450-P: Development and Validation of a Predictive Major Adverse Cardiac Events (MACE) Risk Model for Diabetes Patients with Acute Coronary Syndrome (ACS)

Abstract

Background: Studies have shown that acute coronary syndrome (ACS) has a higher incidence of MACE in diabetic patients than in nondiabetic patients by 39%. The complexity of the MACE limits clinical predictions, the success rate is shallow. Several existing MACE risk prediction models, including some developed specifically for ACS patients, also stuck with low accuracy and are not suitable for complications scenarios. The objective of this study is to develop and validate a model based on EHR data to predict MACE risk in patients with type 2 diabetes mellitus (T2DM) complicated with ACS. Methods: This study recruited 16,807 in-hospital T2DM patients with ACS from 38 urban and rural hospitals. Patients with 41 variables, including age, demographics, living habits, ACS type, and Killip class. The criteria for the diagnosis of MACE developed for this study consider cardiovascular death, myocardial infarction, or ischemic stroke. There are 711 MACE records diagnosed by doctors during patients’ hospitalization and recorded in medical records, only 4.2% of the total cohort. Very low morbidity inspires us to introduce a model called Support Vector Data Description (SVDD) to combat this uneven distribution of data. SVDD only maps the features of MACE patients to the high-dimensional space and constructs a minimum sphere to contain all the target data, taking the sphere as the boundary to classify the risk of newly admitted patients. Models were developed and validated on 80% and 20% of the sample, respectively. The model performance was assessed by C-statistics and the F1. Results: The model performed with C-statistics of 0.873 and F1 of 0.833. We found 5 variables had a significant univariate association with MACE included Age, Heart rate, Killip class, white blood cell count and Creatinine at p<10-4. Conclusions: This novel study suggests that the well trained SVDD model would be possible to predict the harmful MACE among T2DM patients with ACS. Disclosure P. Hu: None. X. Di: None. Y. Zhang: None. Z. Tang: None. S. Li: None. J. Mei: None. C. Ma: None.