AI estimated LVEF with a mean error of 3.2%, and patients with ≤5% discrepancy had 39% and 81% higher risks of death and HF hospitalization respectively.
Does the discrepancy between AI-estimated LVEF and reported LVEF predict mortality and heart failure hospitalizations in patients undergoing echocardiography?
AI-based estimation of LVEF without image segmentation is accurate, and discrepancies from human-reported LVEF independently predict long-term mortality and heart failure hospitalizations.
Absolute Event Rate: 0% vs 0%
Abstract Transthoracic echocardiography (TTE) is increasingly utilised by point-of-care ultrasound (POCUS) devices. The current study aimed to test whether artificial intelligence (AI) could estimate LVEF using standard TTE without measurements of chamber size or image segmentation while using only parasternal-long-axis and apical-4-chamber views. Methods: Deep learning network training was performed in 76,277 (66%), validated in 17,092 (19%) and tested in 21,920 (15%) randomly selected patients who underwent comprehensive TEE studies between 2007 and 2021. A fourth cohort (N=525) from a different hospital was used for an external validation. A fifth POCUS cohort (N=555) was used for a prospective validation. Accuracy and prognostic significance were evaluated using the hold-out testing cohort. Then, using the same hold-out cohort patients were divided into two groups: (1) patients whose machine-estimated LVEF was ≤ 5% points of the reported value (LVEF discrepancy group LDG), (2) all other patients (non-LDG). All-cause mortality and heart failure (HF) hospitalisations were used as the study endpoints. Results: The final hold-out cohort included 21,920 patients with a median age of 69 (IQR 53-79) of whom 12,244 (56%) were men. LVEF was estimated as a continuous variable with a mean average error (MAE) of 3.2, root mean square error (RMSE) of 4.6 and Pearson correlation coefficient (r) of 0.89. Similar results were obtained in both external validation and prospective POCUS cohorts. Among the patients, 2,761 (12.6%) were categorized into the LDG, while 19,159 (87.4%) were classified as non-LDG. During a median follow-up of 6 years, 6,309 (29%) patients died, and 970 (4.4%) patients were admissioned due to HF. A multivariate Cox regression model successfully demonstrated that compared to patients in the non-LDG, individuals in the LDG had an independent and significant 39% and 81% increased risk of death and HF hospitalisation during follow up (95% CI 1.31-1.48, p0.001 for mortality; 95% CI 1.55-2.12, p0.001 for HF) (Figure 1). Consistent results were obtained in subgroup analyses examining separately patients with preserved and reduced ejection fraction. An exploratory analysis demonstrated that an abnormality in the regional wall motion of the apical and septal segments had the greatest contribution to the discrepancy between the machine estimated LVEF and reported LVEF (Figure 2A). Consistently, saliency maps have demonstrated that the same areas were captured by the machine as the most contributory areas for LVEF estimation (Figure 2B). Conclusions: Artificial intelligence can estimate LVEF in a method that is agnostic to measurements or image segmentation. We show that echo-based AI-LVEF is an independent predictor of heart-failure hospitalisation and overall survival and, with further evaluation, could be used for risk stratification and estimation of LVEF at the point of care.Figure 1.Graphical abstract Figure 2.Forest plot and saliency maps
Faierstein et al. (Sat,) reported a other. AI estimated LVEF with a mean error of 3.2%, and patients with ≤5% discrepancy had 39% and 81% higher risks of death and HF hospitalization respectively.