P5014The NPAC score for predicting survival after incident acute myocardial infarction

Abstract

Abstract Background Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to predict short-term mortality after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort. Methods Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n=2127) and an independent tertiary care center (validation cohort, n=1276) with index acute myocardial infarction between January 2013 and December 2017 as confirmed by principal diagnosis and laboratory findings, were identified retrospectively. Results Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant predictors for 90-day mortality (Hosmer-Lemeshow test, P=0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic=0.63). Application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81. Multivariate binary logistic regression Variable β Adjusted Odds ratio (95% CI) P-value Points Age ≥65 years 1.304 3.68 (2.63–5.17) <0.001 2 Peripheral vascular disease 1.109 3.03 (1.52–6.04) 0.002 2 NLRt ≥9.51 1.100 2.73 (2.12–3.51) <0.001 1 Creatinine≥109 μmol/L 1.003 3.00 (2.35–3.85) <0.001 2 NPAC deep learning model Conclusions The NPAC score comprised of four items from routine laboratory parameters and basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gate-keeper to provide more accurate prediction to facilitate clinical decision making.