Abstract
BACKGROUND: Coronavirus disease (COVID-19) may lead to serious complications and increased mortality. The outcomes of patients who survive the early disease period are burdened with persistent long-term symptoms and increased long-term morbidity and mortality. The aim of our study was to determine which baseline parameters may provide the best prediction of early and long-term outcomes. METHODS: The study group comprised 141 patients hospitalized for COVID-19. Demographic data, clinical data and laboratory parameters were collected. The main study endpoints were defined as in-hospital mortality and 1-year mortality. The associations between the baseline data and the study endpoints were evaluated. Prediction models were created. RESULTS: The in-hospital mortality rate was 20.5% (n = 29). Compared with survivors, nonsurvivors were significantly older (p = 0.001) and presented comorbidities, including diabetes (0.027) and atrial fibrillation (p = 0.006). Assessment of baseline laboratory markers and time to early death revealed negative correlations between time to early death and higher IL-6 levels (p = 0.032; Spearman rho - 0.398) and lower lymphocyte counts (p = 0.018; Pearson r -0.438). The one-year mortality rate was 35.5% (n = 50). The 1-year nonsurvivor subgroup was older (p < 0.001) and had more patients with arterial hypertension (p = 0.009), diabetes (p = 0.023), atrial fibrillation (p = 0.046) and active malignancy (p = 0.024) than did the survivor subgroup. The model composed of diabetes and atrial fibrillation and IL-6 with lymphocyte count revealed the highest value for 1-year mortality risk prediction. CONCLUSIONS: Diabetes and atrial fibrillation, as clinical factors, and LDH, IL-6 and lymphocyte count, as laboratory determinants, are the best predictors of COVID-19 mortality risk.