Machine Learning Reveals Prognostic Value of Medical History in Suspected Coronary Artery Disease.
Authors
Affiliations (2)
Affiliations (2)
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
- Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden. Electronic address: [email protected].
Abstract
Risk stratification in suspected coronary artery disease (CAD) relies on clinical history and imaging, but longitudinal medical history may provide additional prognostic information. 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. 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. 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). 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.