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Published on: August 28, 2018
Biological Cardiovascular Age Derived from Coronary CTA Reports Using a Large Language Model: A Novel Predictor of
Gudrun M Feuchtner1, Yannick Scharll1, Johannes Deeg1
1Department of Radiology, Innsbruck Medical University, 6020 Innsbruck, Austria.
Insights
Artificial intelligence (AI) can calculate biological cardiovascular age from coronary computed tomography angiography (CTA) reports. This AI-enhanced biological age better predicts major adverse cardiovascular events (MACE) than chronological age.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- Traditional cardiovascular risk assessment methods have limitations in accuracy.
- Novel approaches are needed to improve cardiovascular risk prediction.
Purpose of the Study:
- To evaluate an AI-driven method for calculating biological cardiovascular age from coronary CTA reports.
- To assess the predictive capability of this AI-enhanced biological age for major adverse cardiovascular events (MACE).
Main Methods:
- A large language model (LLM), ChatGPT-4.0v, analyzed coronary CTA reports.
- Key metrics included coronary artery calcium (CAC) score, stenosis severity (CAD-RADS), plaque characteristics, and left ventricular ejection fraction (LVEF).
- 346 reports from symptomatic patients with suspected CAD were successfully analyzed.
Main Results:
- The mean biological age (bioAGE) was 57.2 years, compared to a chronological age of 58.5 years.
- Biological age exceeded chronological age in 45.4% of patients, indicating significant intra-individual deviations.
- AI-enhanced bioAGE demonstrated superior accuracy (c = 0.768) in predicting MACE compared to chronological age (c = 0.590).
Conclusions:
- Calculating biological age from coronary CTA reports using LLM is feasible.
- While intra-individual deviations in bioAGE can be substantial, it offers improved MACE prediction accuracy over chronological age.
- This AI-enhanced approach holds promise for more precise cardiovascular risk stratification.
Abstract:
Background/Objectives: Coronary artery disease (CAD) remains the leading cause of death worldwide. Traditional cardiovascular risk assessment is based on chronological age and other clinical factors, with inherent limitations and poor accuracy. Objective was to estimate the artificial intelligence (AI)-enhanced biological cardiovascular age calculation derived from coronary computed tomography angiography (CTA) reports using a large language model (LLM), in predicting major adverse cardiovascular events (MACE). Methods: Coronary CTA reports were analyzed using a LLM (ChatGPT-4.0v, OpenAI), from symptomatic patients with suspected CAD who underwent coronary CTA for clinical indications. Patients in which the LLM successfully analyzed the key metrics (1) coronary artery calcium (CAC) score and (2) coronary CTA reports (coronary stenosis severity (CAD-RADS), high-risk anatomy, non-calcified plaque, cardiac function (LVEF and others) were included. Results: 386 CTA reports were uploaded, and 346 (89.6%) included. The mean biological age (bioAGE) was 57.2 ± 10.9 and the chronological 58.5 ± 10.8 years. 137 (39.6%) were women. The intra-individual deviation in bioAGE was high (median: 8.8; IQR 9.98). BioAGE exceeded chronological age in 45.4% patient and was lower or equal in 54.6%) MACE rate was 8.7% comprising 2 deaths, 5 myocardial infarctions, and 22 late revascularizations. The accuracy for prediction of MACE was higher for bioAGE (c = 0.768; 95% CI: 0.681-0.855, p < 0.001) compared to chronological age (c = 0.590; 95% CI: 0.492-0.689, p = 0.102) Conclusions: Biological age calculation from coronary CTA reports using LLM is feasible, yet intra-individual deviations are high. The accuracy for prediction of MACE is improved by bioAGE compared to chronological.
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