Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.
Purpose: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely c...
| Published in: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 2; pp. 485 - 501 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jan2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182239520&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182239520 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Jan2025 vid: 52 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 182239520 179892344 10.1007/s00259-024-06922-4 182239520 ppf: 485 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis. aug: au: Shiri, Isaac Balzer, Sebastian Baj, Giovanni Bernhard, Benedikt Hundertmark, Moritz Bakula, Adam Nakase, Masaaki Tomii, Daijiro Barbati, Giulia Dobner, Stephan Valenzuela, Waldo Rominger, Axel Caobelli, Federico Siontis, George C. M. Lanz, Jonas Pilgrim, Thomas Windecker, Stephan Stortecky, Stefan Gräni, Christoph affil: https://ror.org/02k7v4d05 Department of Cardiology, Inselspital Bern University Hospital, University of Bern, Freiburgstrasse, CH – 3010, Bern, Switzerland sug: ab: Purpose: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely collected data to detect ATTR-CM in patients with severe AS planned for transcatheter aortic valve implantation (TAVI). Methods: In this prospective, single-center study, consecutive patients with AS were screened with [99mTc]-3,3-diphosphono-1,2-propanodicarboxylic acid ([99mTc]-DPD) for the presence of ATTR-CM. Clinical, laboratory, electrocardiogram, echocardiography, invasive measurements, 4-dimensional cardiac CT (4D-CCT) strain data, and CT-radiomic features were used for machine learning modeling of ATTR-CM detection and for outcome prediction. Feature selection and classifier algorithms were applied in single- and multi-modality classification scenarios. We split the dataset into training (70%) and testing (30%) samples. Performance was assessed using various metrics across 100 random seeds. Results: Out of 263 patients with severe AS (57% males, age 83 ± 4.6years) enrolled, ATTR-CM was confirmed in 27 (10.3%). The lowest performances for detection of concomitant ATTR-CM were observed in invasive measurements and ECG data with area under the curve (AUC) < 0.68. Individual clinical, laboratory, interventional imaging, and CT-radiomics-based features showed moderate performances (AUC 0.70–0.76, sensitivity 0.79–0.82, specificity 0.63–0.72), echocardiography demonstrated good performance (AUC 0.79, sensitivity 0.80, specificity 0.78), and 4D-CT-strain showed the highest performance (AUC 0.85, sensitivity 0.90, specificity 0.74). The multi-modality model (AUC 0.84, sensitivity 0.87, specificity 0.76) did not outperform the model performance based on 4D-CT-strain only data (p-value > 0.05). The multi-modality model adequately discriminated low and high-risk individuals for all-cause mortality at a mean follow-up of 13 months. Conclusion: Artificial intelligence-based models using collected pre-TAVI evaluation data can effectively detect ATTR-CM in patients with severe AS, offering an alternative diagnostic strategy to scintigraphy and myocardial biopsy. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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