RESUMEN
BACKGROUND: Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk. METHODS: We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD). RESULTS: During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs. CONCLUSION: Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance.
Asunto(s)
Imagen de Perfusión Miocárdica , Humanos , Aprendizaje Automático , Imagen de Perfusión Miocárdica/métodos , Sistema de Registros , Tomografía Computarizada de Emisión de Fotón Único/métodosRESUMEN
Coronary artery disease (CAD) is the primary cause of death in adults in the United States. Only 50% of patients who present with a myocardial infarction have a prior history of CAD. Non-invasive cardiac imaging tests have been developed to diagnose CAD. Current guidelines and systematic reviews have tried to determine the prognostic value of the coronary artery calcium (CAC) scoring and the coronary computed tomography angiography (CCTA) for major adverse cardiovascular events. Several studies support the roles of CCTA and CAC scoring for the diagnosis of CAD in asymptomatic patients. Further studies are needed to confirm the superior role of CCTA over CAC scoring in symptomatic patients.