AIM: This study aimed to investigate hemogram parameters and C-reactive protein (CRP) that can be used in clinical practice to predict mortality in hospitalized patients with a diagnosis of COVID-19. METHODS: This cohort study was conducted at University Hospital, which is a designated hospital for COVID-19 patients. Adult patients who were admitted to our hospital emergency department with suspected COVID-19 and who were hospitalized in our institution with a COVID-19 diagnosis were analysed. RESULTS: There were 148 patients hospitalized with COVID-19. All-cause mortality of follow-up was 12.8%. There were statistically significant results between the two groups (survivors and nonsurvivors), which were classified based on hospital mortality rates, in terms of the lymphocyte to C-reactive protein ratio (LCRP), systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), CRP concentration and comorbid disease. In a receiver operating characteristic (ROC), curve analysis, LCRP, NLR, PLR and SII area under the curve (AUC) for in-hospital mortality were 0.817, 0.816, 0.733 and 0.742, respectively. Based on an LCRP value of 1 for in-hospital mortality, the sensitivity and specificity rates were 100% and 86.8%, respectively. Based on the average SII of 2699 for in-hospital mortality, the sensitivity, specificity and accuracy rates were 68.4%, 77.5% and 76.3%, respectively. A total of 19 patients died during hospitalization. All of these patients had an LCRP level &lt;= 1;14 had an NLR level &lt;= 10.8;13 had an SII &gt;= 2699 (Fisher's exact test, P = .000). Independent predictors of in-hospital mortality rates were LCRP &lt; 1, PLR, SII &gt;= 2699, white blood cell count, CRP, age, comorbidities, and ICU stay. CONCLUSIONS: We concluded that inflammatory parameters, such as LRCP, SII and NLR, were associated with disease severity and could be used as potentially important risk factors for COVID-19 progression.
The role of hemogram parameters and C-reactive protein in predicting mortality in COVID-19 infection