ABSTRACT
Background: As automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms. Methods: In this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae. Results: Feasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival. Conclusion: Automated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.
ABSTRACT
Coronavirus disease 2019 (COVID-19) has high infectivity and causes extensive morbidity and mortality. Cardiovascular disease is a risk factor for adverse outcomes in COVID-19, but baseline left ventricular ejection fraction (LVEF) in particular has not been evaluated thoroughly in this context. We analyzed patients in our state's largest health system who were diagnosed with COVID-19 between March 20 and May 15, 2020. Inclusion required an available echocardiogram within 1 year prior to diagnosis. The primary outcome was all-cause mortality. LVEF was analyzed both as a continuous variable and using a cutoff of 40%. Among 396 patients (67 ± 16 years, 191 [48%] male, 235 [59%] Black, 59 [15%] LVEF ≤40%), 289 (73%) required hospital admission, and 116 (29%) died during 85 ± 63 days of follow-up. Echocardiograms, performed a median of 57 (IQR 11-122) days prior to COVID-19 diagnosis, showed a similar distribution of LVEF between survivors and decedents (P = 0.84). Receiver operator characteristic analysis revealed no predictive ability of LVEF for mortality, and there was no difference in survival among those with LVEF ≤40% versus >40% (P = 0.49). Multivariable analysis did not change these relationships. Similarly, there was no difference in LVEF based on whether the patient required hospital admission (56 ± 13 vs 55 ± 13, P = 0.38), and patients with a depressed LVEF did not require admission more frequently than their preserved-LVEF peers (P = 0.87). A premorbid history of dyspnea consistent with symptomatic heart failure was not associated with mortality (P = 0.74). Among patients diagnosed with COVID-19, pre-COVID-19 LVEF was not a risk factor for death or hospitalization.