Key result
Machine learning models adding 10-year registry data improved major cardiovascular events prediction over demographics plus stress CMR.
Why the study?
Risk stratification in suspected CAD relies on clinical history and imaging, and whether machine learning models using 10-year historical registry data improve MACE prediction beyond demographics and stress perfusion CMR was unknown.
Does a machine learning model integrating 10-year historical registry data improve prediction of major adverse cardiovascular events beyond demographics and stress perfusion CMR in patients with suspected coronary artery disease?
Cohort (n=2,159)
Does a machine learning model integrating 10-year historical registry data improve prediction of major adverse cardiovascular events beyond demographics and stress perfusion CMR in patients with suspected coronary artery disease?
Answers are grounded in this paper and cite their sources.
Machine learning models incorporating 10-year longitudinal registry medical history modestly improve MACE risk prediction beyond demographics and stress perfusion CMR in patients with suspected CAD.
BACKGROUND: Risk stratification in suspected coronary artery disease (CAD) relies on clinical history and imaging, but longitudinal medical history may provide additional prognostic information. OBJECTIVES: This study evaluated whether machine learning models using 10-year historical registry data improves prediction of major adverse cardiovascular events (MACE) beyond demographics and stress perfusion cardiovascular magnetic resonance (CMR) in patients with suspected CAD. METHODS: A total of 2,159 patients (65 ± 12 years, 44% women) undergoing stress perfusion CMR were included. Models used demographics, CMR findings, and registry diagnoses and procedures from the prior 10 years. MACE was defined as cardiovascular death, myocardial infarction, or coronary intervention. Random Forest models were used for binary prediction, and Random Survival Forests (RSF) and penalized Ridge Cox regression for time-to-event analysis. RESULTS: During 2.7 ± 0.9 years of follow-up, 212 patients (9.8%) experienced MACE. For binary prediction, the area under the receiver-operating characteristic curve (AUROC) was 0.66 (95% CI: 0.62-0.69) using demographics and 0.72 (95% CI: 0.68-0.75; P = 0.001) after adding historical registry data. The full model achieved an AUROC of 0.82 (95% CI: 0.79-0.85) and outperformed the demographics-plus-CMR model (AUROC: 0.81; 95% CI: 0.78-0.84; ΔAUROC: 0.014; 95% CI: 0.002-0.027; P = 0.029). In time-to-event analysis, RSF and Cox regression performed similarly using demographics and CMR (concordance index [C-index] 0.80 for both; P = 0.546). After including registry data RSF achieved a C-index of 0.81 (P = 0.144), and Cox performance decreased to C-index 0.77 (P < 0.001). CONCLUSIONS: In patients with suspected CAD, machine learning models using 10-year historical registry data have the potential to improve prognostic risk prediction beyond demographics and stress perfusion CMR.
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Permlid et al. (2026) conducted a cohort in Suspected coronary artery disease (n=2,159). Machine learning model incorporating 10-year historical registry data, demographics, and stress perfusion CMR vs. Model using demographics and stress perfusion CMR alone was evaluated on Major adverse cardiovascular events (cardiovascular death, myocardial infarction, or coronary intervention) (ΔAUROC 0.014, 95% CI 0.002-0.027, p=0.029). Machine learning models adding 10-year registry data improved major cardiovascular events prediction over demographics plus stress CMR.
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