A Novel Predictive Model for In-Hospital Mortality Based on a Combination of Multiple Blood Variables in Patients...

Area of Science:

• Cardiology
• Clinical Risk Assessment
• Biomarkers

Background:

• Objective risk assessment is crucial in emergency settings for predicting patient outcomes.
• ST-segment elevation myocardial infarction (STEMI) requires rapid and accurate risk stratification for in-hospital mortality.

Purpose of the Study:

• To develop and validate an easy-to-measure, objective risk-score prediction model for in-hospital mortality in STEMI patients.
• To identify key laboratory parameters associated with in-hospital death in STEMI patients.

Main Methods:

• A total of 1027 STEMI patients were divided into derivation (n=669) and validation (n=358) cohorts.
• A risk-score model was created using 5 significant variables from blood tests: estimated glomerular filtration rate, platelet count, albumin, high-sensitivity troponin I, and blood sugar.
• Multivariate analysis and receiver operating characteristic (ROC) curve analysis were employed.

Main Results:

• Five variables (low eGFR, low platelet count, low albumin, high hs-TnI, high blood sugar) were significantly associated with in-hospital death.
• The developed laboratory-based risk score demonstrated adequate discrimination (AUC 0.853 in derivation, 0.879 in validation).
• The model's predictive performance was comparable to the Global Registry of Acute Coronary Events (GRACE) score.

Conclusions:

• A novel, laboratory-based risk score can objectively and accurately predict in-hospital mortality in STEMI patients.
• This model offers a practical tool for risk assessment in emergency clinical settings.
• The findings support the use of readily available blood parameters for improved STEMI patient management.