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Published on: January 14, 2014
Utility of an Automated Artificial Intelligence Echocardiography Software in Risk Stratification of Hospitalized
Tom Kai Ming Wang1, Paul C Cremer1, Nicholas Chan1
1Section of Cardiovascular Imaging, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
Insights
Artificial intelligence (AI) software EchoGo shows promise in assessing cardiac function in COVID-19 patients. EchoGo
Area of Science:
- Cardiology
- Artificial Intelligence
- Infectious Diseases
Background:
- Cardiovascular disease is linked to poor COVID-19 outcomes.
- AI in cardiac imaging is rapidly advancing.
- Assessing cardiac function in COVID-19 patients is crucial for prognosis.
Purpose of the Study:
- To evaluate the utility of AI-driven software EchoGo for analyzing echocardiograms in hospitalized COVID-19 patients.
- To correlate EchoGo outputs with standard echocardiography measurements.
- To assess the association of EchoGo-derived cardiac parameters with clinical outcomes.
Main Methods:
- Fifty consecutive COVID-19 positive inpatients underwent echocardiography.
- Echocardiograms were analyzed using EchoGo AI software.
- EchoGo outputs were correlated with standard measurements and adjusted for APACHE-4 scores to assess clinical outcome associations.
Main Results:
- EchoGo demonstrated strong correlations with standard echocardiography measurements for LVEDV, LVESV, LVEF, and GLS.
- After adjusting for APACHE-4 score, EchoGo's Left Ventricular Ejection Fraction (LVEF) and Global Longitudinal Strain (GLS) were associated with adverse clinical outcomes.
- Specifically, a 1% increase in EchoGo LVEF was associated with a decreased risk (OR 0.92), while a 1% increase in EchoGo GLS was associated with an increased risk (OR 1.22) of the primary endpoint.
Conclusions:
- Automated AI software like EchoGo can serve as a valuable clinical tool for cardiac assessment.
- EchoGo's LVEF and GLS measurements are associated with adverse outcomes in hospitalized COVID-19 patients.
- EchoGo has the potential to aid in the risk stratification of COVID-19 patients with cardiac involvement.
Abstract:
Cardiovascular risk factors, biomarkers, and diseases are associated with poor prognosis in COVID-19 infections. Significant progress in artificial intelligence (AI) applied to cardiac imaging has recently been made. We assessed the utility of AI analytic software EchoGo in COVID-19 inpatients. Fifty consecutive COVID-19+ inpatients (age 66 ± 13 years, 22 women) who had echocardiography in 4/17/2020−8/5/2020 were analyzed with EchoGo software, with output correlated against standard echocardiography measurements. After adjustment for the APACHE-4 score, associations with clinical outcomes were assessed. Mean EchoGo outputs were left ventricular end-diastolic volume (LVEDV) 121 ± 42 mL, end-systolic volume (LVESV) 53 ± 30 mL, ejection fraction (LVEF) 58 ± 11%, and global longitudinal strain (GLS) −16.1 ± 5.1%. Pearson correlation coefficients (p-value) with standard measurements were 0.810 (<0.001), 0.873 (<0.001), 0.528 (<0.001), and 0.690 (<0.001). The primary endpoint occurred in 26 (52%) patients. Adjusting for APACHE-4 score, EchoGo LVEF and LVGLS were associated with the primary endpoint, odds ratios (95% confidence intervals) of 0.92 (0.85−0.99) and 1.22 (1.03−1.45) per 1% increase, respectively. Automated AI software is a new clinical tool that may assist with patient care. EchoGo LVEF and LVGLS were associated with adverse outcomes in hospitalized COVID-19 patients and can play a role in their risk stratification.
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