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The NPAC score for predicting survival after incident acute myocardial infarction

Session Challenges in contemporary management of coronary artery disease

Speaker Ka Hou Li

Congress : ESC Congress 2019

  • Topic : coronary artery disease, acute coronary syndromes, acute cardiac care
  • Sub-topic : Coronary Artery Disease - Epidemiology, Prognosis, Outcome
  • Session type : Moderated Posters
  • FP Number : P5014

Authors : K H Li (Newcastle Upon Tyne,GB), J Ho (Shatin,HK), Z Xu (Hong Kong,HK), I Lakhani (Shatin,HK), G Bazoukis (Athens,GR), T Liu (Tianjin,CN), W T Wong (Shatin,HK), S H Cheng (Shatin,HK), M T V Chan (Shatin,HK), T Gin (Shatin,HK), M C S Wong (Shatin,HK), I Wong (Hong Kong,HK), WKK Wu (Shatin,HK), Q Zhang (Shatin,HK), G Tse (Shatin,HK)

K H Li1 , J Ho2 , Z Xu3 , I Lakhani2 , G Bazoukis4 , T Liu5 , W T Wong2 , S H Cheng2 , M T V Chan2 , T Gin2 , M C S Wong2 , I Wong6 , WKK Wu2 , Q Zhang2 , G Tse2 , 1Newcastle University - Newcastle Upon Tyne - United Kingdom of Great Britain & Northern Ireland , 2The Chinese University of Hong Kong - Shatin - Hong Kong , 3The Chinese University of Hong Kong, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences - Hong Kong - Hong Kong , 4Evangelismos General Hospital of Athens, Cardiology - Athens - Greece , 52nd Hospital of Tianjin Medical University, Cardiology - Tianjin - China , 6The University of Hong Kong - Hong Kong - Hong Kong ,

On behalf: International Health Informatics Study Network


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.

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.



Adjusted Odds ratio (95% CI)



Age ≥65 years


3.68 (2.63-5.17)



Peripheral vascular disease


3.03 (1.52-6.04)



NLR t 9.51


2.73 (2.12-3.51)



Creatinine≥ 109 µmol/L


3.00 (2.35-3.85)



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