A random forest classifier predicted in-hospital mortality in ST-elevation AMI with an AUC of 0.95, sensitivity of 0.89, and specificity of 0.91, outperforming common scoring methods.
Observational (n=2,553)
Does a machine learning model using integrated biological markers accurately predict in-hospital mortality in patients with ST-elevation AMI compared to common scoring methods?
A machine learning model using 10 routine clinical and biological markers demonstrated high accuracy (AUC 0.95) for predicting in-hospital mortality in patients with ST-elevation AMI, outperforming traditional scoring methods.
Effect estimate: AUC 0.95
Commonly used prediction methods for acute myocardial infarction (AMI) were created before contemporary percutaneous coronary intervention was recognized as the primary therapy. Although several studies have used machine learning techniques for prognostic prediction of patients with AMI, its clinical application has not been achieved. Here, we developed an online application tool using a machine learning model to predict in-hospital mortality in patients with AMI. A total of 2,553 cases of ST-elevation AMI were assigned to 80% training subset for cross validation and 20% test subset for model performance evaluation. We implemented random forest classifier for the binary classification of in-hospital mortality. The selected best feature set consisted of ten clinical and biological markers including max creatine phosphokinase, hemoglobin, heart rate, creatinine, systolic blood pressure, blood sugar, age, Killip class, white blood cells, and c-reactive protein. Our model achieved high performance: the area under the curve of the receiver operating characteristic curve for the test subset, 0.95: sensitivity, 0.89: specificity, 0.91: precision, 0.43: accuracy, 0.91 respectively, which outperformed common scoring methods. The freely available application tool for prognostic prediction can contribute to risk triage and decision-making in patient-centered modern clinical practice for AMI.
Nishi et al. (Thu,) conducted a observational in ST-elevation acute myocardial infarction (n=2,553). Machine learning prediction model (random forest classifier) vs. Common scoring methods was evaluated on In-hospital mortality (AUC 0.95). A random forest classifier predicted in-hospital mortality in ST-elevation AMI with an AUC of 0.95, sensitivity of 0.89, and specificity of 0.91, outperforming common scoring methods.