Prognostic value of GRACE risk score in patients hospitalized for coronavirus disease 2019

Abstract

Objective COVID-19 pandemic continues to threaten human health as novel mutant variants emerge and disease severity ranges from asymptomatic to fatal. Thus, studies are needed to identify the patients with ICU need as well as those who have subsequent mortality. Global Registry of Acute Coronary Events (GRACE) risk score is a validated score in acute coronary syndrome. We aimed to evaluate if GRACE score can indicate adverse outcomes and major ischemic events in hospitalized COVID-19 patients.Methods All hospitalized patients due to COVID-19 at our institution between March 2020 and September 2020 were included in this retrospective study. Patients were grouped according to GRACE risk scores: low risk 0-108 points, intermediate risk 109-140 and high risk ≥141.Results A total of 787 patients were enrolled; 434 patients formed group 1. One-hundred forty-one patients in group 2 and 212 patients formed group 3. We found that inhospital mortality, length of hospital stay, ICU and advanced ventilatory support need were associated with increasing GRACE risk score. In addition, major ischemic events were more frequently observed in higher risk groups and strong positive correlations between GRACE risk score and pro-BNP, procalcitonin and moderate positive correlation with D-dimer, CRP, NLR was found. Regression analysis showed that only GRACE risk score was an independent risk factor associated with inhospital mortality, major ischemic events, advanced ventilatory support and ICU need. ConclusionThe GRACE risk score is easy to apply on hospital admission and useful for classifying those in medium-high-intensity care units and to raise the assignments of sources.

Adaptability of prognostic prediction models for patients with acute coronary syndrome during the COVID-19 pandemic

“… 10 Furthermore, the GRACE risk score demonstrated versatility to predict in-hospital mortality, major ischaemic events and the necessity for advanced ventilatory support and intensive care unit admission even for patients without ACS hospitalised due to COVID-19. 11 A machine learning–based prediction model, KOTOMI, has shown the prediction capability for in-hospital mortality in patients with STEMI with enhanced precision compared with conventional methods. 12 However, there exists a knowledge gap regarding the durability and adaptability of these predictive models in accurately determining short-term mortality for patients with ACS, irrespective of the COVID-19 status, during the pandemic.…”