Predicting mortality in COVID-19: Comparison of novel score with apache IV

ABSTRACT

INTRODUCTION: The novel coronavirus disease (COVID-19), is an infectious disease caused by the newly discovered virus (SARS-CoV-2). A few COVID-19 patients can have severe disease that is life-threatening and require ICU admission. The objectives of this study are to evaluate the performance of a novel scoring system in predicting mortality in COVID-19 patients admitted to the ICU, and to compare outcome prediction to APACHE IV. METHODS: A novel scoring system (COVID-19 severity score, CSS) is calculated from neutrophil/lymphocyte ratio (NLR), CRP, Ferritin levels (F), and D-dimer levels (D) as follows CSS = (NLR X CRP X F X D)/10,000. All data ware retrospectively collected from electronic health records of COVID-19 patients on day 1 of admission to our ICU between March 1st and May 30th, 2020. Student t test was used to compare means. The C statistic was calculated as a measure of the overall strength of prediction for both CSS and APACHE IV. Receiver-operating characteristic (ROC) curves were used to assess the mortality predictions. RESULTS: A total of 40 patients with COVID-19 were included, with 27 survivors (S) and 13 nonsurvivors (NS);ICU mortality rate of 32.5 %. Compared to S, NS were older (68 ± 19 vs 61 ± 14, p = 0.07), had higher APACHE IV scores (75 ± 37 vs 56 ± 28, p = 0.01), higher NLR (8.8 ± 8.6 vs 3.8 ± 2.2, p = 0.0006), lower CRP (75 ± 37 vs 99 ± 61, p = 0.04), similar F (3300 ± 5200 vs 2500 ± 2100, p = 0.4), similar D (2.8 ± 1.2 vs 2.7 ± 1.3, p = 0.7), and higher CSS (520 ± 1000 vs 147 ± 120, p = 0.03). Estimating the ROC area under the curve (AUC) showed that CSS was a significantly better predictor of hospital mortality compared to APACHE IV (AUC = 0.75 ± 0.05 for CSS compared to 0.70 ± 0.05 for APACHE IV (p < 0.0001). CONCLUSIONS: CSS was a strong predictor of mortality in patients with COVID-19 admitted to ICU. These findings should be further investigated in large multicenter prospective studies.