Integrating AI-ECG and Point-of-Care Cardiac Ultrasound for Screening Structural Heart Disease: A Proof-of-Concept Study
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Background
Early structural heart disease (SHD) detection is crucial for improving prognostic outcomes, but widely accessible screening methods are lacking. The advent of artificial intelligence-enabled electrocardiograms (AI-ECG) and point-of-care cardiac-ultrasonography (POCCUS) offers promising new approaches for patient screening. We explored the feasibility and potential of integrating these innovative technologies into a practical SHD screening framework.
Methods
Outpatients who underwent ECG at the Mayo Clinic electrocardiogram-laboratory between November 2023 and February 2024 were randomly offered also to receive POCCUS, performed by a novice operator and reviewed by an expert echocardiologist. Impressions from AI-ECG and POCCUS were integrated to assess for SHD, including low left ventricular systolic function (ejection-fraction<50%), aortic stenosis, and increased left ventricular wall thickness indicative of cardiac amyloidosis or hypertrophic cardiomyopathy. Operators were blinded to patients’ comorbidities and formal echocardiogram results.
Results
Of 486 patients (median-age 64 years;49% women), 286 had available formal echocardiography, with 17.5% having SHD. AI-ECG had a 32% positive predictive value (PPV) and a 94% negative predictive value (NPV) to detect any SHD. Adding POCCUS increased the overall PPV to 64% with an NPV of 93%, with an increase in diagnostic accuracy from 67% to 88%. Notably, 89.5% (17/19) of the “false positives” by AI-ECG+POCCUS had less-than-moderate-SHD. Applying the AI-ECG+POCCUS screening workflow on the entire cohort resulted in a number-needed-to-screen of eight to identify one patient requiring formal echocardiography (Central Figure).
Conclusions
The integration of AI-ECG and POCCUS holds promise as a potentially effective screening method for SHD, facilitating improved patient selection for formal echocardiography.
