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Updated: Nov 5, 2025
Murine Myocardial Infarction Model using Permanent Ligation of Left Anterior Descending Coronary Artery
Published on: August 16, 2019
Development and validation of a model for predicting 18-month mortality in type 2 myocardial infarction
Truong H Hoang1, Victor V Maiskov2, Imad A Merai2
1Department of Internal Diseases with the Course of Cardiology and Functional Diagnostics named after V.S. Moiseev, Institute of Medicine, RUDN University, Moscow, Russia.
Background:
Despite the poor prognosis in patients with type 2 myocardial infarction (MI), no prospective data on risk stratification exists. The aim of this study was to develop and validate a model for prediction of 18-month mortality of among patients with type 2 MI (T2MI) and compare its performance with GRACE and TARRACO scores.
Methods:
The prospective observational study included 712 consecutive patients diagnosed with MI undergoing coronary angiography <24 h between January 2017 and December 2018. Diagnosis of T2MI was adjusted according to Third universal definition. A prognostic model was developed by using Bayesian approach and logistic regression analysis with identifying predictors for mortality. The model was validated by bootstrap validation. Comparison performance between scores using Delong test.
Results:
T2MI was identified in 174 (24.4%) patients. The median age of patients was 69 years, 52% were female. The mortality rate was 20.1% at 18 months. Prior MI, presence of ST elevation, hemoglobin level at admission, Charlson comorbidity index and were independently associated with 18-month mortality. The model to predict 18-month mortality showed excellent discrimination (optimism corrected c-statistic = 0.822) and calibration (corrected slope = 0.893). GRACE and TARRACO scores had moderate discrimination [c-statistic = 0.748 (95% CI 0.652-0.843) and 0.741, 95% CI 0.669-0.805), respectively] and inferior compared with model (p = 0.043 and 0.037, respectively).
Conclusions:
The risk of mortality among T2MI patients could be accurately predicted by using common clinical characteristics and laboratory tests. Further studies are required with external validation of nomogram prior to clinical implementation.
