Validation of an Artificial Intelligence-Derived ECG Algorithm for Detecting Cardiac Amyloidosis in Patients With Heart Failure With Preserved Ejection Fraction: Clinical Application and Prognostic Implications.

BACKGROUND: Cardiac transthyretin amyloidosis (ATTR-CA) is frequently underdiagnosed and commonly presents as heart failure with preserved ejection fraction (HFpEF). Early identification enables disease-modifying therapy but remains challenging in routine practice, so simple, widely available screening tools are needed. METHODS: In this multicenter validation study, 885 patients with HF from 2 European centers were included. The internal validation cohort comprised 560 patients with preserved ejection fraction and analyzable ECGs: 149 with ATTR-CA, 318 with HFpEF, and 93 with hypertrophic cardiomyopathy. External validation used an independent cohort of 107 patients (72 ATTR-CA, 31 HFpEF, 4 hypertrophic cardiomyopathy). Standard 12-lead ECGs were analyzed blindly by 3 independent observers using a previously developed, 2-step, artificial intelligence-derived, visually interpretable ECG algorithm. RESULTS: The ECG pattern was present in 82.6% of patients with ATTR-CA, versus 10.2% with HFpEF and 6.5% with hypertrophic cardiomyopathy (P<0.001). Internal-cohort accuracy was high: area under the curve 0.87 (95% CI, 0.84-0.90), sensitivity 83% (95% CI, 76%-88%), specificity 91% (95% CI, 88%-93%), and negative predictive value 93% (95% CI, 91%-96%). In the external cohort, the area under the curve was 0.84 (95% CI, 0.76-0.92), with sensitivity 89% (95% CI, 78%-94%) and specificity 79% (95% CI, 63%-90%). The pattern was strongly associated with ATTR-CA (odds ratio, 46 [95% CI, 27-80]; P<0.001) and with reduced 3-year survival (log-rank P=0.007). CONCLUSIONS: A visually interpretable, artificial intelligence-derived ECG algorithm enables effective screening for ATTR-CA among patients with HFpEF. Its simplicity and compatibility with standard ECG systems support broad clinical implementation.