Citations

Abstract Aims Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is estimated that a large proportion of those with early systolic dysfunction remain asymptomatic at a stage when guideline-directed medical therapies have been shown to prevent disease progression. To develop an artificial intelligence (AI) model capable of predicting abnormal left ventricular ejection fraction (EF) directly from static, non-gated, non-contrast chest computed tomography (CT) scans as a form of opportunistic screening, Methods and results Using a multi-institutional dataset of 34 058 paired non- contrast CT images and echocardiogram reports from two academic centres, we trained our model of classification for predicting left-ventricle ejection fraction (LVEF) categories: abnormal EF (EF < 50%) vs. normal on 25 948 studies. We validated the model on 8110 paired chest CT and echocardiogram results from a separate institution. The model achieved an area under the receiver operating characteristic (AUROC) curve of 0.786 on the hold-out test set and 0.762 on external validation to detect an abnormal EF (<50%). Beyond strong predictive performance, the AI model surpassed expert radiologists in both accuracy and efficiency and provided interpretable visualizations highlighting imaging features linked to reduced LVEF. Conclusion In this study, we developed and validated an AI model capable of predicting abnormal LVEF directly from static, non-gated, non-contrast chest CT scans, a novel application for an imaging modality typically used for unrelated indications as a form of opportunistic screening. This technology holds significant promise for early detection of systolic HF, reducing the diagnostic gap, and improving outcomes in asymptomatic HF patients.

BACKGROUND: Although chronic obstructive pulmonary disease (COPD) and heart failure with preserved ejection fraction often coexist with overlapping clinical features, they are usually studied separately. The SPIROMICS HF (Subpopulations and Intermediate Outcome Measures in COPD and Heart Failure Study) is testing hypotheses that new computed tomography emphysema subtypes are associated with specific cardiovascular phenotypes (eg, cor pulmonale , cor pulmonale parvus ), common airway branch variants are associated with right heart dysfunction, and symptomatic tobacco-exposed persons with preserved spirometry have signs of increased left ventricular afterload. METHODS: SPIROMICS is a multicenter observational study of COPD with extensive pulmonary phenotyping of participants with ≥20 pack-years smoking and nonsmoking controls. COPD and COPD severity were defined by standard spirometric criteria and symptomatic tobacco-exposed persons with preserved spirometry by ≥20 pack-years, normal spirometry, and COPD Assessment Test score >10. SPIROMICS HF selected all participants in SPIROMICS visit 5 at major sites. Its comprehensive speckle-tracking echocardiography, which included physiological perturbations of leg raise and low-intensity exercise, was harmonized prospectively with the Multi-Ethnic Study of Atherosclerosis Early Heart Failure and HeartSHARE (Combining Omics, Deep Phenotyping, and Electronic Health Records for Heart Failure Subtypes and Treatment Targets) studies. The cardiopulmonary magnetic resonance imaging protocol with gadolinium administration included myocardial fibrosis sequences, pulmonary angiography, time-resolved 3-dimensional cine magnetic resonance imaging (4-dimensional flow) of venous return, and metronome-paced tachypnea to induce dynamic hyperinflation. Coronary artery calcium was assessed on computed tomography scans. The Kansas City Cardiomyopathy Questionnaire was administered. RESULTS: Of the final sample of 753 participants, 57% had COPD (15% mild, 27% moderate, and 15% severe), 18% had symptomatic tobacco-exposed persons with preserved spirometry, 16% were smoking controls, and 8% were nonsmoking controls. Reproducibility of the main measures from speckle-tracking echocardiography (intraclass correlation coefficient, 0.83–0.99), exercise echocardiography (intraclass correlation coefficient, 0.71–0.99) and magnetic resonance imaging (intraclass correlation coefficient, 0.57–0.99) were good-to-excellent, including in severe COPD. CONCLUSIONS: SPIROMICS HF aims to characterize and understand cardiopulmonary interactions in COPD and COPD-related phenotypes to inform targeted treatments for combined cardiopulmonary failure.

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water‐fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.