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. 2024 Aug;10(8):1873-1884.
doi: 10.1016/j.jacep.2024.04.006. Epub 2024 Jun 5.

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR

Mark S Brahier  1 , Shwetha Kochi  2 , Julia Huang  2 , Emma Piliponis  2 , Andrew Smith  2 , Adam Johnson  2 , Suraya Poian  2 , Musa Abdulkareem  3 , Xiaoyang Ma  2 , Colin Wu  4 , Jonathan P Piccini  5 , Steffen Petersen  6 , Jose D Vargas  7
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Free article

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR

Mark S Brahier et al. JACC Clin Electrophysiol. 2024 Aug.
Free article

Abstract

Background: New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR.

Objectives: The objective of this analysis was to compare and assess standard, manual echocardiographic and cardiac computed tomography (cCT) measurements as well as machine learning-derived cCT measurements of left atrial volume index and epicardial adipose tissue as risk factors for NOAF following TAVR.

Methods: The study included 1,385 patients undergoing elective, transfemoral TAVR for severe, symptomatic aortic stenosis. Each patient had standard and machine learning-derived measurements of left atrial volume and epicardial adipose tissue from cardiac computed tomography. The outcome of interest was NOAF within 30 days following TAVR. We used a 2-step statistical model including random forest for variable importance ranking, followed by multivariable logistic regression for predictors of highest importance. Model discrimination was assessed by using the C-statistic to compare the performance of the models with and without imaging.

Results: Forty-seven (5.0%) of 935 patients without pre-existing atrial fibrillation (AF) experienced NOAF. Patients with pre-existing AF had the largest left atrial volume index at 76.3 ± 28.6 cm3/m2 followed by NOAF at 68.1 ± 26.6 cm3/m2 and then no AF at 57.0 ± 21.7 cm3/m2 (P < 0.001). Multivariable regression identified the following risk factors in association with NOAF: left atrial volume index ≥76 cm2 (OR: 2.538 [95% CI: 1.165-5.531]; P = 0.0191), body mass index <22 kg/m2 (OR: 4.064 [95% CI: 1.500-11.008]; P = 0.0058), EATv (OR: 1.007 [95% CI: 1.000-1.014]; P = 0.043), aortic annulus area ≥659 mm2 (OR: 6.621 [95% CI: 1.849-23.708]; P = 0.004), and sinotubular junction diameter ≥35 mm (OR: 3.891 [95% CI: 1.040-14.552]; P = 0.0435). The C-statistic of the model was 0.737, compared with 0.646 in a model that excluded imaging variables.

Conclusions: Underlying cardiac structural differences derived from cardiac imaging may be useful in predicting NOAF following transfemoral TAVR, independent of other clinical risk factors.

Keywords: TAVR; cardiac imaging; machine learning; new-onset atrial fibrillation.

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Conflict of interest statement

Funding Support and Author Disclosures This work was supported by the National Institute for Health and Care Research Barts Biomedical Research Centre (NIHR203330); a delivery partnership of Barts Health NHS Trust, Queen Mary University of London, St George’s University Hospitals NHS Foundation Trust and St George’s University of London. Drs Abdulkareem and Petersen receive support from the CAP-AI programme (led by Capital Enterprise in partnership with Barts Health NHS Trust and Digital Catapult and funded by the European Regional Development Fund and Barts Charity) and Health Data Research UK (HDR UK—an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities). Dr Petersen also receives support from the SmartHeart EPSRC programme grant (EP/P001009/1) and the London Medical Imaging and AI Center for Value-Based Healthcare; and has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 825903 (euCanSHare project). DrPiccini is supported by National Institutes of Aging grant R01AG074185. He has also received grants for clinical research from Abbott, the American Heart Association, the Association for the Advancement of Medical Instrumentation, Bayer, Boston Scientific, iRhythm, and Philips; and serves as a consultant to Abbott, Abbvie, ARCA biopharma, Bayer, Boston Scientific, Bristol Myers Squibb (Myokardia), Element Science, Itamar Medical, LivaNova, Medtronic, Milestone, ElectroPhysiology Frontiers, ReCor, Sanofi, Philips, and Up-to-Date. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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