DOI: 10.1161/circ.148.suppl_1.16844 ISSN: 0009-7322

Abstract 16844: Identifying Splenic Radiomics Features Associated With Risk of Coronary Artery Disease

Meghana Kamineni, Zhi Yu, Vineet Raghu, Ahmed M Alaa, Art Schuermans, Sam F Friedman, Christopher Reeder, Patrick T Ellinor, Mahnaz Maddah, Anthony A Philippakis, Whitney Hornsby, Pradeep Natarajan
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Introduction: Inflammation is a validated mechanism for residual coronary artery disease (CAD) risk via clinical trials and analysis of germline and somatic genetic variation in blood cells. While the spleen plays an important role in regulating inflammation, the role it plays in CAD is unclear.

Hypothesis: We aim to test the hypothesis that radiographic splenic biomarkers may offer novel insights for CAD.

Methods: We used deep learning to segment spleens from the abdominal MRIs of 38,474 patients in the UK Biobank. CAD was defined as history of coronary artery bypass grafting, myocardial infarction (MI), coronary artery angioplasty, or billing codes (OPCS-4: K40, K41, K45, K49, K50.2, K75). We extracted image features using the pyradiomics package. To identify image features associated with CAD, we used L1-regularized logistic regression models with the splenic features and clinical features used in the Pooled Cohort Equations (PCE) for outcomes of CAD and cardiovascular disease (CVD) events (defined as a composite of CAD, MI, and stroke outcomes). We computed the area under the receiver operating characteristic (AUROC) on a held-out 30% of the patient population and determined 95% confidence intervals using 1,000 Monte-Carlo resampled bootstraps.

Results: Study participants had mean (SD) age 55.1 (7.5) years, 19,921 (51.8%) were women, 1,412 (3.7%) had CAD, and 2,473 (6.4%) had CVD events. The model to predict CAD using the splenic and PCE features had an AUROC of 0.84 (95% CI, 0.84-0.85), and 3 out of 10 significant covariates were splenic features. 1-SD increases in sphericity, minor axis length, and entropy in neighboring voxel gray levels were associated with 5.4% (95% CI, 5.1-5.7) higher, 13.3% (95% CI, 13.0-13.6) lower, and 10.5% (95% CI, 10.2-10.8) higher odds of CAD after adjustment. Results were consistent in the model to predict CVD events, and decreased minor axis length and increased texture variation were associated with increased risk. Model performance was lower for older age groups, who had higher incidences of CAD.

Conclusions: This study leverages deep-learning methods across a population based cohort to highlight splenic features correlated with CAD and offers new opportunities to understand the role of the hematopoietic system in CAD.

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