O-26 ASPARTATE AMINOTRANSFERASE, AGE AND D-DIMER IN COVID-19 PATIENTS: A USEFUL PROGNOSTIC MODEL

Abstract

Introduction

Some patients with SARSCov-2 infection develop severe disease (SARS); however, the factors associated with severity are not yet fully understood. Some reports indicate that liver injury may be a poor prognostic factor.

Aim

To identify the biochemical factors related to the development of SARS with mechanical ventilation (MV) requirement in patients with SARSCov-2 and COVID-19.

Methods Type of study

Observational. Cohort study.

Procedure

Data from COVID-19 patients were collected at admission time to a tertiary care center. Differential factors were identified between seriously ill SARS+MV patients versus stable patients without MV. Transformation to the natural logarithm of significant variables was performed and multiple linear regression was applied, then a predictive model of severity called AAD (Age-AST-D dimer) was constructed.

Results

166 patients were included, 114(68.7%) men, mean age 50.6±13.3 years-old, 27(16.3%) developed SARS+MV. In the comparative analysis between those with SARS+MV versus stable patients without MV we found significant raises of ALT (225.4±341.2 vs. 41.3±41.1; P=0.003), AST 325.3±382.4 vs. 52.8±47.1; P=0.001), LDH (764.6±401.9 vs. 461.0±185.6; P=0.001), D dimer (7765±9109 vs. 1871±4146; P=0.003), age (58.6±12.7 vs. 49.1±12.8; P=0-001). The results of the regression are shown in the Table, where model 3 was the one that best explained the development of SARS+MV; with these variables was constructed the model called AAD, where: [AAD= 3.896 + ln(age)x-0.218 + ln(AST)x-0.185 + ln(DD)x0.070], where a value ≤ 2.75 had sensitivity=0.797 and 1-specificity= 0.391, AUROC=0.74 (95%CI: 0.62-0.86; P<0.0001), to predict the risk of developing SARS+MV (OR=5.8, 95%CI: 2.2-15.4; P=0.001).

Conclusions

Elevation of AST (probable marker of liver damage) is an important predictor of progression to SARS, together with elevation of D-dimer and age early (at admission) and efficiently predict which patients will potentially require MV.