Preferred Health Imaging

We Keep Predicting Risk. The Plaque Already Knows. For decades, preventive cardiology has revolved around estimating the probability of obstructive CAD. Age. Sex. Symptoms. Risk factors. This study asks a different question: **What if the best predictor isn't the probability of disease... but the amount of disease already present?** Using AI-based quantitative CCTA in more than **6,000 symptomatic patients**, the CONFIRM2 investigators found that **total plaque volume (TPV)** was the main driver of future cardiovascular events, regardless of whether patients had a very low, low, or moderate pre-test likelihood of obstructive CAD. The numbers are striking. At 4 years, MACE rates increased from: - **1.4%** with no plaque - **3.3%** with minimal plaque - **12.6%** with TPV 250–750 mm³ - **18.6%** with TPV >750 mm³ And these event rates were remarkably consistent across all clinical likelihood categories. That should make us pause. For years we have refined clinical scores to estimate the likelihood of finding obstructive stenosis. Meanwhile, the **actual burden of atherosclerosis** appears to carry far more prognostic information. ## The Bigger Message This is another piece of evidence supporting a shift that has been emerging for years: **Risk factors don't cause events.** **Plaques do.** Risk factors help us estimate who *might* have disease. CCTA allows us to measure who *already has it*. Those are fundamentally different questions. ## My Take This paper is not really about AI. AI simply makes comprehensive plaque quantification scalable. The real story is that cardiovascular prevention is gradually moving away from a **risk-factor paradigm** toward a **disease-based paradigm**. The artery has become the biomarker. And once you can quantify the entire atherosclerotic burden, it becomes increasingly difficult to argue that stenosis should remain the center of the conversation. ## Where PCCT Comes In This study used conventional CCTA. Now imagine combining this biological approach with Photon Counting CT. - Higher spatial resolution. - Better characterization of non-calcified plaque. - Improved detection of subtle high-risk features. - More reproducible plaque quantification. If total plaque volume is already one of the strongest predictors of outcome, improving how we measure plaque—not just stenosis—may be where the next leap in preventive cardiology occurs. **Treat the plaque. Not the probability of finding one.** #CardiacCT #CCTA #Atherosclerosis #PreventiveCardiology #TreatThePlaque #PhotonCountingCT #PCCT #ArtificialIntelligence #CardiovascularImaging #PrecisionMedicine