⚡ Quick Summary
The FISH&CHIPS study highlights the effectiveness of artificial intelligence-enabled coronary plaque quantification (AI-CPA) in assessing cardiovascular risk and guiding personalized lipid-lowering therapy. This innovative approach could lead to a 19.1% reduction in cardiovascular events over ten years, showcasing the potential of AI in cardiovascular care.
🔍 Key Details
- 📊 Study Population: 7,899 symptomatic patients undergoing CCTA
- 🧩 Analysis Method: AI-based quantitative coronary plaque analysis
- ⚙️ Risk Staging: Total plaque volume (TPV) categorized into four stages
- 🏆 Primary Outcome: Estimated reduction in cardiovascular death or non-fatal myocardial infarction (MI)
🔑 Key Takeaways
- 📊 AI-CPA provides a novel method for assessing coronary artery disease (CAD).
- 💡 Total plaque volume (TPV) is used to categorize patients into risk stages for tailored therapy.
- 🧑⚕️ Personalized treatment can significantly reduce cardiovascular risks.
- 🏥 The study included 6,054 patients with plaque, average age 59.4 years.
- 📉 10-year risk reduction varied by risk stage, with a maximum of 33.8% for the highest risk group.
- 🔍 Number needed to treat (NNT) was as low as 11 for the highest risk patients.
- 🌍 Conducted in the UK, emphasizing the importance of population-level data.
- 🔮 Future implications for AI in personalized medicine and cardiovascular health.
📚 Background
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Traditional methods of assessing cardiovascular risk often lack the precision needed for effective management. The integration of artificial intelligence into coronary computed tomographic angiography (CCTA) offers a promising avenue for enhancing risk assessment and tailoring treatment strategies for patients with atherosclerotic cardiovascular disease (ASCVD).
🗒️ Study
The FISH&CHIPS study analyzed data from adult patients who underwent clinically indicated CCTA across two sites in the UK. The researchers employed AI-CPA to categorize total plaque volume (TPV) into four distinct risk stages, allowing for a more nuanced approach to lipid-lowering therapy. The study aimed to model the utility of this risk staging system in guiding treatment decisions for patients with stable suspected or known CAD.
📈 Results
The findings revealed that the overall 10-year modeled relative risk reduction using a TPV-based treat-to-target LDL-C strategy was 19.1%, with a number needed to treat (NNT) of 61. The risk reduction and NNT varied significantly across the DECIDE stages, highlighting the importance of personalized treatment approaches. For instance, patients in the highest risk category experienced a remarkable 33.8% reduction in risk with an NNT of just 11.
🌍 Impact and Implications
The implications of this study are profound. By utilizing AI-CPA for coronary plaque quantification, healthcare providers can identify patients at elevated long-term cardiovascular risk more effectively. This approach not only enhances the precision of risk assessment but also facilitates the implementation of personalized lipid-lowering strategies, potentially leading to a significant decrease in cardiovascular events. The integration of AI in clinical practice could revolutionize how we approach cardiovascular health.
🔮 Conclusion
The FISH&CHIPS study underscores the transformative potential of artificial intelligence in cardiovascular risk assessment and management. By leveraging AI-enabled coronary plaque quantification, healthcare professionals can deliver more personalized and effective treatment strategies, ultimately improving patient outcomes. As we continue to explore the capabilities of AI in medicine, the future looks promising for enhanced cardiovascular care.
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Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH&CHIPS study✰.
Abstract
BACKGROUND: Coronary computed tomographic angiography (CCTA) is a guideline-endorsed tool to evaluate coronary artery disease (CAD) in symptomatic patients. Artificial intelligence enabled quantitative coronary plaque analysis on CCTA (AI-CPA) is a promising strategy for tailored management of atherosclerotic cardiovascular disease (ASCVD). Population-level data are needed on how CCTA-derived plaque analyses can inform lipid-lowering strategies for ASCVD risk reduction.
OBJECTIVES: To model the utility and efficiency of a total plaque volume (TPV)-based risk staging system in guiding lipid-lowering therapy in patients undergoing clinically-indicated CCTAs for evaluation of stable suspected or known CAD.
METHODS: We analyzed adult patients from the Computed Tomography Angiography Helps/Hinders Improve Patient care and Societal costs (FISH&CHIPS) across 2 sites in the UK who underwent a clinically-indicated CCTA with AI-based quantitative plaque analysis. TPV was categorized into four risk stages using pre-defined thresholds of 1-100, 101-250, 251-750, and >750 mm3 for DECIDE stages 1-4, respectively. The primary outcome was the estimated reduction in cardiovascular death or non-fatal MI, and the number needed to treat (NNT) based on AI-CPA, over 10 years. We modeled lipid-lowering therapy utilizing treat-to-target LDL-C goals of <100, <70, <55, and <40 mg/dL for DECIDE stages 1-4, respectively, to estimate risk reduction and NNT over 10 years.
RESULTS: The study population included 7899 total symptomatic participants undergoing CCTA and AI-CPA. Of these, 6054 patients had any plaque and were included in the final cohort; the mean age was 59.4 ± 11.7 years and 42.7% were women. Among the full cohort, the 10-year modeled relative risk reduction using a TPV treat-to-target LDL-C was 19.1% with NNT of 61. The 10-year relative risk reduction and NNT by DECIDE stages 1-4 was 1.5% (NNT = 1686), 18.2% (NNT = 59), 24.2% (NNT = 27), and 33.8% (NNT = 11), respectively.
CONCLUSIONS: Quantitative TPV measured by AI-CPA identifies symptomatic patients at elevated long-term cardiovascular risk and may efficiently inform implementation of personalized lipid-lowering strategies to reduce cardiovascular events.
Author: [‘Parsa S’, ‘Peng AW’, ‘Bell J’, ‘Sengupta S’, ‘Mullen S’, ‘Rogers C’, ‘Nicol ED’, ‘Weir-McCall JR’, ‘Tidbury L’, ‘Martin SS’, ‘Fairbairn T’, ‘Rodriguez F’]
Journal: Am J Prev Cardiol
Citation: Parsa S, et al. Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH&CHIPS study✰. Artificial intelligence-enabled coronary plaque quantification for personalized risk assessment and lipid-lowering therapy: Insights from the FISH&CHIPS study✰. 2026; 26:101452. doi: 10.1016/j.ajpc.2026.101452