Archives of Academic Emergency Medicine. 2022; 10(1): e49 OR I G I N A L RE S E A RC H Clinical and Laboratory Predictors of COVID-19-Related In-hospital Mortality; a Cross-sectional Study of 1000 Cases Zohreh Mohammadi1, Masood Faghih Dinevari2, Nafiseh Vahed3, Haniyeh Ebrahimi Bakhtavar1, Farzad Rahmani4∗ 1. Emergency and Trauma Care Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. 2. Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. 3. Research Center for Evidence-Based Medicine, Tabriz University of Medical Sciences, Tabriz, Iran. 4. Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, IR Iran. Received: April 2022; Accepted: May 2022; Published online: 23 June 2022 Abstract: Introduction: Identifying patients at risk for mortality and using appropriate treatment for each patient based on their situation could be an effective strategy in improving their outcome. This study aimed to evaluated the predictors of COVID-19 in-hospital mortality. Methods: This descriptive cross-sectional study was con- ducted on all adult COVID-19 patients who were managed in Imam-Reza and Sina Hospitals, Tabriz, Iran, from November 2020 until December 2021. The demographic, clinical, and laboratory characteristics of patients were evaluated and predictors of in-hospital mortality were identified using logistic regression model. Results: 1000 patients with the mean age of 56.34 ± 18.00 years were studied (65.7% male). There were significant associa- tions between COVID-19 in-hospital mortality and hospitalization above five days (p = 0.001), white blood cell count (WBC) > 4000 Cells*103/mL (p < 0.01), aspartate aminotransferase (AST) above 40 IU/L (p = 0.001), alanine transaminase (ALT) above 40 IU/L (p = 0.001), creatinine above 1.4 mg/dL (p = 0.007), urea above 100 mg/dL (p = 0.024), and SaO2 below 80% (p = 0.001). Hospital stay above five days (OR: 3.473; 95%CI: 1.272 - 9.479; p = 0.15), AST above 40 IU/L (OR: 0.269, 95%CI: 0.179 - 0.402; p = 0.001), creatinine above 1.4 mg/dL (OR: 0.529; 95%CI: 0.344 - 0.813; p = 0.004), urea above 100 mg/dL (OR: 0.327, 95%CI: 0.189 - 0.567; p = 0.001), and SaO2 below 80% (OR: 8.754, 95%CI: 5.413 - 14.156; p = 0.001) were among the independent predictors of COVID-19 in-hospital mortality. Conclusion: The mortality rate of patients with COVID-19 in our study was 29.9%. Hospitalization of more than five days, AST above 40 IU/L, creatinine above 1.4 mg/dL, urea above 100 mg/dL and SaO2 < 80% were independent risk factors of in-hospital mortality among patients with COVID-19. Keywords: COVID-19; Mortality; Prognosis; Respiratory Distress Syndrome Cite this article as: Mohammadi Z, Faghih Dinevari MF, Vahed N, Ebrahimi Bakhtavar H, Rahmani F. Clinical and Laboratory Predic- tors of COVID-19-Related In-hospital Mortality; a Cross-sectional Study of 1000 Cases. Arch Acad Emerg Med. 2022; 10(1): e49. https://doi.org/10.22037/aaem.v10i1.1574. 1. Introduction In December 2019, patients were diagnosed with pneumonia of unknown origin, later known as SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2), in Wuhan, China (1, 2). The clinical manifestation of SARS-CoV-2 infection is ∗Corresponding Author: Farzad Rahmani; Emam Reza Medical Research and Training Hospital, Tabriz University of Medical Sciences, Tabriz, Iran. Tel: 00984133352078, Fax: 00984133352078, Email: Rahmanif@tbzmed.ac.ir, OR- CID: http://orcid.org/0000-0001-5582-9156. mutable and includes asymptomatic disease, upper respira- tory tract disorders, and in some cases, acute and severe fa- tal conditions. Therefore, to summarize the clinical manifes- tations and widespread consequences of SARS-CoV-2 infec- tions, the WHO chose the specific name COVID-19 (Coron- avirus disease 2019) for this disease (3-5). The mortality rate is the most crucial factor in turning an in- fection into a public concern and the risk of developing a pandemic. Different viruses become epidemics each year, but very few of them become a public concern (6-8). Swine influenza A (H1N1 virus), severe acute respiratory syndrome This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Z. Mohammadi et al. 2 (SARS), and Ebola (Zaire ebolavirus) have led to global con- cern in recent years due to high mortality (9, 10). As in the above cases, COVID-19 has received all the attention and has caused concern due to its high mortality rate (3, 11). For ex- ample, although the flu is widespread, its mortality rate is only 0.1%. Reports have also shown that COVID-19 is highly contagious and can spread via various routes (3, 5, 12). The rate of infectivity is a significant factor, but the mortal- ity rate from COVID-19 is not correctly estimated. Because when the initial mortality rate is reported, only patients with very severe condition are in the statistical population, and patients with mild to moderate disease are not included in the investigation (12). The rapid development of COVID-19 in Wuhan, China, has resulted in thousands of deaths (13), and the widespread virus worldwide has resulted in hun- dreds of thousands of patients (14). In Iran, WHO reports that there were about 10000 new cases of COVID-19 per day dur- ing the study, and death rate was about 400 cases daily (15). More deaths were observed in patients with severe disease, and other patients in whom the disease symptoms were flu- like, improved quickly, and returned to everyday life (5). In addition, the difference between the clinical features of pa- tients with severe and non-severe diseases has been rarely re- ported (16, 17). Also, in some studies, the clinical features of patients with severe diseases who died were compared with patients who survived after the infection. We hypothesized that assessing routine parameters such as vital signs and lab- oratory tests in COVID-19 patients, especially patients with severe disease, can help medical staff better manage patients. Therefore, this study aimed to design a predictive model of mortality in patients admitted with COVID-19, to identify pa- tients with different conditions and use appropriate treat- ment for each patient based on their situation. 2. Methods 2.1. Study design and setting This descriptive cross-sectional study was conducted with the approval of the institutional ethics committee at Tabriz University of Medical Sciences (IR.TBZMED.REC.1399.950) in two Medical Research, Training and Treatment General Hospitals, Imam-Reza and Sina Hospitals, Tabriz, Iran, from November 2020 until December 2021. The data of all adult COVID-19 patients admitted in the mentioned hospitals dur- ing the study period were evaluated and predictors of in- hospital mortality were determined using logistic regression model. 2.2. Participants The study included patients older than 18 years, with COVID-19 pneumonia, confirmed by reverse transcriptase- polymerase chain reaction (RT-PCR) for SARS-CoV-2. The Figure 1: Study flow diagram of patients’ enrolment. sampling method was a complete census. The minimum number of samples is 1000 patients. The sample size was estimated based on the COVID-19 prevalence of 33% (18), a confidence interval of 95%, and a relative estimation er- ror of 10%. Exclusion criteria were incomplete information recorded in the patient’s medical record, discharge against medical advice, leaving the study in the middle of the study procedure, not willing to participate in the project, and neg- ative PCR test (figure 1). 2.3. Data gathering Patients’ demographic characteristics at the time of admis- sion (age, sex, body mass index (BMI)), underlying disease, drug history, vital signs (blood pressure, heart rate (HR), res- piration rate (RR), body temperature, O2 Saturation, AVPU level of consciousness), need for supplemental oxygen (via nasal cannula or mask), lung involvement on computed to- mography (CT) scan, and the laboratory test results were recorded in the checklist. Laboratory findings included complete blood count (white blood cell (WBC), Neutrophil, Lymphocyte, Hemoglobin and Platelet counts), Liver func- tional enzymes (including Aspartate transaminase, Alanine transaminase, and Alkaline phosphatase), Creatinine, Urea, Coagulation status (including Prothrombin Time (PT), Par- tial Thromboplastin Time (PTT), and International Normal- ized Ratio (INR)), Venous blood gas analysis (including pH, PaCO2, and HCO3), and serum sodium (Na) and potassium (K) status. The patients were followed up during hospital- ization (short-term follow-up of 30 days), and the duration This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2022; 10(1): e49 Figure 2: Area under the receiver operating characteristic (ROC) curve of hospital stay above five days (P value: 0.435), aspartate aminotrans- ferase (AST) above 40 IU/L (P value: 0.001), creatinine above 1.4 mg/dL (P value: 0.001), urea above 100 mg/dL (P value: 0.001), and SaO2 below 80% (P value: 0.001) in predicting the in-hospital mortality of COVID-19 patients. of hospitalization, intubation, intensive care unit (ICU) hos- pitalization, and outcome, including death or survival, were assessed. 2.4. Outcome The study’s primary outcome was patient mortality during the hospitalization period or within 30 days from admission. 2.5. Statistical analysis All data were entered into the SPSS 21. Normal data distri- bution was assessed using Kolmogorov-Smirnov test. For the descriptive data, mean ± standard deviation was used to re- port the findings in the case of normal data distribution. In case of non-normal data distribution, the median was used, and for qualitative variables frequency (percentage) was re- ported. The Independent sample’s T-test was used to com- pare quantitative data if the data distribution was normal, and the Mann Whitney U test was used if it was non-normal. The Chi-square test was used to compare qualitative data. The receiver operating characteristic (ROC) curve was used to determine the predictive value of each of the studied vari- ables. Area under the ROC curve (AUC), cut-off point, sen- sitivity, specificity, positive predictive value, negative predic- tive value, positive and negative likelihood ratios, and J point were reported. In all cases, a P value less than 0.05 was con- sidered significant. Logistic regression and Odds Ratio were used to determine the value of each variable and their coeffi- cients to create the model. The primary bias of the study was missing data, to address this problem we excluded patients with missing data. The comparison was made between pa- tients who survived and those who died. 3. Results 3.1. Baseline and Clinical findings 1000 patients with the mean age of 56.34 ± 18.00 (range: 18 - 96) years were studied (65.7% male). The most frequent un- derlying disease was hypertension (32.2%). Of all patients, 29.9% died during admission. The demographic and clini- cal findings of the studied patients are compared between survived and non-survived cases in table 1. Results showed that the mean age of dead patients was significantly higher (59.36 ± 18.40 vs. 55.05 ± 17.68 years; p = 0.001), and the rate of mortality was significantly lower in females than in males This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Z. Mohammadi et al. 4 Table 1: Comparing the demographic and clinical findings between survived and non-survived cases Variable Alive (n=701) Dead (n=299) Dead (n=1000) P Age (year) Mean ± SD 55.05±17.68 59.36±18.40 56.34±18.00 0.001 Sex Male 443 (63.2) 214 (71.6) 657 (65.7) 0.011 Female 258 (36.8) 85 (28.4) 343 (34.3) Underlying Disease HTN 233 (33.2) 89 (29.8) 322 (32.2) 0.198 DM 92 (13.1) 40 (13.4) 132 (13.2) 0.212 HLP 11 (1.6) 6 (2.0) 17 (1.7) 0.134 Hypothyroidism 14 (2.0) 9 (3.0) 23 (2.3) 0.313 CAD 6 (0.9) 0 00.0) 6 (0.6) 0.219 CVA 4 (0.6) 5 (1.7) 9 (0.9) 0.192 CRF 16 (2.3) 9 (3.0) 25 (2.5) 0.217 Vital Signs HR (beats/min) 91.41±13.41 94.61±13.93 92.37±13.64 0.001 RR (breath/min) 22.05±9.79 23.28±8.67 22.42±9.48 0.031 BT 37.24±0.53 37.31±0.56 37.26±0.54 0.030 SBP (mmHg) 119.82±15.21 121.46±22.96 120.31±17.89 0.128 DBP (mmHg) 74.79±9.04 75.39±12.16 74.97±10.007 0.225 SPO2 (%) 90.32±4.77 84.41±8.17 88.55±6.57 0.001 Hospitalization (Day) Mean ± SD 5.52±4.19 6.23±5.00 5.73±4.45 0.016 * Data were analyzed using Independent-Sample t Test and Chi-Square and presented as mean ± standard deviation (SD) and frequency (%). ** HTN: Hypertension; DM: Diabetes mellitus; HLP: Hyperlipidemia; CAD: Coronary artery disease; CVA: Cerebrovascular accident; CRF: Chronic renal disease; HR: Heart rate; RR: Respiratory rate; BT: Body temperature; SBP: Systolic blood pressure; DBP: Diastolic blood pressure. (28.4% vs. 71.6%; p = 0.011). The mean HR (94.61±13.93 vs. 91.41±13.41/minute; p = 0.001), RR (23.28 ± 8.67 vs. 22.05 ± 9.79/minute; p = 0.031), and temperature (37.31 ± 0.56 vs. 37.4 ± 0.53 Celsius; p = 0.001) were significantly higher in dead patients; however, the value of SaO2 (84.41±8.17 vs. 90.3±4.77%; p=0.001) was lower in dead cases. The length of hospitalization in dead patients was significantly longer (6.23 ± 5.00 vs. 5.52 ± 4.19 days; p=0.016). 3.2. Laboratory findings Laboratory and paraclinical findings of the patients are shown in Table 2. The results showed that the values of WBC (8.27±7.71 vs. 7.53±5.29 cells*103; p = 0.009), neutrophils (81.68±9.90% vs. 78.93±12.19%; p=0.001), ALT (60.82±66.04 vs. 46.11±47.48 IU/L; p=0.001), AST (56.46±75.00 vs. 41.73±38.55 IU/L; p=0.001), creatinine (1.56±1.34 vs. 1.23±12.1 mg/dL; p=0.001), and urea (62.85±54.98 vs. 42.98±32.62; p=0.001) were significantly higher in dead patients. 3.3. Predictors of Mortality There were significant associations between COVID-19 in- hospital mortality and hospitalization above five days (p = 0.001), WBC > 4000 Cells*103/mL (p < 0.01), AST above 40 IU/L (p = 0.001), ALT above 40 IU/L (p = 0.001), creatinine above 1.4 mg/dL (p = 0.007), urea above 100 mg/dL (p = 0.024), and SaO2 below 80% (p = 0.001) (table 3). Based on the results of multivariate logistic regression anal- ysis, hospital stay above five days (OR: 3.473; 95%CI: 1.272 - 9.479; p = 0.15), AST above 40 IU/L (OR: 0.269, 95%CI: 0.179 - 0.402; p = 0.001), creatinine above 1.4 mg/dL (OR: 0.529; 95%CI: 0.344 - 0.813; p = 0.004), urea above 100 mg/dL (OR: 0.327, 95%CI: 0.189 - 0.567; p = 0.001), and SaO2 below 80% (OR: 8.754, 95%CI: 5.413 - 14.156; p = 0.001) were among the independent predictors of COVID-19 in-hospital mortal- ity (Table 4). To evaluate the diagnostic value of independent risk factors of mortality, the ROC Curve analysis was used (Figure 2 and Table 5). SaO2 has an excellent diagnostic value for pre- dicting in-hospital mortality of COVID-19 patients in cut-off point of 85.5% (67.7% sensitivity, and 56.3% specificity). 4. Discussion In this study, which was performed to design a prediction model for hospital mortality in admitted COVID-19 patients, 1000 patients who referred to Imam Reza and Sina Hospitals in Tabriz were studied. The mean age of the patients was 56.34 years, and 65.7% of the patients were male. The mor- tality rate was 29.9%. Evaluation of demographic character- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2022; 10(1): e49 Table 2: Comparing the laboratory findings on admission between survived and non-survived cases Variable Alive (n=701) Dead (n=299) Dead (n=1000) P WBC (Cells*103 /mL) 7.53±5.29 8.27±7.71 7.75±6.12 0.009 Neutrophil (%) 78.93±12.19 81.68±9.90 79.75±11.62 0.001 Lymphocyte (%) 20.88±12.27 18.00±10.04 20.02±11.72 0.001 Hb (g/dL) 13.46±1.94 12.27±2.15 13.11±2.08 0.061 Plt (Cells*103 /mL) 235.53±100.71 235.99±119.43 241.05±106.92 0.110 AST (IU/L) 46.11±47.48 60.82±66.04 50.51±54.09 0.001 ALT (IU/L) 41.73±38.55 56.46±75.00 46.13±52.58 0.001 AlkP (IU/L) 205.01±148.65 203.61±136.81 204.59±145.14 0.443 Cr (mg/dL) 1.23±1.12 1.56±1.34 1.33±1.20 0.001 Urea (mg/dL) 42.98±32.62 62.85±54.98 48.92±41.60 0.001 PT (sec) 14.13±3.97 14.49±4.29 14.24±4.07 0.111 PTT (sec) 37.73±11.04 41.85±14.50 38.97±12.31 0.101 INR 1.14±0.35 1.17±0.38 1.15±0.36 0.126 Na (mEq/L) 140.14±3.94 141.07±4.46 140.42±4.12 0.301 K (mEq/L) 4.24±0.47 4.30±0.46 4.26±0.46 0.231 pH 7.40±0.03 7.39±0.03 7.40±0.03 0.150 HCO3 - (mEq/L) 25.13±6.66 24.71±6.60 25.01±6.64 0.179 PaCO2 (mmHg) 41.48±12.61 43.12±13.74 41.97±12.97 0.835 CRP 0 273 (38.9) 133 (44.5) 406 (40.6) +1 254 (36.2) 64 (21.4) 318 (31.8) +2 136 (19.4) 88 (29.4) 224 (22.4) 0.746 +3 16 (2.3) 7 (2.3) 23 (2.3) +4 22 (3.1) 7 (2.3) 29 (2.9) * Data were analyzed using Independent-Sample t Test and Chi-Square and presented as mean ± SD and frequency (%). ** WBC: White Blood Cell; Hb: Hemoglobin; Plt: Platelet; AST: Aspartate aminotransferase; ALT: Alanine transaminase; AlkP: Alkaline Phosphatase; Cr: Creatinine; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; INR: International Normalized Ratio; Na: Sodium; K: Potassium; CRP: C-Reactive Protein. istics of the studied patients showed that the mean age of the deceased patients was significantly higher (59.36±18.40 vs. 55.05±17.68; p=0.001), and mostly male patients died (71.6% vs. 28.4%; p = 0.011). Assessment of clinical signs also showed that the level of SaO2 was significantly lower in dead patients. The results showed that hospitalization over five days, AST above 40 IU/L, creatinine above 1.4 mg/dL, urea above 100 mg/dL, and SaO2 below 80% were the inde- pendent risk factors of in-hospital mortality among COVID- 19 patients. Numerous predictive models have been published in recent studies to estimate the risk of nosocomial mortality in pa- tients with COVID-19 in eastern and western countries; espe- cially the 4C mortality score, which includes age, sex, number of comorbidities, respiration rate, oxygen saturation, level of consciousness, urea, and c-reactive protein (CRP), which were evaluated in a cohort of 35,000 patients and had an ex- cellent prediction power (AUC = 0.79) (19). In the present study, patients with COVID-19 who died had a higher mean age than other patients. Consistent with the present study, studies conducted in China and the United States also intro- duced a high age as a risk factor for in-hospital mortality but compared to the above studies, the mortality rate in our pa- tients was lower, which seems to be due to differences in de- mographic variables (4, 20, 21). Recent studies have examined various variables in predict- ing mortality in patients with COVID-19 with mild to severe disease and ICU admission. For example, the data of 4711 pa- tients with COVID-19 were investigated in a study by Altschul et al., and the results showed a classification scale for mortal- ity of COVID-19 patients with six variables (age, SPO2, mean arterial pressure (MAP), urea, CRP, and INR) at the time of admission (22). In the study by Liang et al., ten variables (in- cluding radiographic chest abnormalities, age, hemoptysis, dyspnea, unconsciousness, number of comorbidities, his- tory of cancer, neutrophil to lymphocyte ratio, lactate dehy- drogenase (LDH), and direct bilirubin) were evaluated. The results showed that these variables are good predictors of mortality risk in COVID-19 patients (23). The study by Knight et al. also reported a mortality prediction scale consisting of 8 variables (age, sex, number of comorbidities, respiration rate, SPO2, level of consciousness, urea, and CRP), the evaluation of which is a good criterion in the initial clinical examina- tion of patients at hospitalization to predict mortality (19). Consistent with the above studies, the methods used in stud- ies that used machine learning to predict mortality showed This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Z. Mohammadi et al. 6 Table 3: Univariate logistic regression analysis of COVID-19 mortality risk factors Variable Alive (n=701) Dead (n=299) OR (95% CI) P Age (year) < 60 390 (55.6) 150 (50.2) - 0.096 60 - 80 260 (37.1) 111 (37.1) 1.754 (0.932 - 3.304) 0.082 > 80 51 (7.3) 38 (12.7) 2.340 (0.892 - 6.138) 0.084 RR (/minute) > 20 5 (1.6) 2 (1.7) 0.747 (0.106 - 5.282) 0.770 Hospitalizations (day) < 5 647 (93) 233 (80.1) - 0.001 5 - 10 40 (5.7) 49 (16.8) 4.401 (1.955 - 9.906) 0.001 > 10 9 (1.3) 9 (3.1) 4.006 (0.843 - 19.043) 0.081 WBC (Cells*103 /mL) < 4 56 (8.0) 56 (18.7) 0.717 (0.212 - 1.989) 0.016 4 - 10 531 (75.7) 170 (56.9) 0.295 (0.128 - 0.687) 0.004 > 10 114 (16.3) 73 (24.4) 0.354 (0.121 - 1.034) 0.058 Neutrophil (%) < 18 0 0 - - 18 - 63 69 (9.8) 12 (4.0) - - > 63 632 (90.2) 287 (96.0) 0.625 (0.205 - 1.908) 0.409 AST (IU/L) > 40 209 (29.8) 151 (50.5) 6.190 (3.170 - 12.085) 0.001 ALT (IU/L) > 40 305 (43.5) 121 (40.5) 0.287 (0.141 - 0.582) 0.001 Creatinine (mg/dL) > 1.4 96 (13.7) 77 (25.8) 2.673 (1.313 - 5.444) 0.007 Urea (mg/dL) > 100 38 (5.4) 54 (18.1) 2.764 (1.146 - 6.668) 0.024 O2 Saturation (%) < 60 0 (0.0) 0 (0.0) - - 60 - 80 37 (5.3) 102 (34.1) 1.289 (0.891 - 4.313) 0.003 > 80 664 (84.7) 197 (65.9) 0.095 (0.039 - 0.228) 0.001 * Data are presented as frequency (%). CI: confidence interval; OR: Odds Ratio; RR: Respiratory rate; WBC: White blood cells; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase. Table 4: Multivariate logistic regression analysis of COVID-19 mortality risk factors Variable OR (95% CI) P-value Hospitalization > 5 Days 3.473 (1.272 - 9.479) 0.015 AST > 40 IU/L 0.269 (0.179 - 0.402) 0.001 Creatinine > 1.4 mg/dL 0.529 (0.344 - 0.813) 0.004 Urea > 100 mg/dL 0.327 (0.189 - 0.567) 0.001 SPO2 < 80% 8.754 (5.413 - 14.156) 0.001 OR: odds ratio; CI: confidence interval; AST: Aspartate aminotransferase. Table 5: Diagnostic value of independent risk factors of COVID-19 mortality Variable AUC (95% CI) P-value Cut off point Sensitivity Specificity Hospitalization 0.484 (0.443-0.526) 0.439 - - - AST 0.374 (0.328-0.403) 0.001 36.5 61.9 57.6 Creatinine 0.366 (0.335-0.414) 0.001 1.05 48.8 69.4 Urea 0.402 (0.362-0.442) 0.001 71 29.6 89.4 SPO2 0.705 (0.666-0.745) 0.001 85.5 67.7 56.3 AUC: area under the receiver operating characteristic curve; CI: confidence interval; AST: Aspartate aminotransferase. that the above variables in COVID-19 patients admitted to the ICU are good predictors of mortality (24). This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2022; 10(1): e49 In recent studies, the nutritional status of patients with COVID-19 with severe disease was evaluated using mNU- TRIC criteria at the time of hospitalization, and the results showed that the risk of mortality in patients with high nutri- tional risk, based on the above criteria, is twice as high as pa- tients with low nutritional risk (25). However, in the present study, the nutritional status of the patients was not studied, and it is better to be considered in future studies. By comparing the present study’s findings with previous studies, we can say that the clinical and paraclinical char- acteristics of patients and risk factors are different, which seems to be due to differences in the number of samples, de- mographic characteristics, and status of patients on admis- sion. In addition, logistic regression and ROC curve analyses were used to Identify the factors affecting inpatient mortal- ity. However, risk regression models or standard Cox propor- tional hazard models were used in some of the other stud- ies (26, 27). Another reason for the difference between the present study and other studies is the age of the patients. In the present study, young and middle-aged patients were studied, while in other studies, elderly patients with a mean age over 60 years were studied (22, 24, 25, 28-30). In a multicenter study conducted by Gupta et al. on 2215 pa- tients in the United States, nine risk factors (including age, sex, BMI, coronary artery disease (CAD), active cancers, hy- poxemia, hepatic impairment, renal impairment, and the number of hospital ICU beds) were introduced as predictors of 28-day patient mortality (29). In the present study, SPO2, Urea, creatinine, AST, and hospitalization were the factors that predicted the mortality of patients. In contrast, studies have used non-COVID-19 predictive criteria, including the Waterlow score, to predict short-term mortality and length of hospital stay in elderly patients. Waterlow score is a mul- tidimensional criterion for evaluating bed sores, calculated based on age, nutritional status, weight, patient movement, sex, smoking, comorbidities, and medications used (30). 5. Limitations and strengths One of the strengths of this study is the large sample size and evaluation of demographic variables, vital signs, and labora- tory findings of patients with COVID-19. In addition, the as- sessment of mortality risk in patients based on patients’ clin- ical and laboratory findings also increases the applicability of the results of the present study to other patients. Limitations of the present study include: Some of the patients’ tests were not completely performed and they were excluded from the study. Some patients were discharged against medical advice or referred to other centers, and their information could not be fully verified and they were excluded from the study. Also, we didn’t evaluate and report the severity of disease. 6. Conclusion The mortality rate of patients with COVID-19 in our study was 29.9%. Hospitalization of more than five days, AST above 40 IU/L, creatinine above 1.4 mg/dL, urea above 100 mg/dL, and SaO2 < 80% were the independent risk factors of in- hospital mortality of patients with COVID-19. 7. Declarations 7.1. Acknowledgments The researchers acknowledge all study participants and staff of the toxicology ward in the Hospitals for their support from the beginning to the end of the research process. 7.2. Data availability It can be available after legal permits. 7.3. Authors’ contributions All authors participated in the conception and design, acqui- sition of data, analysis and interpretation of data, drafting of the article, review of the article, and finding approval. 7.4. Funding and supports None. 7.5. Conflict of interest No potential and actual conflicts of interest were present dur- ing our investigation. References 1. Phelan AL, Katz R, Gostin LO. The novel coronavirus orig- inating in Wuhan, China: challenges for global health governance. JAMA. 2020;323(8):709-10. 2. Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten C, Gulyaeva AA, et al. The species Severe acute respi- ratory syndrome-related coronavirus: classifying 2019- nCoV and naming it SARS-CoV-2. Nature Microbiology. 2020;5(4):536-44. 3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet. 2020;395(10223):497-506. 4. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. 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