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, 14 (10), e0224502

Deep-learning-based Risk Stratification for Mortality of Patients With Acute Myocardial Infarction


Deep-learning-based Risk Stratification for Mortality of Patients With Acute Myocardial Infarction

Joon-Myoung Kwon et al. PLoS One.


Objective: Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI).

Methods: The data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, and the thrombolysis in myocardial infarction (TIMI) score using the validation data.

Results: In-hospital mortality for the study subjects was 4.4% and 6-month mortality after survival upon discharge was 2.2%. The areas under the receiver operating characteristic curves (AUCs) of the DAMI were 0.905 [95% confidence interval 0.902-0.909] and 0.870 [0.865-0.876] for the ST elevation myocardial infarction (STEMI) and non ST elevation myocardial infarction (NSTEMI) patients, respectively; these results significantly outperformed those of the GRACE (0.851 [0.846-0.856], 0.810 [0.803-0.819]), ACTION (0.852 [0.847-0.857], 0.806 [0.799-0.814] and TIMI score (0.781 [0.775-0.787], 0.593[0.585-0.603]). DAMI predicted 30.9% of patients more accurately than the GRACE score. As secondary outcome, during the 6-month follow-up, the high risk group, defined by the DAMI, has a significantly higher mortality rate than the low risk group (17.1% vs. 0.5%, p < 0.001).

Conclusions: The DAMI predicted in-hospital mortality and 12-month mortality of AMI patients more accurately than the existing risk scores and other machine-learning methods.

Conflict of interest statement

The authors have declared that no competing interests exist.


Fig 1
Fig 1. Study flow chart.
Fig 2
Fig 2. Deep-learning based model development and accuracy test.
AMI denotes acute myocardial infarction, CKMB creatinine kinase-MB, CVA cerebrovascular accident, DM diabetes mellitus, HTN hypertension, PMHx past medical history, ReLU rectified linear unit.
Fig 3
Fig 3. Receiver operating characteristic curve for predicting in-hospital mortality.
AUC denotes area under the receiver operating characteristic curve, CI confidence interval, GRACE global registry of acute coronary event, TIMI thrombolysis in myocardial infarction.
Fig 4
Fig 4. Reclassification of Individuals predicted to be at intermediate risk group by additional assessment of DAMI.
DAMI denotes deep-learning-based risk stratification for the mortality of patients with AMI and GRACE denotes global registry of acute coronary event.
Fig 5
Fig 5. Kaplan-Meier survival curve stratified by deep-learning model risk score group.

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Grant support

This research was supported by a fund (2013E2100200) by Research of Korea Centers for Disease Control and Prevention. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012-3110. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.