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Carlos Andrés Vergara Sánchez: AI-Driven CCTA Plaque Quantification for Better ASCVD Risk Assessment
Carlos Andrés Vergara Sánchez/LinkedIn;Shyon Parsa/Stanford Medicine
Mar 11, 2026, 13:47

Carlos Andrés Vergara Sánchez: AI-Driven CCTA Plaque Quantification for Better ASCVD Risk Assessment

Carlos Andrés Vergara Sánchez, Chief Cardiovascular Disease Fellow at Mayo Clinic College of Medicine and Science, shared a post on LinkedIn, about a recent article by Shyon Parsa et al, adding:

“New insights on ASCVD risk stratification using AI‑enabled CCTA plaque quantification.

A recent study by S. Parsa et al. explores an important question:

Can AI‑derived plaque quantification from CCTA guide lipid‑lowering therapy and individualize ASCVD prevention?

Their findings suggest the answer may be yes.

Study Overview

  • 6,054 adults from the FISH and CHIPS observational study
  • Conducted across two UK clinical sites
  • Underwent clinically indicated CCTA with AI plaque quantification in symptomatic patients
  • Total plaque volume (TPV) categorized into four DECIDE stages (1–4), which were then linked to LDL‑C targets for a ‘treat‑to‑target’ strategy.

LDL‑C Targets by DECIDE Stage

  • Stage 1: LDL‑C less than 100 mg/dL
  • Stage 2: LDL‑C less than 70 mg/dL
  • Stage 3: LDL‑C less than 55 mg/dL
  • Stage 4: LDL‑C less than 40 mg/dL

(Usual care LDL‑C approximately 100 mg/dL)

Key Findings

10‑year Relative Risk Reduction in MACE

  • Stage 1: 1.47 percent
  • Stage 2: 18.15 percent
  • Stage 3: 24.24 percent
  • Stage 4: 33.76 percent

10‑year Number Needed to Treat (NNT)

  • Stage 1: 1686
  • Stage 2: 59
  • Stage 3: 27
  • Stage 4: 11

Across the full cohort:

  • 19.12 percent risk reduction
  • NNT of 61
  • Importantly, the NNT continued improving at 3‑, 5‑, and 10‑year follow‑ups.

Why This Matters

This AI‑enabled, plaque‑guided strategy could potentially:

  • Improve cardiovascular outcomes
  • Identify high‑risk patients who would benefit from therapy intensification
  • Move prevention toward a more individualized, imaging‑driven paradigm.

This is a very important work in the new era of AI-driven care.

As we try to find different ways to optimize and personalize prevention strategies before we manage the consequences of atherosclerosis, whether there is presence of events or not.

Translating the use of standardized AI based plaque analysis technology into tangible outcomes, gives more of a base for clinicians to decide whether it is worth and cost effective to go beyond CAC score given the current technology, and its continued advancement.

Where does this leave us?

Cardiovascular disease prevention is in an era where we have more tools for every aspect.

AI is here to stay and implementing it efficiently in our clinical practice will help us more than ever to capture more patients that would benefit from our expanding arsenal of therapies, earlier and more aggressively.

AI plaque analysis is a promising technology already showing results, the cost of implementation will be an issue, and to adapt to a new paradigm, more data is needed, fast.

Something is clear, the evidence of ‘lower is better’ when it comes to atherogenic lipoproteins (i.e. LDL) continues to be supported with every study.

This is a study worth reading carefully.”

Title: Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH and CHIPS study

Authors: Shyon Parsa, Allison W. Peng, Jack Bell, Souma Sengupta, Sarah Mullen, Campbell Rogers, Edward D Nicol, Jonathan R Weir-McCall, Laurence Tidbury, Seth S. Martin, Timothy Fairbairn, Fatima Rodriguez

Read the Full Article on American Journal of Preventive Cardiology

Carlos Andrés Vergara Sánchez: AI-Driven CCTA Plaque Quantification for Better ASCVD Risk Assessment

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Allison W. Peng ASCVD Campbell Rogers Cardiology Cardiovascular Disease Carlos Andrés Vergara Sánchez CCTA Edward D. Nicol Fatima Rodriguez Health Hematology Hemostasis News Hemostasis Today Jack Bell Jonathan R Weir-McCall Laurence Tidbury Medicine Sarah Mullen Seth S. Martin Shyon Parsa Souma Sengupta Timothy Fairbairn
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