Since December 2019, the world has been facing an unprecedented coronavirus outbreak that started in China and rapidly spread internationally. Although outbreaks of the bird flu (H5N1), severe acute respiratory syndrome (SARS), swine flu (H1N1), Middle East respiratory syndrome (MERS), Ebola virus, and Zika virus have occurred in recent history, the most similar pandemic to the Covid-19 outbreak in terms of the number of people affected was the Spanish flu, which occurred about a century ago (1-4). The Spanish flu is thought to have been responsible for 17-50 million deaths (5) . According to the World Health Organization's (WHO) most recent report dated 17 August 2020, 21,294,845 people have been infected by Covid-19 and 761,779 have died (6) .
Treatment guidelines have been constantly updated in the past few months. It is clear that better disease management and treatment algorithms for pandemics are needed, given that there is not yet an effective treatment or vaccine for this virus and the outbreak has not been fully controlled. More
This prospective observational study was carried out in the emergency department (ED) of a tertiary care teaching hospital between 16 April 2020 and 16 June 2020 following approval by the local ethical committee.
Patients older than 18 years who met the criteria for moderate to severe Covid-19 were consecutively included in the study. Moderate to severe Covid-19 was defined according to the
Ministry of Health. It is recommended that these patients be hospitalized, and patients who do not meet these criteria should undergo home isolation (7). This definition included patients with symptoms of fever, muscle ache, cough, dyspnea, sore throat or nasal congestion, and at least one of the following: O 2 saturation below 93% in room air; tachypnea (>22/min); poor prognostic criteria in blood tests (blood lymphocyte count <800/μl, D-Dimer >1000 ng/ml, ferritin>500ng/ml, or C-reactive protein [CRP] >40 mg/L); bilateral diffuse parenchymal infiltration on chest x-ray or computed tomography; age of >50 years; and presence of comorbid diseases (diabetes mellitus, hypertension, malignancy, chronic lung disease, immunodeficiency disorders). Finally, at least one Covid-19 polymerase chain reaction test result was required to be positive. Patients with all negative results were excluded from the study.
The baseline characteristics of the included patients-age, gender, complaint at the time of admission, comorbid diseases, angiotensin-converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARBs), prophylactic hydroxychloroquine use, smoking, clinical features (such as vital signs), laboratory test results (white blood cell count, neutrophil count, lymphocyte count, CRP level, paO 2 level), and fiO 2 level-were recorded on a form created by a research assistant. The J o u r n a l P r e -p r o o f patients were followed up to determine the treatment they received, the duration of treatment, and the outcome.
In this study, we aimed to determine the factors that affect mortality in moderate to severe Covid-19 patients. The primary outcome was 30-day mortality rate. The secondary outcome was determination of the factors that affect the development of acute respiratory distress syndrome (ARDS). A receiver operating characteristic (ROC) curve was drawn for the laboratory values that were statistically associated with mortality, and the value with the highest sum of sensitivity and specificity was accepted as the cut-off value. Positive and negative likelihood ratios were determined according to this cut-off value. To determine the predictive value of several variables for both mortality and ARDS development, a multivariate regression model was created using variables whose p values were <0.2 in univariate analyses. After performing Spearman's test, the neutrophil count was excluded from the model because it was highly correlated with white blood cell count. When the patients were grouped into smokers and non-smokers, both groups were found to be similar in terms of demographic and clinical characteristics (supplemental file 1).
A two-tailed p value of <0.05 was considered statistically significant.
In total, 557 possible Covid-19 patients who agreed to participate were included in the study, and the data of 168 patients who remained after applying the exclusion criteria were subjected to statistical analysis ( Figure 1 ). The median age of the patients was 59.5 (48.3 to 76), and 90 (53.6%) were male.
The baseline characteristics and clinical features of the patients are presented in Table 1 .
Patients were divided into two groups according to the mortality outcome. In the univariate analysis, the following parameters were found to be statistically associated with mortality: age, dyspnea, coronary artery disease, congestive heart failure, active malignancy, chronic kidney failure, presence of any comorbid disease, loss of taste, respiratory rate, O 2 saturation, white blood cell count, neutrophil count, CRP, fiO 2 , and paO2/fiO 2 value. According to the results of multivariate regression analysis using variables that were statistically significant in univariate analysis (age, dyspnea, presence of any comorbid disease, O 2 saturation, white blood cell count, CRP level, and presence of nauseavomiting), it was determined that the presence of any comorbid disease, elevated CRP levels, and presence of dyspnea were found to be significantly associated with increased mortality, while high O2 saturation level was associated with decreased mortality. ( Table 2 ). The goodness of fit of the model was checked with the Hosmer-Lemeshow test (p = 0.730).
Univariate analysis was performed to test whether the same variables predict ARDS development.
The results indicate that the following parameters had a statistically significant relationship with ARDS development: older age, dyspnea, presence of any comorbid disease, congestive heart failure, chronic obstructive pulmonary disease, chronic kidney failure, ACEI or ARB use, increased respiratory rate, low O 2 saturation, elevated white blood cell counts, elevated neutrophil, elevated CRP, decreased paO 2 , increased fiO 2 , and decreased paO2/fiO 2 ratio. When multivariate regression analysis was performed to determine whether the variables predict ARDS development, high O 2 saturation level and low heart rates were found to be associated with decreased ARDS development.
The goodness of fit of the logistic regression model was determined by Hosmer-Lemeshow test (p=0.951). The results of the univariate and multivariate analyses are given in Table 3 .
As researchers are learning about Covid-19 in real time during its outbreak and knowledge about the disease is continuously slightly updated, determining the variables that predict the prognosis of the disease in different locations is crucial for the development of disease management guidelines.
In this way, policymakers can determine how patients should be triaged in EDs, which patients should be hospitalized, and which ones should be treated on an outpatient basis.
In our study, we found that dyspnea, presence of any comorbid disease, CRP level, and pulse O 2 saturation predict mortality in moderate to severely ill Covid-19 patients. Importantly, the presence of any comorbid disease and dyspnea are associated with increased mortality rate. Indeed, it is wellknown that severe illness is more common in patients with comorbidities (5). In a study conducted in Italy, older age, low lymphocyte count, cancer, coronary artery disease, and high radiographic assessment of lung edema score were found to be independent factors associated with increased risk of mortality (8) . According to a multiple machine learning-based classification algorithm developed by Yadaw et al., five variables were suggested as prognostic predictors: age, hydroxychloroquine use, O 2 saturation, body temperature, and type of patient (9) . In a large retrospective study of 44,000 cases in China, age and presence of comorbid diseases were found to be the two main determinants of mortality in Covid-19 cases. Considering the development of ARDS, which was the secondary outcome of our study, we found that only low oxygen saturation levels and increased heart rate increased the risk of ARDS development.
In our study, some results agreed with the literature and some did not. The most striking result is that increased CRP level had the highest AUC value, indicating that it is associated with increased mortality. In a meta-analysis by Huang et al, elevated CRP levels with elevated procalcitonin, D-dimer, and serum ferritin levels were associated with a composite poor outcome for Covid-19. When the cut-off value for CRP was ≥10 mg/L, AUC J o u r n a l P r e -p r o o f was calculated as 0.84, sensitivity was 51%, and specificity was 88% (10) . While these AUC values were similar to the results of our study, the sensitivity we found was 93.8%, higher at the same cut-off level.
Importantly, age was not found to have an effect on mortality according to the multivariate analysis, although it seems to be associated with increased mortality according to the results of the univariate analysis. This could be explained by the exclusion of mild Covid-19 patients, who are likely to have a lower median age, which led to a median age of survivors of 62 years. As expected in other studies involving these excluded patients, older age appears to increase mortality (11, 12) . In addition to the studies in the literature that indicate that age increased mortality, we believe that older age is associated with poor clinical outcomes in Covid-19 patients, considering that it increased the development of ARDS according to the results of our analysis.
In our study, we found that smoking was not associated with mortality or ARDS development.
In studies that consider many chronic diseases, especially lung diseases caused by smoking, many authors indicate that smoking has a negative effect on the clinical outcomes of Covid-19 patients.
However, the results of the literature are contradictory enough to prevent us from making a conclusion regarding this. Some studies report that smoking leads to an increased risk of adverse clinical outcomes in Covid-19 patients, and others state that tobacco smoking may facilitate transmission of the virus to other people through smoke (9,13). However, in some studies with much larger case numbers, smoking has not been associated with poor clinical outcomes in Covid-19 patients, and confusingly, some have reported that it may have a protective effect. A meta-analysis that included five studies found that active smoking was not associated with the severity of Covid-19 when the data of individual studies were pooled (OR, 1.69; 95% CI, 0.41-6.92; p = 0.254) (14). A study from Mexico analyzed 32,583 patient results and found that smoking was not a risk factor for Covid-19, but in fact was protective against Covid-19, with an adjusted odds ratio of 0.63 (95% CI, 0.51-0.77) (15).
Although some authors have tried to explain the -protective‖ effect of smoking as a result of various J o u r n a l P r e -p r o o f mechanisms, such as ACE2 allelic variants and protease activation, we believe that this interpretation may cause erroneous inferences due to the limited number of studies available today (16). New and higher-quality studies on this topic are needed
The fact that the study was conducted at one center is a major limitation of the study that limits the generalizability of the results. The relatively low number of patients may have caused small but significant differences between groups to remain undetected. Validation of our results is needed with larger sample sizes.
Dyspnea, the presence of any comorbid disease, elevated CRP levels, and low pulse O 2 saturation levels predict mortality in moderate to severely ill Covid-19 patients. In addition, low oxygen saturations and increased heart rate predict ARDS development. These criteria should be included in Covid-19 disease scoring systems developed in the future, and patients that meet these criteria should be hospitalized and treated closely. 