Tanja Kero

Aims: Left ventricular (LV) hypertrophy is associated with adverse cardiac outcomes. LV mass index (LVMi) and interventricular septal wall thickness (WT) can be derived from 15O-water positron emission tomography (PET), but their prognostic value is unknown. We aimed to assess the prognostic value and test–retest repeatability of PET-derived LV hypertrophy metrics in patients evaluated for suspected coronary artery disease (CAD). Methods and results: We retrospectively included 783 consecutive patients referred for rest-stress PET. LVMi and WT were determined on non-ECG-gated resting 15O-water PET scans. Subjects were stratified by previous heart failure (HF) diagnosis and patients with known hypertrophic/infiltrative cardiomyopathy were excluded. The primary outcome was major adverse cardiac events (MACE: cardiovascular death or acute HF). The prognostic value of LV hypertrophy measurements was evaluated using Cox hazard ratios (HR [95% confidence interval]). Test–retest repeatability was evaluated in prospective data from 21 patients with repeated PET scans. Patients with known HF but no hypertrophic/infiltrative cardiomyopathy (18%) had significantly higher LVMi (P < 0.0001) and WT (P = 0.0015) compared to subjects without HF (82%). Among patients without HF, 30 experienced MACE during a median 3.8 year follow up. Both LVMi (HR 1.04 [1.02–1.06] per g/m2, P < 0.0001) and WT (HR 1.36 [1.10–1.68] per mm, P = 0.0045) independently predicted MACE after adjustment for sex, age and myocardial flow reserve. Test–retest repeatability was high, with no significant bias. Conclusion: PET LV hypertrophy metrics can be robustly quantified from routine 15O-water PET and provide prognostic information beyond myocardial perfusion.

Aims: The relationship between the extent and composition of coronary atherosclerosis and the severity of myocardial ischaemia remains incompletely understood. We assessed whether artificial intelligence-guided coronary computed tomography angiography–derived plaque burden and composition correlate with ischaemia severity. Methods and results: We included 837 symptomatic patients undergoing coronary computed tomography angiography and subsequent 15O-water positron emission tomography myocardial perfusion imaging. Artificial intelligence–guided coronary computed tomography angiography was used to quantify plaque features—diameter stenosis, percent atheroma volume (PAV), percent non-calcified plaque volume (NCPV), and percent calcified plaque volume (CPV)—per patient and per major coronary artery (LAD, LCx, RCA). Ischaemia severity was classified into four categories based on regional hyperaemic myocardial blood flow. Increasing severity of ischaemia was associated with higher diameter stenosis and plaque burden (PAV, NCPV, CPV) on patient level and in all major coronary territories (overall P < 0.001). The LAD consistently demonstrated higher atherosclerotic burden as compared to the LCx and RCA. Ordinal logistic regression confirmed that diameter stenosis (OR 1.02–1.03, P < 0.001) and NCPV (OR 1.04–1.05, P = 0.011–0.031) were significant predictors of ischaemia severity in all coronary arteries, while CPV was predictive only in the LAD and RCA (OR 1.03–1.04, P = 0.002–0.015). Conclusion: Artificial intelligence–guided coronary computed tomography angiography–derived measures of plaque burden and stenosis are associated with the severity of myocardial ischaemia, although overlapping distributions across ischaemia severity indicate that anatomical imaging alone may be insufficient for accurate phenotyping of flow-limiting CAD. These findings encourage for the integration of functional imaging with quantitative plaque analysis for a more comprehensive evaluation of coronary artery disease.