving left ventricular ejection fraction (LVEF). However, a good predictor of improvement with this therapy has not been identified. This substudy of BEST examined whether myocardial contractile reserve, as determined by dobutamine stress echocardiography, predicts improvement in LVEF. Methods and Results— Seventy-nine patients with class III/IV CHF underwent dobutamine stress echocardiography before treatment with bucindolol (n=41) or placebo (n=38). Regional wall motion score index (WMSI) was calculated as the sum of the scores in each segment divided by the total number of segments visualized. WMSI was compared with change in LVEF after 3 months of therapy as determined by gated radionuclide scan. Change in WMSI correlated inversely with change in LVEF after 3 months of bucindolol ( r =−0.72, P <0.0001) and was the most significant multivariate predictor of change in LVEF ( P =0.0002). Patients with contractile reserve had demographics similar to those of patients without contractile reserve, including RVEF, LVEF, systolic blood pressure, and CHF duration. However, patients without contractile reserve had higher baseline plasma norepinephrine levels (687±333 versus 420±246 pg/mL, P <0.05) and greater decrease in plasma norepinephrine in response to bucindolol (−249±171 versus −35±277 pg/mL, P <0.05). Conclusions— This study suggests a direct relationship between contractile reserve and improvement in LVEF with β-blocker therapy in patients with advanced CHF. Patients without contractile reserve have higher resting adrenergic drive, as reflected by plasma norepinephrine, and may experience greater sympatholytic effects from bucindolol.