Development of a Laboratory Risk-Score Model to Predict One-Year Mortality in Acute Myocardial Infarction Survivors.

Area of Science:

• Cardiology
• Clinical Risk Prediction
• Biomarkers

Background:

• High post-discharge mortality in acute myocardial infarction (AMI) survivors necessitates improved risk stratification.
• Existing prediction tools may lack ease of use or rely on complex data.
• Need for accessible tools to identify AMI patients at risk for one-year mortality.

Purpose of the Study:

• To develop and validate a risk-prediction model for one-year mortality using pre-procedural blood tests in AMI survivors.
• To assess the association between specific laboratory parameters and post-discharge mortality.
• To determine the efficacy of a combined laboratory risk score in predicting mortality.

Main Methods:

• Development of a risk-score model using parameters from routine pre-procedural blood tests in a derivation cohort (n=949) of AMI patients.
• Validation of the model in an independent cohort (n=406) of AMI patients who underwent primary coronary revascularization.
• Multivariable analysis and receiver-operating characteristic (ROC) curve analysis to assess predictive accuracy.

Main Results:

• Hemoglobin < 11 g/dL, estimated glomerular filtration rate < 30 mL/min/1.73 m2, albumin < 3.8 mg/dL, and high-sensitivity troponin I > 2560 ng/L were significant predictors of one-year mortality.
• An increased risk score (0-4 points) was strongly associated with higher one-year mortality in both derivation and validation cohorts (p < 0.001).
• ROC analysis showed adequate discrimination (AUC 0.850 and 0.820 in derivation and validation cohorts, respectively).

Conclusions:

• A laboratory risk-score model based on routine blood tests effectively predicts one-year mortality in AMI survivors.
• This accessible tool can aid clinicians in identifying high-risk patients for targeted interventions.
• The model demonstrates good discriminatory performance in both derivation and validation cohorts.