Clinical Characteristics and Laboratory Values Affecting Mortality in Critical Coronavirus Disease 2019 Patients Followed in the Intensive Care Unit
Conditions: Covid19 and Mortality
Sponsor: Saglik Bilimleri Universitesi Gazi Yasargil Training and Research Hospital

Factors Affecting Mortality in Critical Patients Admitted to Intensive Care Unit Due to 
Coronavirus Disease 2019 
NCT number: NCT04659876 
Date: 11.09.2020 
Study Design, Population, and Data 
Critical patients hospitalized due to COVID-19 in ICU of the University of Health 
Sciences Turkey, Diyarbakır Gazi Yaşargil Training and Research of Hospital between March 
22 and September 1, 2020, were included in this study. The study was approved by the Ethics 
Committee of University of Health Sciences Turkey, Diyarbakır Gazi Yaşargil Training and 
Research of Hospital (No: 550, 11.09.2020). The trial was registered with clinicaltrials.gov 
(NCT04659876). This retrospective cohort study was conducted in accordance with the 2008 
Declaration of Helsinki criteria. 
Critical patients diagnosed with COVID-19 on the dates specified, followed up in 
ICU, aged >18 years, in serious need of oxygen support according to WHO and the temporary 
guidelines of T.C. Science Board of the Ministry of Health [presence of fever, muscle/joint 
pain, cough, and sore throat; tachypnea (30 breaths/min) or dyspnea; use of extra respiratory 
muscles; SpO2 level below of ≤90% in room air; bilateral diffuse pneumonia symptom 
detected on chest radiography or computerized tomography (CT); and PaO2/ FiO2 ratio 
of<300], and developed or had complications including severe pneumonia, ARDS, 
sepsis/septic shock, and acute renal failure were included in the study. Patients with COVID-
19 aged <18 years with mild-to-moderate symptoms, no respiratory distress, and no signs of 
diffuse pneumonia on chest X-ray or CT as well as ICU patients excepted from COVID-19 
diagnosis were excluded from the study. In addition, patients whose complete data could not 
be accessed from the hospital system or the patient file records were excluded. When the 
patients were admitted to ICU for the first time, their clinical conditions were evaluated with 
APACHE II and SOFA scores, and the degree of renal failure was evaluated using the Kidney 
Disease: Improving Global Outcomes (KDIGO) classification. Age; sex; comorbidity; ABO 
and Rh blood groups; APACHE II and SOFA scores and KDIGO stage during admission to 
ICU; hemogram parameters [white blood cell (WBC), neutrophil, lymphocyte, 
neutrophil/lymphocyte (N/L) ratio, hemoglobin, hematocrit, and platelet count]; blood gas 
values [pH, partial oxygen pressure (PO2), partial carbon dioxide pressure (PCO2), 
bicarbonate (HCO3), and lactate]; coagulation parameters [prothrombin time (PTZ) and D-
dimer]; blood biochemistry results [creatine kinase (CK), lactate dehydrogenase (LDH), C-

reactive protein (CRP), urea, creatinine, alanine aminotransferase (ALT), aspartate 
aminotransferase (AST), total bilirubin, direct bilirubin, and indirect bilirubin]; and 
procalcitonin (PCT) and ferritin levels of the patients were recorded. Moreover, the length of 
stay in ICU and whether the patient died or survived were recorded. Patient data were 
rechecked for erroneous information before the last data entry and entered into a 
computerized database. 
Patients were divided into two groups—those who survived (Survivors) and those who 
died (Non-survivors) during ICU follow-up. Both groups were compared in terms of clinical 
characteristics; APACHE II and SOFA scores and KDIGO stage; and laboratory values at the 
first admission to ICU. We attempted to determine the factors that affect mortality in critically 
ill patients hospitalized in ICU with COVID-19 diagnosis. 
Statistical Analysis 
SPSS 16.0 software for Windows (SPSS Inc., Chicago, IL, USA) was used for 
statistical analysis. Continuous data were expressed as means (SD or minimum–maximum), 
and categorical data were expressed as frequencies with percentages. Comparison of 
categorical data in the groups was performed using chi-square and Fisher’s exact test, and the 
results were presented as n%. Kolmogorov–Smirnov test was used to determine whether the 
numerical data fit the normality distribution. Data conforming to the normality distribution 
were evaluated using Student t-test, and Mann–Whitney U and Kruskal–Wallis tests were 
employed to compare data that did not fit the normality distribution. Binary logistic regression 
was performed for the risk factors that were found to be significant in the univariate analysis. 
Odds ratio (OR) with 95% confidence interval (CI) was used to report the association between 
mortality and exposure to the risk factors. In all comparisons, P value of <0.05 was 
considered significant.