Why the study?
Early detection of ATTR-CM is important given new disease-modifying therapies and its high prevalence among patients with AS undergoing TAVR.
Does a multimodal AI model using chest CT, echocardiography, and electrocardiography improve the detection of ATTR-CM in patients undergoing TAVR?
Observational (n=816)
Yes
Does a multimodal AI model using chest CT, echocardiography, and electrocardiography improve the detection of ATTR-CM in patients undergoing TAVR?
Answers are grounded in this paper and cite their sources.
A multimodal AI approach using routine chest CT, echocardiography, and ECG can effectively screen for ATTR-CM in TAVR patients, potentially facilitating earlier diagnosis.
Background and Aims Early detection is important given the availability of new disease-modifying therapies and the high prevalence of transthyretin amyloid cardiomyopathy (ATTR-CM) among patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). We developed a multimodal artificial intelligence (AI) model for early detection of ATTR-CM using chest computed tomography (CT), echocardiography, and electrocardiography. This approach may provide a scalable strategy for preclinical monitoring. Methods This retrospective study included patients who underwent technetium-99m–pyrophosphate scintigraphy (PYP) at two academic medical centers: Columbia University Irving Medical Center and Weill Cornell Medicine. ATTR-CM status was determined using a composite reference standard incorporating PYP scan interpretation, laboratory tests, and endomyocardial biopsy results when available. The diagnostic performance of the model was measured by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and predictive values at various thresholds. Results Among 816 patients (median age 79.0 years, 61.2% male), 127 (15.6%) had confirmed ATTR-CM. Patients with ATTR-CM were older, more often male, and had characteristic echocardiographic features, including increased wall thickness and reduced ejection fraction. In the independent TAVR test cohort, the multimodal AI model achieved an AUROC of 0.85 (95% CI: 0.74-0.93), significantly outperforming single-modality approaches in our data. At the optimal threshold, the model demonstrated 73.3% sensitivity, 82.9% specificity, and 96% negative predictive value. Conclusion and Relevance A multimodal AI approach using routinely acquired chest computed tomography, echocardiography, and electrocardiography data can enable screening for ATTR-CM in TAVR patients, potentially facilitating earlier diagnosis and treatment initiation.
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Nizam et al. (2026) conducted an observational in Transthyretin cardiac amyloidosis in severe aortic stenosis (n=816). Multimodal artificial intelligence (AI) model vs. Single-modality approaches was evaluated on Area under the receiver operating characteristic curve (AUROC) for ATTR-CM detection (AUROC 0.85, 95% CI 0.74-0.93).
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