Key result
The GRAAL echo algorithm detects ATTR-CM with an AUC of 0.90, outperforming standard guideline scores.
Why the study?
Transthyretin amyloid cardiomyopathy is an increasingly recognized cause of heart failure, but detection remains challenging due to echocardiographic similarities with age- and hypertension-related cardiac remodelling.
Does the GRAAL machine-learning algorithm improve diagnostic accuracy for detecting ATTR-CM on echocardiography compared to standard guideline scores in patients with suspected ATTR-CM?
Does the GRAAL machine-learning algorithm improve diagnostic accuracy for detecting ATTR-CM on echocardiography compared to standard guideline scores in patients with suspected ATTR-CM?
The GRAAL machine-learning algorithm, utilizing four standard echocardiographic parameters, significantly improves the diagnostic accuracy for detecting ATTR-CM compared to traditional guideline scores.
May warrant GRAAL use for ATTR-CM echo detection in suspected patients; extends diagnostic accuracy beyond guideline scores.
Aims Transthyretin amyloid cardiomyopathy (ATTR-CM) is an increasingly recognized cause of heart failure, yet detection remains challenging due to its echocardiographic similarities with age- and hypertension-related cardiac remodelling. Methods and results We retrospectively included 260 patients (76.5 ± 12.9 years old, 59.6% male) referred for suspected ATTR-CM. A supervised machine-learning diagnosis algorithm differentiating patients with (n = 111) and without (n = 149) ATTR-CM based on echocardiographic data, and subsequently validated in an external multicentre cohort of 454 patients (76.3 ± 12.6 years old, 69.1% male). Patients with ATTR-CM had a lower systolic function [left ventricular ejection fraction 47 ± 11 vs. 54 ± 12%, P < 0.00; global longitudinal strain (GLS) 11.0 ± 3.7 vs. 14.1 ± 4.5%, P < 0.001] and more significant relative apical longitudinal sparing (RALS) (1.5 ± 1.2 vs. 0.9 ± 0.4, P < 0.001) compared with controls. Machine learning identified right ventricular free wall thickness (RVFWT), RALS, GLS, and LV mass index as key variables for detecting ATTR-CM [AUC 0.90 (0.86–0.94); P < 0.001]. These variables enhanced diagnostic accuracy compared with the increased wall thickness guideline score [increase in C-index of 0.17 (0.11–0.23), P < 0.001]. Diagnostic performance was confirmed in the validation multicentre cohort [AUC of 0.83 (0.80–0.87), P < 0.001] Conclusion The simple GRAAL algorithm (GLS, RVFWT, Apical spAring, LV Mass) enhances detection accuracy for ATTR-CM and improves patient selection for bone scintigraphy.
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Fraix et al. (2026) studied this question. The GRAAL echocardiographic algorithm detected transthyretin amyloid cardiomyopathy with an AUC of 0.90, increasing the C-index by 0.17 compared to the standard guideline score.
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