The Age-AST-D Dimer (AAD) Regression Model Predicts Severe COVID-19 Disease

Area of Science:

• Infectious Diseases
• Hepatology
• Critical Care Medicine

Background:

• Coronavirus disease (COVID-19) presents a spectrum of illness severity, from mild to life-threatening.
• Early liver injury in COVID-19 patients may indicate a poorer prognosis.
• Identifying early predictive factors for severe COVID-19 is crucial for timely intervention.

Purpose of the Study:

• To identify early biochemical markers predicting severe COVID-19 requiring intensive care unit (ICU) admission.
• To develop and validate a predictive model for ICU admission risk in COVID-19 patients.

Main Methods:

• Retrospective analysis of biochemical data from COVID-19 patients at admission.
• Comparison of biochemical factors between ICU and non-ICU patients.
• Development and validation of the Age-AST-D dimer (AAD) predictive model using multiple linear regression.

Main Results:

• Patients requiring ICU admission exhibited significantly higher levels of liver injury markers (ALT, AST, LDH) and D-dimer, along with advanced age.
• The AAD model demonstrated good predictive performance in both derivation (c-statistic = 0.74) and validation (c-statistic = 0.80) cohorts for ICU admission risk.
• A cut-point of <2.75 for the AAD model predicted ICU admission with significant odds ratios in both cohorts.

Conclusions:

• Elevated aspartate aminotransferase (AST), D-dimer (DD), and patient age are efficient predictors of early ICU admission in COVID-19.
• The AAD model provides a valuable tool for early risk stratification and triage of COVID-19 patients.
• Implementing the AAD model can enhance the management of COVID-19 patients by identifying those at high risk for severe disease.