AI-Derived Splenic Response in Cardiac PET Predicts Mortality: A Multi-Site Study

BackgroundInadequate pharmacologic stress may limit the diagnostic and prognostic accuracy of myocardial perfusion imaging (MPI). The splenic ratio (SR), a measure of stress adequacy, has emerged as a potential imaging biomarker. ObjectivesTo evaluate the prognostic value of artificial intelligence (AI)-derived SR in a large multicenter 82Rb-PET cohort undergoing regadenoson stress testing. MethodsWe retrospectively analyzed 10,913 patients from three sites in the REFINE PET registry with clinically indicated MPI and linked clinical outcomes. SR was calculated using fully automated algorithms as the ratio of splenic uptake at stress versus rest. Patients were stratified by SR into high ([&ge;]90th percentile) and low (<90th percentile) groups. The primary outcome was major adverse cardiovascular events (MACE). Survival analysis was conducted using Kaplan-Meier and Cox proportional hazards models adjusted for clinical and imaging covariates, including myocardial flow reserve (MFR [&ge;]2 vs. <2). ResultsThe cohort had a median age of 68 years, with 57% male patients. Common risk factors included hypertension (84%), dyslipidemia (76%), diabetes (33%), and prior coronary artery disease (31%). Median follow-up was 4.6 years. Patients with high SR (n=1,091) had an increased risk of MACE (HR 1.18, 95% CI 1.06-1.31, p=0.002). Among patients with preserved MFR ([&ge;]2; n=7,310), high SR remained independently associated with MACE (HR 1.44, 95% CI 1.24-1.67, p<0.0001). ConclusionsElevated AI-derived SR was independently associated with adverse cardiovascular outcomes, including among patients with preserved MFR. These findings support SR as a novel, automated imaging biomarker for risk stratification in 82Rb PET MPI. Condensed AbstractAI-derived splenic ratio (SR), a marker of pharmacologic stress adequacy, was independently associated with increased cardiovascular risk in a large 82Rb PET cohort, even among patients with preserved myocardial flow reserve (MFR). High SR identified individuals with elevated MACE risk despite normal perfusion and flow findings, suggesting unrecognized physiologic vulnerability. Incorporating automated SR into PET MPI interpretation may enhance risk stratification and identify patients who could benefit from intensified preventive care, particularly when traditional imaging markers appear reassuring. These findings support SR as a clinically meaningful, easily integrated biomarker in stress PET imaging.