Clinical and paraclinical predictive factors for in-hospital mortality in adult patients with COVID-19

Clinical and paraclinical predictive factors for in-hospital mortality in adult patients with COVID-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Clinical and paraclinical predictive factors for in-hospital mortality in adult patients with COVID-19 Seyed Alireza Mousavi, Reyhaneh Sadat Mousavi-Roknabadi, Fateme Nemati, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-819065/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Since December 2019, a type of coronavirus has emerged in Wuhan, China, which has become the focus of global attention due to an epidemic of pneumonia of unknown cause, called COVID-19. This study aimed to investigate the factors affecting in-hospital mortality of patients with COVID-19 hospitalized in one of the main hospital in central Iran. Methods This retrospective cross-sectional study (February 2019-May 2020) was conducted on patients with confirmed diagnosis COVID-19, who were admitted in Yazd Shahid Sadoughi Hospital, in middle of Iran. The patients with uncompleted or missed medical files were excluded from the study. Data were extracted from the patients' medical files and then analyzed. The patients were categorized as survivors and non-survivors groups, and they were compared. Results Totally, 573 patients were enrolled, that 356 (62.2%) were male. The mean ± SD of age was 56.29 ± 17.53 years, and 93 (16.23%) were died. All the complications were more in non-survivors. Intensive care unit (ICU) admission was in 20.5% of the patients which was more in non-survivors (P < 0.001). The results of multivariate logistic regression test showed that plural effusion in lung computed tomography (CT) scan (OR = 0.055, P = 0.009), white blood cell (WBC) (OR = 1.417, P = 0.022), serum albumin (OR = 0.009, P < 0.001), non-invasive mechanical ventilation (OR = 34.315, P < 0.001), and acute respiratory distress syndrome (ARDS) (OR = 66.039, P = 0.001) were achieved as the predictive factors for in-hospital mortality were the predictive factors for in-hospital mortality. Conclusion In-hospital mortality in patients with COVID-19 was about 16%. Plural effusion in lung CT scan, WBC, albumin, non-invasive mechanical ventilation, and ARDS were obtained as the predictive factors for in-hospital mortality. Tropical Medicine Critical Care & Emergency Medicine COVID-19 Prognosis factors Mortality Prevalence Background Since December 2019, a type of coronavirus has emerged in Wuhan, China, which has become the focus of global attention due to an epidemic of pneumonia of unknown cause, called COVID-19. According to statistics of the World Health Organization, in this pandemic, more than 110 million definitive cases of patients with COVID-19 were identified until February 22, 2021. Also, in Iran, until the same date, more than 1.5 million cases and about 60,000 deaths have been reported due to the virus ( 1 ). Early diagnosis of this disease is very important because it affects the prognosis of patients. On the other hand, controlling risk factors and identifying high-risk individuals are considered essential ( 9 ). Given that different studies on different communities have reported scattered results on common clinical symptoms and paraclinical findings as well as factors affecting the severity and mortality, this study aimed to investigate the factors affecting in-hospital mortality of patients with COVID-19 hospitalized in one of the main hospital in central Iran. Methods This retrospective cross-sectional study (February 2019-May 2020) was conducted on patients' medical files with diagnosis COVID-19, who were admitted and hospitalized in Shahid Sadoughi Hospital, Yazd, Iran, one of the biggest teaching and referral hospital in middle of Iran. The inclusion criteria were all adult patients (> 18 years), with confirmed diagnosis of COVID-19 using polymerase chain reaction (PCR) test. The patients with uncompleted or missed medical files were excluded from the study. After relevant coordination, the patients' medical files were extracted from the hospital's archives unit and assessed. Data were recorded in a data gathering form, which was designed by the researchers according to previous researches. It was consist of below parts: 1) patients' demographic information (age, gender, marital status, type of residence, education levels); 2) medical history and clinical findings at the time of admission; 3) laboratory findings; 4) computed tomography (CT) scan findings; 5) treatments; 6) complications; and 7) outcomes (discharge or in-hospital mortality). All analyses were performed by SPSS version 16.0 for Windows. The Shapiro-Wilk t-test was used to test normal distribution of numerical variables. Independent sample t or Mann-Whitney tests was used for two-group comparisons of continuous variables. Chi-square and Fisher’s exact tests were used for proportions. In the univariate logistic regression analysis, each variable was separately entered. Variables with a P < 0.2 from the univariate analysis were entered into the multivariate logistic regression analysis, using the Forward Stepwise methods to determine predictive factors for in-hospital mortality, and odds ratio (OR) were reported. It is noteworthy that despite the large number of variables studied in this survey, the variables were entered the regression model in cluster form (medical findings, laboratory findings, treatments, and complications); and finally, significant variables in each cluster were entered the final multivariate logistic regression model. Results were presented as mean ± standard deviation (SD) for continuous variables and were summarized in number (percentage) for categorical ones. Two-sided P-value less than 0.05 and confidence interval (CI) of 95% were considered to be statistically significant. The current study was conducted in accordance with the Declaration of Helsinki, and it was approved by the vice‑chancellor of research and technology, as well as the local ethics committee of Shahid Sadoughi University of Medical Sciences (IR.SSU.REC.1399.028). To consider ethical issue, the collected data were not revealed to anyone, except for the researchers; hence, patients’ names were kept confidential. Results Totally, 573 patients were enrolled, that 356 (62.2%) were male (P < 0.001). The mean ± SD of age was 56.29 ± 17.53 (range; 19–94) years, and 93 (16.23%) were died in the hospital (P < 0.001). The patients were categorized as two groups: survivors and non-survivors. The patients' demographics' characteristics were statistically similar in both groups Hypertension (36.4%), diabetes mellitus (DM) (26.6%), and chronic heart disease (CHD) (12.8%) were the most common underlying disease, which were observed more in non-survivors (P < 0.001, P < 0.001 and P = 0.001, respectively). Cough (72.9%), fever (69.8%), dyspnea (61%) and myalgia (43%) were the most common clinical findings. The frequency of dyspnea and loss of consciousness were higher in non-survivors (P < 0.001 and P < 0.001) The mean ± SD of pulse rate (PR) and respiratory rate (RR) were 86.99 ± 13.18 and 19.37 ± 5.89 respectively, which were higher in non-survivors (P < 0.001 and P < 0.001). Moreover, higher body temperature was recorded in them (P < 0.001). On the other hand, the peripheral O2 saturation was lower in this group (P < 0.001). But, the frequency of Glasgow coma scale (GCS) of 15 was more in survivors (P < 0.001) (Table 1 ). Table 1 Demographic characteristics and clinical features of patients with definitive diagnosis of COVID-19 Variables Total (n = 573) Non-survivors (n = 93) Survivors (n = 426) P-value Age (year) (mean ± SD) 56.29 ± 17.53 69.71 ± 14.36 53.45 ± 16.42 < 0.001* Gender (%) Male Female 356 (62.6) 213 (37.4) 64 (68.8) 29 (31.2) 262 (61.8) 162 (38.2) 0.236 Marital status (%) Married Single 477 (96.0) 20 (4.0) 88 (97.8) 2 (2.2) 389 (95.6) 18 (4.4) 0.552 Place of residence (%) Urban Rural 404 (94.8) 22 (5.2) 85 (98.8) 1 (1.2) 319 (93.8) 21 (6.2) 0.096 Smoking (%) 6 (5.9) 0 (0) 6 (6.9) 0.588 Hookah consumption (%) 3 (3.2) 0 (0) 3 (3.6) 0.999 Drug abuse (%) 5 (5.0) 2 (12.5) 3 (3.6) 0.180 Suspicious contact (%) 15 (51.7) 1 (50.0) 14 (51.9) 0.999 Recent travel (%) 5 (26.3) 1 (33.3) 4 (25.0) 0.999 Type of travel (%) Internal Abroad 7 (70.0) 3 (30.0) 2 (100) 0 (0) 5 (62.5) 3 (37.5) 0.999 History of underlying diseases (%) Hypertension Diabetes mellitus COPD Asthma Pregnancy Chronic heart diseases Chronic kidney diseases Liver diseases Hematologic diseases Neurological diseases Immunodeficiency diseases Cancer Receiving chemotherapy HIV/AIDS Corticosteroid use 178 (34.6) 137 (26.6) 18 (3.5) 14 (2.7) 3 (0.6) 66 (12.8) 18 (3.5) 3 (0.6) 2 (0.4) 5 (1.0) 11 (2.1) 12 (2.3) 7 (1.4) 0 (0) 7 (1.4) 48 (53.3) 39 (43.3) 9 (10.0) 3 (3.3) 0 (0) 22 (24.4) 11 (12.2) 1 (1.1) 1 (1.1) 2 (2.2) 10 (11.1) 7 (7.8) 5 (5.6) 0 (0) 2 (2.2) 130 (30.6) 98 (23.1) 9 (2.1) 11 (2.6) 3 (0.7) 44 (10.4) 7 (1.6) 2 (0.5) 1 (0.2) 3 (0.7) 1 (0.2) 5 (1.2) 2 (0.5) 0 (0) 5 (1.2) < 0.001* < 0.001* 0.001* 0.720 0.999 0.001* < 0.001* 0.439 0.319 0.212 < 0.001* 0.001* 0.002* - 0.353 Clinical signs and symptoms at the time of admission (%) Fever Cough Sputum Sore throat Myalgia Fatigue Headache Dyspnea Nausea Vomiting Diarrhea Abdominal pain Anorexia Anosmia Loss of taste Loss of consciousness Seizure 360 (69.8) 376 (72.9) 69 (13.4) 29 (5.6) 222 (43.0) 107 (20.7) 102 (19.8) 314 (61.0) 87 (16.9) 63 (12.2) 42 (8.1) 22 (4.3) 52 (10.1) 5 (1.0) 0 (0) 15 (2.9) 2 (0.4) 61 (67.8) 62 (68.9) 16 (17.8) 6 (6.7) 31 (34.4) 25 (27.8) 11 (12.2) 72 (80.0) 12 (13.3) 9 (10.0) 6 (6.7) 4 (4.5) 9 (10.0) 0 (0) 0 (0) 12 (13.3) 1 (1.1) 299 (70.2) 314 (73.7) 53 (12.4) 23 (5.4) 191 (44.8) 82 (19.2) 91 (21.4) 242 (56.9) 75 (17.6) 54 (12.7) 36 (8.5) 18 (4.2) 43 (10.1) 5 (1.2) 0 (0) 3 (0.7) 1 (0.2) 0.705 0.363 0.232 0.802 0.079 0.085 0.057 < 0.001* 0.357 0.491 0.676 0.999 0.999 0.593 - < 0.001* 0.319 Vital signs at the time of admission (mean ± SD) Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Pulse rate (per minute) Respiratory rate (per minute) Body temperature (°C) Oxygen saturation (%) GCS 120.17 ± 15.53 75.74 ± 10.40 86.99 ± 13.18 19.37 ± 5.89 37.46 ± 1.86 90.91 ± 8.61 14.91 ± 3.67 120.93 ± 19.31 75.34 ± 11.86 92.38 ± 17.17 23.00 ± 8.58 37.80 ± 1.07 82.94 ± 15.00 13.64 ± 2.71 120.02 ± 14.64 75.82 ± 10.08 85.87 ± 11.91 18.62 ± 4.85 37.39 ± 1.97 92.56 ± 5.26 15.18 ± 3.78 0.677 0.697 < 0.001* < 0.001* 0.058 < 0.001* < 0.001* Vital signs at the time of admission in categories (%) Systolic blood pressure ≤ 100 mmHg Pulse rate ≤ 60 per minute Pulse rate ≥ 90 per minute Respiratory rate ≥ 20 per minute Body temperature ≥ 37.8°C (under 60 years) Body temperature ≥ 37.5°C (over 60 years) Oxygen saturation (%) ≤ 85 85–89 90–92 ≥93 GCS 15 >15 139 (27.5) 41 (8.1) 243 (47.8) 196 (38.7) 149 (49.8) 144 (58.8) 131 (25.7) 178 (34.8) 264 (51.6) 463 (90.6) 491 (95.5) 79 (15.4) 70 (79.5) 24 (27.3) 75 (85.2) 83 (94.3) 17 (70.8) 55 (76.4) 74 (84.1) 78 (88.6) 58 (65.9) 51 (58.6) 66 (75.0) 72 (81.8) 69 (16.5) 17 (4.1) 168 (40.0) 113 (27.0) 132 (48.0) 89 (51.4) 57 (13.5) 100 (23.6) 206 (48.6) 412 (97.2) 425 (99.8) 7 (1.6) < 0.001* < 0.001* < 0.001* < 0.001* 0.129 < 0.001* < 0.001* < 0.001* 0.003* < 0.001* < 0.001* < 0.001* * Statistically significant; COPD: chronic obstructive pulmonary disease; GCS: Glasgow coma scale; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome Generally, bilateral lung infiltration (90.5%), peripheral pulmonary lobes involvement (65.3%), Ground-glass opacification/opacity (GGO) (45%), and air bronchogram (43%) were the most common lung CT scan findings. Mixed GGO/consolidation (P = 0.002), air bronchogram (P < 0.001), bilateral lung infiltration (P = 0.039), mixed central/peripheral pulmonary lobes involvement (P < 0.001), lymphadenopathy (LAP) ((P < 0.001), crazy paving (P < 0.001) and septal thickening were observed more in non-survivors. Nonetheless, consolidation (P = 0.018) and peripheral pulmonary lobes involvement (P < 0.001) were more in survivors (Table 2 ). Table 2 Paraclinical findings of patients with definitive diagnosis of COVID-19 Variables Total (n = 573) Non-survivors (n = 93) Survivors (n = 426) P-value Lung CT scan findings (%) GGO Consolidation Mixed GGO/consolidation Air Bronchogram Infiltration Unilateral Bilateral Pulmonary lobe involvement Central Environmental Central and peripheral composition Lymphadenopathy Nodules Crazy paving Septal thickening Pleural effusion Severity of pulmonary involvement Normal Minimal Mild Moderate Severe 179 (45.0) 44 (11.1) 171 (43.0) 56 (14.1) 34 (8.5) 360 (90.5) 3 (0.8) 260 (65.3) 131 (32.9) 37 (9.3) 6 (1.5) 72 (18.1) 72 (18.1) 27 (6.8) 11 (2.7) 43 (10.6) 145 (35.9) 149 (36.9) 56 (13.9) 25 (36.8) 2 (2.9) 41 (60.3) 22 (32.4) 2 (2.9) 66 (97.1) 0 (0) 28 (41.2) 40 (58.8) 14 (20.6) 1 (1.5) 30 (44.1) 30 (44.1) 7 (10.3) 0 (0) 2 (3.0) 9 (13.4) 35 (52.2) 21 (31.3) 130 (46.4) 38 (13.6) 108 (38.6) 26 (9.3) 28 (10.0) 248 (88.6) 2 (0.7) 200 (71.4) 74 (26.4) 20 (7.1) 4 (1.4) 35 (12.5) 35 (12.5) 18 (6.4) 7 (2.5) 32 (11.3) 118 (41.7) 97 (34.3) 29 (10.2) 0.174 0.018* 0.002* < 0.001* 0.088 0.039* 0.999 < 0.001* < 0.001* 0.002* 0.999 < 0.001* < 0.001* 0.294 < 0.001* Laboratory findings (mean ± SD) WBC (10 9 /L) Hemoglobin (g/dL) Platelet (10 9 /L) Lymphocyte (10 2 /µL) Blood sugar (mg/dL) BUN (mg/dL) Serum creatinine (mg/dL) CPK (mcg/L) LDH (U/L) AST (U/L) ALT (U/L) ALKP (IU/L) Bilirubin total (mg/dL) Bilirubin direct (mg/dL) Serum sodium (mEq/L) Serum potassium (mEq/L) Calcium (mg/dL) Phosphor (mg/dL) Magnesium (mg/dL) Serum albumin (g/dL) INR ESR CRP Negative Poor positive + 1 + 2 +3 Troponin Positive Negative PH PCO2 HCO3 6.60 ± 6.29 13.85 ± 6.04 177.34 ± 67.01 22.24 ± 11.34 143.01 ± 78.30 32.55 ± 26.10 1.51 ± 4.46 200.80 ± 329.47 530.65 ± 282.76 39.27 ± 37.05 33.39 ± 32.92 197.32 ± 178.28 1.31 ± 1.76 0.48 ± 1.21 136.82 ± 3.80 4.05 ± 0.50 8.21 ± 0.64 3.66 ± 0.96 1.88 ± 0.96 3.78 ± 0.58 1.31 ± 0.32 50.01 ± 30.19 126 (25.4) 43 (8.7) 113 (22.7) 159 (32.0) 56 (11.3) 2 (1.1) 184 (98.9) 7.37 ± 0.11 37.25 ± 9.55 22.25 ± 5.40 10.71 ± 14.12 12.86 ± 2.26 177.75 ± 70.45 14.72 ± 13.72 172.33 ± 88.35 54.85 ± 48.70 1.80 ± 1.35 338.47 ± 545.62 760.05 ± 510.24 55.18 ± 48.22 37.58 ± 42.08 249.45 ± 375.91 1.68 ± 2.26 0.78 ± 1.67 135.25 ± 4.54 4.23 ± 0.68 7.84 ± 0.66 4.00 ± 1.53 1.92 ± 0.38 3.27 ± 0.51 1.34 ± 0.29 67.92 ± 30.93 14 (18.4) 5 (6.6) 25 (32.9) 19 (25.0) 13 (17.1) 0 (0) 34 (100.0) 7.35 ± 0.10 37.29 ± 9.30 20.99 ± 4.23 5.84 ± 2.28 14.14 ± 6.86 177.45 ± 65.59 23.86 ± 10.12 129.81 ± 68.86 27.54 ± 14.51 1.48 ± 5.11 166.94 ± 248.42 484.29 ± 191.58 34.92 ± 30.91 32.0 ± 30.08 185.56 ± 76.29 1.21 ± 1.62 0.40 ± 1.10 137.31 ± 3.51 4.00 ± 0.44 8.33 ± 0.59 3.58 ± 0.66 1.88 ± 0.42 3.98 ± 0.48 1.28 ± 0.34 46.44 ± 28.64 102 (27.3) 35 (9.4) 76 (20.3) 123 (32.9) 38 (10.2) 1 (0.8) 132 (99.2) 7.40 ± 0.12 37.16 ± 10.68 24.31 ± 6.98 0.002* 0.087 0.970 < 0.001* < 0.001* < 0.001* 0.553 0.021 < 0.001* 0.001* 0.284 0.168 0.329 0.180 < 0.001* 0.004* < 0.001* 0.034* 0.467 < 0.001* 0.259 < 0.001* 0.028* 0.999 0.034* 0.952 0.012* * Statistically significant; ALT: alanine transaminase; ALKP: alkaline phosphatase; AST: aspartate transaminase; BUN: Blood urea nitrogen; CPK: creatine phosphokinase; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; GGO: Ground-glass opacification/opacity; INR: international normalized ratio; SD: standard deviation; WBC: white blood cell The laboratory findings are also shown in Table 2 . The mean ± SD of white blood cell (WBC) (P = 0.002), blood sugar (P < 0.001), urea (P < 0.001), lactate dehydrogenase (LDH) (P < 0.001), AST (P = 0.001), serum potassium (P = 0.004), phosphor (P = 0.034), erythrocyte sedimentation rate (ESR) (P < 0.001) were statistically higher in non-survivors. On the other hand, the mean ± SD of lymphocyte (P < 0.001), serum sodium (P = 0.004), calcium (P < 0.001), and serum albumin (P < 0.001) were higher in survivors. Most frequent administrated treatment were lopinavir/ritonavir (Kalletra) (91.8%), hydroxychloroquine (88.7%), and oseltamivir (73%), respectively. In non-survivors vitamin D3 (P < 0.001), levofloxacin (tavanex) (P < 0.001), corticosteroids (P < 0.001), non-invasive mechanical ventilation (P < 0.001), invasive mechanical ventilation (P < 0.001), and renal replacement therapy (RRT) were more administered (P < 0.001). But, administration of hydroxychloroquine was more in survivors (P = 0.028) (Table 3 ). Table 3 Treatments in patients with definitive diagnosis of COVID-19 Variables Total (n = 573) Non-survivors (n = 93) Survivors (n = 426) P-value Hydroxychloroquine Kalletra Oseltamivir Vitamin D3 Antibiotics Meropenem Ceftriaxone Tavanex Corticosteroids Noninvasive mechanical ventilation Invasive mechanical ventilation Renal replacement therapy 456 (88.7) 471 (91.8) 374 (73.0) 300 (58.6) 176 (34.4) 157 (30.7) 83 (16.2) 87 (17.0) 104 (20.3) 43 (8.4) 71 (13.8) 17 (3.3) 72 (81.8) 79 (89.8) 59 (67.0) 46 (52.9) 57 (64.8) 62 (70.5) 18 (20.5) 19 (21.6) 54 (61.4) 32 (36.4) 70 (79.5) 16 (18.4) 384 (90.1) 392 (92.2) 315 (74.3) 254 (59.8) 119 (28.1) 95 (22.5) 65 (15.4) 68 (16.1) 50 (11.8) 11 (2.6) 1 (0.2) 1 (0.2) 0.028* 0.520 0.187 0.282 < 0.001* < 0.001* 0.266 0.214 < 0.001* < 0.001* < 0.001* < 0.001* * Statistically significant As was shown in Table 4 , respiratory failure (15.1%), sepsis (13.1%), and acidosis (9.2%) were the most common complications, totally. All the complications were more in non-survivors. Intensive care unit (ICU) admission was in 20.5% of the patients which was more in non-survivors (P < 0.001). The mean ± SD of hospitalization was 6.79 ± 4.98 days, which was more in non-survivors (P < 0.001). But, the duration of ICU admission was similar in both groups (P = 0.201). Table 4 Complications and outcomes in patients with definitive diagnosis of COVID-19 Variables Total (n = 573) Non-survivors (n = 93) Survivors (n = 426) P-value Complications (%) Sepsis Septic shock Respiratory failure ARDS Heart failure Acidosis Coagulopathy AKI Acute heart injury Secondary infection 67 (13.1) 35 (6.8) 77 (15.1) 40 (7.8) 4 (0.8) 47 (9.2) 20 (3.9) 32 (6.3) 3 (0.6) 7 (1.4) 60 (69.0) 35 (40.2) 73 (83.9) 39 (45.3) 4 (4.7) 38 (44.2) 19 (22.1) 31 (36.0) 3 (3.5) 7 (8.1) 7 (1.7) 0 (0) 4 (0.9) 1 (0.2) 0 (0) 9 (2.1) 1 (0.2) 1 (0.2) 0 (0) 0 (0) < 0.001* < 0.001* < 0.001* < 0.001* 0.001* < 0.001* < 0.001* < 0.001* 0.005* < 0.001* Outcomes Hospital readmission (%) Admission to the intensive care unit (%) Duration of hospitalization (days) (mean ± SD) Duration of hospitalization in the intensive care unit (days) (mean ± SD) Time between onset of clinical symptoms and outcome (days) (mean ± SD) 27 (5.2) 103 (20.5) 6.79 ± 4.98 7.84 ± 7.25 13.02 ± 6.26 10 (11.1) 79 (91.9) 10.68 ± 7.91 8.36 ± 7.78 15.68 ± 8.00 17 (4.0) 24 (5.8) 5.97 ± 3.62 6.22 ± 5.11 12.52 ± 5.76 0.015* < 0.001* < 0.001* 0.201 0.003* Duration between the onset of clinical symptoms and admission to the intensive care unit (day) (mean ± SD) Duration between onset of fever and hospital admission (days) (mean ± SD) Duration between cough onset and hospital admission (days) (mean ± SD) Duration between the onset of dyspnea and hospital admission (days) (mean ± SD) Time between onset of symptoms and onset of corticosteroid treatment (days) (mean ± SD) 10.10 ± 5.24 7.11 ± 4.27 6.69 ± 4.04 6.25 ± 4.58 12.34 ± 4.11 9.90 ± 5.12 6.42 ± 3.58 6.26 ± 3.57 6.38 ± 4.89 12.20 ± 4.38 11.00 ± 5.97 7.22 ± 4.36 6.77 ± 4.12 6.22 ± 4.52 13.25 ± 1.71 0.533 0.310 0.463 0.847 0.644 * Statistically significant; AKI: Acute kidney injury; ARDS: acute respiratory distress syndrome; SD: standard deviation The results of multivariate logistic regression test in each cluster was shown in Table 5 . Plural effusion in lung CT scan (OR = 0.055, P = 0.009), WBC (OR = 1.417, P = 0.022), albumin (OR = 0.009, P < 0.001), non-invasive mechanical ventilation (OR = 34.315, P < 0.001), and acute respiratory distress syndrome (ARDS) (OR = 66.039, P = 0.001) were achieved as the predictive factors for in-hospital mortality (Table 6 ). Table 5 Results of multivariate logistic regression test in each category (medical findings, paraclinical findings, treatments, and complications) Variables B Standard error P-value OR 95% CI for odds ratio Lower Upper Medical findings Age 0.043 0.011 < 0.001* 1.044 1.022 1.067 Diabetes mellitus 0.711 0.325 0.029* 2.036 1.077 3.846 Chemotherapy 2.579 0.933 0.006* 13.181 2.118 82.040 Dyspnea 0.703 0.354 0.047* 2.020 1.010 4.042 Respiratory rate 0.120 0.040 0.003* 1.128 1.043 1.220 GCS 0.677 0.677 0.011* 0.177 0.047 0.667 O 2 saturation 0.024 0.024 0.002* .930 0.887 0.974 Paraclinical findings Plural effusion -5.378 1.975 0.006* 0.005 0.001 0.222 WBC 0.492 0.168 0.003* 1.635 1.177 2.271 Albumin -4.069 1.026 < 0.001* 0.017 0.002 0.128 Treatments Hydroxychloroquine -1.235 0.349 < 0.001* 0.291 0.147 0.576 Antibiotics 0.716 0.288 0.013* 2.046 1.164 3.596 Non-invasive mechanical ventilation 2.101 0.422 < 0.001* 8.174 3.577 18.677 Corticosteroids 1.629 .303 < 0.001* 5.101 2.816 9.239 Complications Respiratory failure 3.759 0.730 < 0.001* 42.923 10.263 179.510 ARDS 4.156 1.376 0.003* 63.788 4.304 945.380 AKI 4.345 1.328 0.001* 77.056 5.711 1039.743 ICU admission 2.853 0.727 < 0.001* 17.346 4.175 72.070 Readmission 2.337 0.915 0.011* 10.346 1.720 62.223 * Statistically significant; ARDS: acute respiratory distress syndrome; AKI: Acute kidney injury; CI: confidence interval; GCS: Glasgow coma scale; ICU: intensive care unit; OR: odds ratio; WBC: white blood cell Table 6 Results of multivariate logistic regression test to determine the predictive factors for in-hospital mortality Variables B Standard error P-value OR 95% CI for odds ratio Lower Upper Plural effusion -2.901 1.237 0.019* 0.055 0.005 0.621 WBC 0.349 0.153 0.022* 1.417 1.051 1.911 Albumin -4.688 1.208 < 0.001* 0.009 0.001 0.098 Non-invasive mechanical ventilation 3.536 0.799 < 0.001* 34.315 7.172 164.178 ARDS 4.190 1.274 0.001* 66.039 5.437 802.171 * Statistically significant; ARDS: acute respiratory distress syndrome; CI: confidence interval; OR: odds ratio; WBC: white blood cell The comparison of confirmed COVID-19 patients in regards of ICU and regular wards' admissions was shown in supplementary Table 1. Discussion The current study investigated the factors affecting in-hospital mortality of patients with COVID-19 hospitalized in one of the main teaching hospital in central Iran. The results showed that the mean ± SD of age was 17.53 ± 56.29 years, and about 60% of inpatients were male. In line with our findings, in a study by Chen et al., the mean age of inpatients was reported as 55 years, of which 67% were male ( 10 ). In another study by Wu et al., the highest mortality rate was reported in older men with underlying disease ( 3 ). During a systematic review and a meta-analysis, Li et al. epidemiologically investigated clinical features, risk factors, and treatment outcomes in patients with COVID-19. The results showed that the mean age of all patients with COVID-19 was 46.7 years, of which 51.8% were male ( 11 ). In the present study, 93 patients (16.23%) died in the hospital. The mean ± SD of age of these patients was 14.36 ± 69.71 years. However, the results showed that age, gender, marital status, place of residence, cigarette, hookah, and drug use, recent travel history, and the history of contact with the suspect person were not different between survivors and non-survivors. In a study conducted in China, Wei et al. showed that the mean age of individuals who died from COVID-19 was 51 years, and most of them were elderly men ( 12 ). In a study by Zhou et al., the mean age of individuals who died from COVID-19 was reported as 69 years, which was significantly higher than the survived group, and most of the deceased patients were male ( 2 ). In a study by Tang et al., this rate was obtained 64 years which was significantly higher than the discharged group ( 13 ). The most common underlying diseases in patients with COVID-19 in the present study were hypertension (36.4%), DM (26.6%), and CHD (12.8%), respectively, all of which were observed in non-survivors. Chen et al. also reported that 51% of patients with a definitive diagnosis of COVID-19 had an underlying disease ( 10 ). Similarly, Liang et al. showed that 40% of patients with COVID-19 had an underlying disease, including cardiovascular, pulmonary, and cerebrovascular diseases, as well as DM and cancer, respectively ( 14 ). In another study performed by Zou et al., 51.59% of individuals had an underlying disease, including hypertension, cardiovascular diseases, DM, chronic respiratory disease, or cancer ( 15 ). In another study, the most common clinical manifestations of COVID-19 were reported as fever, cough, and fatigue ( 16 ). In Zhang et al.’s study, gastrointestinal symptoms, hypertension, and DM were reported as the main underlying diseases in these patients ( 17 ). The present study showed that the most common clinical symptoms in patients were cough, fever, shortness of breath, and myalgia. Shortness of breath and loss of consciousness were significantly more common in non-survivors. Previous similar studies have shown that the most common symptoms observed in patients with COVID-19 were fever and chills, shortness of breath, cough, myalgia, weakness, lethargy, and gastrointestinal symptoms such as nausea, vomiting, and diarrhea ( 6 , 10 , 15 ). Common symptoms in patients with COVID-19 were reported by Wei et al. as fever and cough ( 12 ). Zou et al. stated that the most common symptom present in patients included fever, cough, shortness of breath, hemoptysis, and diarrhea, respectively ( 15 ). In a meta-analysis, Cao et al. showed that the most prevalent clinical manifestations in patients with COVID-19 were fever, cough, shortness of breath, myalgia or fatigue, and respiratory distress ( 18 ). The mean ± SD of PR and RR per minute in this study were significantly higher in non-survivors. On the other hand, the percentage of O 2 saturation in non-survivors was lower. GCS of 15 was more common in survived patients. In a study by Liu et al., the means of heart rate and RR per minute were 24 and 94, respectively ( 19 ). A RR > 24 per minute was reported 29% in Chen et al.’s study, which was significantly higher in deceased patients (63% versus 16%). Also, a heart rate > 125 beats per minute was observed in only 1% of patients. Fever was recorded in 94% of patients and it was similar in survivors and non-survivors ( 10 ). In the present study, the most findings of lung CT scan were bilateral infiltration (90.5%), peripheral lobes involvement (65.3%), GGO (45%), and air bronchogram (43%), generally. In non-survivors, mixed GGO/consolidation, air bronchogram, bilateral infiltration, mixed central-peripheral lobe involvement, LAP, crazy paving, and septal thickening were observed, but consolidation and peripheral lobe involvement were significantly higher in survivors. Chen et al. found that pulmonary involvement was mostly as bilateral pneumonia followed by GGO lesions ( 10 ). Francone et al. showed that the most common view observed on CT scan (less than 7 days from the onset of symptoms) was the GGO; and after 7 days, crazy paving, consolidation, and fibrosis were the most common views, respectively ( 20 ). In a study by Huang et al., 98% had bilateral lung involvement, and in general GGO was more common ( 6 ). Cao et al. mentioned the main findings of imaging as bilateral pneumonia and GGO ( 18 ). Salehi et al. stated that one of the known features of COVID-19 in patients’ early lung CT scans is GGO with peripheral or posterior distribution, mainly in the lower lobes and less in the middle lobe. Septal thickening, bronchiectasis, pleural thickening, and subpleural involvement are some of the less prevalent findings that are mainly seen in the later stages of the disease. Pleural effusions, pericardial effusions, lymphadenopathy, cavitation, halo symptoms, and pneumothorax are very rare but may be seen as the disease progresses. Imaging patterns related to clinical improvement usually occur after 2 weeks of illness and include the gradual removal of opacities and the decrease in the number of lesions and involved lobes ( 21 ). In this study, the means ± SD of WBC, blood sugar, urea, LDH, aspartate transaminase (AST), serum potassium, phosphorus, and ESR were higher in non-survivors, but the mean ± SD of lymphocytes, serum potassium, calcium, and albumin were higher in survivors. In Huang et al.’s study, laboratory features in patients with COVID-19 included leukopenia (25%), lymphopenia (25%), and increased AST (37%) ( 6 ). Zhang et al. also found that prothrombin and D-dimer levels were higher in patients with ICU than ( 17 ). In a meta-analysis, Lippi et al. reported that in almost all patients with COVID-19 (99%), the troponin level increased to the maximum normal range. In addition, troponin levels highly increased in patients with severe infection than those with milder disease; and it could predict the likelihood of heart damage and disease progression toward worse clinical signs, and protective cardiac treatments may be helpful in these patients ( 23 ). In another systematic review and meta-analysis, it was reported that out of 4,663 patients, the most common laboratory finding related to COVID-19 was C-reactive protein (CRP), followed by decreased albumin, increased ESR, decreased eosinophil, increased interleukin 6, decreased lymphocyte count, and finally increased LDH, respectively. Their meta-analytic findings on 1905 patients also showed that the increased CRP and LDH levels, as well as decreased lymphocyte in the patients’ blood samples, would be significantly associated with increased disease severity and mortality ( 25 ). Our findings demonstrated that the most treatments performed were Kaletra, hydroxychloroquine, and oseltamivir, respectively. In non-survivors, vitamin D3, antibiotics, tavanex, corticosteroids, non-invasive mechanical ventilation, invasive mechanical ventilation, and RRT were further used. However, hydroxychloroquine was more administered to survivors. Zhou et al. stated antibiotics and corticosteroids as the most commonly used treatments ( 2 ). Moreover, Chen et al. mentioned the most commonly used treatments for patients as antiviral drugs, oxygen therapy, and antibiotics, but found no evidence of their effectiveness ( 10 ). Various treatments have been suggested for patients over time, which the reason for the observed differences may be due to the increased knowledge and experience of physicians regarding drugs effectiveness in the treatment of COVID-19 and its complications. The most common complications observed in our studied patients included respiratory failure, sepsis, and acidosis, respectively. All complications were more common in non-survivors. Zhou et al. mentioned sepsis, respiratory failure, ARDS, and heart failure as the most common complications, all of which were significantly higher in non-survivors. The results regarding the difference between the onset of symptoms and the onset of complications in our study were similar to the obtained results by Zhou et al.’s( 2 ). In Chen et al.’s study, ARDS was observed in 17% of patients, which was the most common complication ( 10 ). Hospital readmission was observed in 5.2% of patients in the present study, which was higher in non-survivors. ICU admission was observed in 20.5% of patients, which was higher in non-survivors. In Zhou et al.’s study, 26% of patients were admitted to the ICU, which was significantly higher in non-survivors ( 2 ). Also, in our study, the mean of hospital stay were higher in patients with in-hospital mortality, but it was lower in deceased patients in Zhou et al.’s study. However, similar to their results ( 2 ), the duration of ICU admission in patients of both groups was not significantly different in the present research. Also, in line with their results, the means of the time between the onset of clinical symptoms and outcome were more common in non-survivors ( 2 ). The results of the present study showed that 76.7% of patients with ICU admission died, which was significantly higher than survivors. In the study by Auld et al., the mortality rate of patients with ICU admission was reported as 33.9%, which was lower than the result obtained in the present study. This rate was reported as 52–62% in other similar studies ( 28 ). Another study in the United States found that 50–67% of patients with ICU admission died ( 29 ). The results of this study showed that plural effusion in lung CT scan, WBC, albumin, non-invasive mechanical ventilation, and ARDS were the predictive factors for in-hospital mortality in patients with COVID-19. Wang et al. found that CRP could be a valuable marker for predicting the likelihood of exacerbation of the disease in adult patients with non-severe COVID-19 ( 24 ). By examining the clinical findings of 82,719 patients with coronavirus that resulted in the treatment of 4632 patients who died, Deng et al. considered old age and male gender as risk factors for mortality. It was also observed that the time from the onset of symptoms to the treatment center, the time from the onset of symptoms to laboratory confirmation of COVID-19, and the duration of onset of symptoms to the patients' hospitalization of were directly related to higher mortality ( 30 ). The retrospective nature of the study, lack of recording all data accurately, and lack of follow-up of discharged patients were among the limitations of this study. On the other hand, the high sample size of patients and the study of various factors were among the strengths of this study. Using the results of the current study can be effective in physicians' clinical decisions and also policy makers. However, performing multicenter and prospective studies with larger sample sizes and assessing other factors, especially the effect of vaccination, as well as the drug doses and their complications, can be valuable. Conclusions This study showed that most inpatients were male. In-hospital mortality was obtained at about 16% and ICU admission was observed in about 20% of patients with COVID-19. Plural effusion in lung CT scan, WBC, albumin, non-invasive mechanical ventilation, and ARDS were obtained as predictive factors for in-hospital mortality in these patients. Abbreviations ARDS acute respiratory distress syndrome AKI Acute kidney injury ALT alanine transaminase ALKP alkaline phosphatase AST aspartate transaminase BUN Blood urea nitrogen CHD chronic heart disease COPD chronic obstructive pulmonary disease CRP C-reactive protein CPK creatine phosphokinase CT scan computed tomography scan DM diabetes mellitus ESR erythrocyte sedimentation rate GCS Glasgow coma scale GGO Ground-glass opacification/opacity HIV/AIDS human immunodeficiency virus/ acquired immunodeficiency syndrome LAP lymphadenopathy LDH lactate dehydrogenase ICU intensive care unit INR international normalized ratio OR odds ratio PCR polymerase chain reaction PR pulse rate RR respiratory rate RRT renal replacement therapy SD standard deviation WBC white blood cell Declarations Ethics approval and consent to participate The current study was approved by Shahid Sadoughi University of Medical Sciences (grant No. 7745), as well as the local Ethic Committee of Shahid Sadoughi University of Medical Sciences (IR.SSU.REC.1399.028). This was a retrospective cross-sectional study, which was conducted on patients' medical files. So informed consent was not required for this survey. To consider ethical issue, the collected data were not revealed to anyone, except for the researchers; hence, patients’ names were kept confidential. Consent for publication Not applicable Availability of data and materials The data are available on logical request. Competing interests The authors have no conflicts of interest to declare for this study. Funding The current study was approved and financially supported by Yazd Shahid Sadoughi University of Medical Sciences (grant No. 7745). Authors' contributions SAM and RSM contributed to supervision, conception, design, acquisition of data, and writing up the manuscript. RSM, FN, SP, AG, HP, KA, RSM contributed to search literature and related studies. RSM, FN, SP, AG, HP, KA, ASY contributed to data acquisition. RSM and RSM contributed to data analysis. All authors contributed to write the first draft of the manuscript, reviewed and edited it. All authors approved the final version of the manuscript. Acknowledgement The current study was approved and financially supported by Shahid Sadoughi University of Medical Sciences (grant No. 7745), as well as the local Ethic Committee of Shahid Sadoughi University of Medical Sciences (IR.SSU.REC.1399.028). References World Health O. WHO Coronavirus Disease (COVID-19) Dashboard. 2021. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The lancet. 2020;395(10229):1054–62. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. Jama. 2020;323(13):1239–42. doi: 10.1001/jama.2020.2648 . PubMed PMID: 32091533. 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Deng X, Yang J, Wang W, Wang X, Zhou J, Chen Z, et al. Case fatality risk of novel coronavirus diseases 2019 in China. medRxiv. 2020. Epub 2020/06/09. doi: 10.1101/2020.03.04.20031005 . PubMed PMID: 32511425; PubMed Central PMCID: PMCPMC7217011. Supplementary Files Supplementalmaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Mousavi-Roknabadi","email":"data:image/png;base64,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","orcid":"","institution":"Shahid Sadoughi University of Medical Sciences and Health Services","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Reyhaneh","middleName":"Sadat","lastName":"Mousavi-Roknabadi","suffix":""},{"id":47375288,"identity":"ce4ea709-f740-4f40-a8a1-e79b6e0fb116","order_by":2,"name":"Fateme Nemati","email":"","orcid":"","institution":"Islamic Azad University of 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Yazd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hesan","middleName":"","lastName":"Pourgholamali","suffix":""},{"id":47375292,"identity":"1561ab35-6f58-4c56-b955-ad5823886cff","order_by":6,"name":"Kazem Ansari","email":"","orcid":"","institution":"Islamic Azad University of Yazd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kazem","middleName":"","lastName":"Ansari","suffix":""},{"id":47375293,"identity":"3fd554e1-20ae-423f-9c86-9552b08d0450","order_by":7,"name":"Razieh Sadat Mousavi-Roknabadi","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Razieh","middleName":"Sadat","lastName":"Mousavi-Roknabadi","suffix":""},{"id":47375294,"identity":"5a463507-a973-4d64-92da-886e38dc5bed","order_by":8,"name":"Abdolrahim Sadeghi Yakhdani","email":"","orcid":"","institution":"Shahid Sadoughi University of Medical Sciences and Health Services","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdolrahim","middleName":"Sadeghi","lastName":"Yakhdani","suffix":""}],"badges":[],"createdAt":"2021-08-16 11:33:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-819065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-819065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13711086,"identity":"50346d2f-42a6-46c5-9297-ccd8986137cf","added_by":"auto","created_at":"2021-09-17 14:21:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-819065/v1/24c660f6-a6f2-4a40-aa58-f9db12a4677b.pdf"},{"id":12721127,"identity":"855be6f9-cf60-4f0f-bdbf-81dbed8e6fcb","added_by":"auto","created_at":"2021-08-24 16:28:01","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":44352,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-819065/v1/62892876be90e9c9bc6ac797.docx"}],"financialInterests":"","formattedTitle":"Clinical and paraclinical predictive factors for in-hospital mortality in adult patients with COVID-19","fulltext":[{"header":"Background","content":"\u003cp\u003eSince December 2019, a type of coronavirus has emerged in Wuhan, China, which has become the focus of global attention due to an epidemic of pneumonia of unknown cause, called COVID-19. According to statistics of the World Health Organization, in this pandemic, more than 110\u0026nbsp;million definitive cases of patients with COVID-19 were identified until February 22, 2021. Also, in Iran, until the same date, more than 1.5\u0026nbsp;million cases and about 60,000 deaths have been reported due to the virus (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarly diagnosis of this disease is very important because it affects the prognosis of patients. On the other hand, controlling risk factors and identifying high-risk individuals are considered essential (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Given that different studies on different communities have reported scattered results on common clinical symptoms and paraclinical findings as well as factors affecting the severity and mortality, this study aimed to investigate the factors affecting in-hospital mortality of patients with COVID-19 hospitalized in one of the main hospital in central Iran.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective cross-sectional study (February 2019-May 2020) was conducted on patients' medical files with diagnosis COVID-19, who were admitted and hospitalized in Shahid Sadoughi Hospital, Yazd, Iran, one of the biggest teaching and referral hospital in middle of Iran. The inclusion criteria were all adult patients (\u0026gt;\u0026thinsp;18 years), with confirmed diagnosis of COVID-19 using polymerase chain reaction (PCR) test. The patients with uncompleted or missed medical files were excluded from the study.\u003c/p\u003e \u003cp\u003eAfter relevant coordination, the patients' medical files were extracted from the hospital's archives unit and assessed. Data were recorded in a data gathering form, which was designed by the researchers according to previous researches. It was consist of below parts: 1) patients' demographic information (age, gender, marital status, type of residence, education levels); 2) medical history and clinical findings at the time of admission; 3) laboratory findings; 4) computed tomography (CT) scan findings; 5) treatments; 6) complications; and 7) outcomes (discharge or in-hospital mortality).\u003c/p\u003e \u003cp\u003eAll analyses were performed by SPSS version 16.0 for Windows. The Shapiro-Wilk t-test was used to test normal distribution of numerical variables. Independent sample \u003cem\u003et\u003c/em\u003e or Mann-Whitney tests was used for two-group comparisons of continuous variables. Chi-square and Fisher\u0026rsquo;s exact tests were used for proportions. In the univariate logistic regression analysis, each variable was separately entered. Variables with a P\u0026thinsp;\u0026lt;\u0026thinsp;0.2 from the univariate analysis were entered into the multivariate logistic regression analysis, using the Forward Stepwise methods to determine predictive factors for in-hospital mortality, and odds ratio (OR) were reported. It is noteworthy that despite the large number of variables studied in this survey, the variables were entered the regression model in cluster form (medical findings, laboratory findings, treatments, and complications); and finally, significant variables in each cluster were entered the final multivariate logistic regression model. Results were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and were summarized in number (percentage) for categorical ones. Two-sided P-value less than 0.05 and confidence interval (CI) of 95% were considered to be statistically significant.\u003c/p\u003e \u003cp\u003e The current study was conducted in accordance with the Declaration of Helsinki, and it was approved by the vice‑chancellor of research and technology, as well as the local ethics committee of Shahid Sadoughi University of Medical Sciences (IR.SSU.REC.1399.028). To consider ethical issue, the collected data were not revealed to anyone, except for the researchers; hence, patients\u0026rsquo; names were kept confidential.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTotally, 573 patients were enrolled, that 356 (62.2%) were male (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age was 56.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53 (range; 19\u0026ndash;94) years, and 93 (16.23%) were died in the hospital (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The patients were categorized as two groups: survivors and non-survivors. The patients' demographics' characteristics were statistically similar in both groups\u003c/p\u003e \u003cp\u003eHypertension (36.4%), diabetes mellitus (DM) (26.6%), and chronic heart disease (CHD) (12.8%) were the most common underlying disease, which were observed more in non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and P\u0026thinsp;=\u0026thinsp;0.001, respectively). Cough (72.9%), fever (69.8%), dyspnea (61%) and myalgia (43%) were the most common clinical findings. The frequency of dyspnea and loss of consciousness were higher in non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of pulse rate (PR) and respiratory rate (RR) were 86.99\u0026thinsp;\u0026plusmn;\u0026thinsp;13.18 and 19.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.89 respectively, which were higher in non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, higher body temperature was recorded in them (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On the other hand, the peripheral O2 saturation was lower in this group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). But, the frequency of Glasgow coma scale (GCS) of 15 was more in survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics and clinical features of patients with definitive diagnosis of COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;573)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (year) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.45\u0026thinsp;\u0026plusmn;\u0026thinsp;16.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356 (62.6)\u003c/p\u003e \u003cp\u003e213 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (68.8)\u003c/p\u003e \u003cp\u003e29 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262 (61.8)\u003c/p\u003e \u003cp\u003e162 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eMarried\u003c/p\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e477 (96.0)\u003c/p\u003e \u003cp\u003e20 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (97.8)\u003c/p\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389 (95.6)\u003c/p\u003e \u003cp\u003e18 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eUrban\u003c/p\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404 (94.8)\u003c/p\u003e \u003cp\u003e22 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (98.8)\u003c/p\u003e \u003cp\u003e1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319 (93.8)\u003c/p\u003e \u003cp\u003e21 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e1 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of travel\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eInternal\u003c/p\u003e \u003cp\u003eAbroad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (70.0)\u003c/p\u003e \u003cp\u003e3 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (100)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (62.5)\u003c/p\u003e \u003cp\u003e3 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of underlying diseases\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003cp\u003ePregnancy\u003c/p\u003e \u003cp\u003eChronic heart diseases\u003c/p\u003e \u003cp\u003eChronic kidney diseases\u003c/p\u003e \u003cp\u003eLiver diseases\u003c/p\u003e \u003cp\u003eHematologic diseases\u003c/p\u003e \u003cp\u003eNeurological diseases\u003c/p\u003e \u003cp\u003eImmunodeficiency diseases\u003c/p\u003e \u003cp\u003eCancer\u003c/p\u003e \u003cp\u003eReceiving chemotherapy\u003c/p\u003e \u003cp\u003eHIV/AIDS\u003c/p\u003e \u003cp\u003eCorticosteroid use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (34.6)\u003c/p\u003e \u003cp\u003e137 (26.6)\u003c/p\u003e \u003cp\u003e18 (3.5)\u003c/p\u003e \u003cp\u003e14 (2.7)\u003c/p\u003e \u003cp\u003e3 (0.6)\u003c/p\u003e \u003cp\u003e66 (12.8)\u003c/p\u003e \u003cp\u003e18 (3.5)\u003c/p\u003e \u003cp\u003e3 (0.6)\u003c/p\u003e \u003cp\u003e2 (0.4)\u003c/p\u003e \u003cp\u003e5 (1.0)\u003c/p\u003e \u003cp\u003e11 (2.1)\u003c/p\u003e \u003cp\u003e12 (2.3)\u003c/p\u003e \u003cp\u003e7 (1.4)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e7 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (53.3)\u003c/p\u003e \u003cp\u003e39 (43.3)\u003c/p\u003e \u003cp\u003e9 (10.0)\u003c/p\u003e \u003cp\u003e3 (3.3)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e22 (24.4)\u003c/p\u003e \u003cp\u003e11 (12.2)\u003c/p\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003cp\u003e10 (11.1)\u003c/p\u003e \u003cp\u003e7 (7.8)\u003c/p\u003e \u003cp\u003e5 (5.6)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (30.6)\u003c/p\u003e \u003cp\u003e98 (23.1)\u003c/p\u003e \u003cp\u003e9 (2.1)\u003c/p\u003e \u003cp\u003e11 (2.6)\u003c/p\u003e \u003cp\u003e3 (0.7)\u003c/p\u003e \u003cp\u003e44 (10.4)\u003c/p\u003e \u003cp\u003e7 (1.6)\u003c/p\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003cp\u003e3 (0.7)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003cp\u003e5 (1.2)\u003c/p\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003cp\u003e0.720\u003c/p\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.439\u003c/p\u003e \u003cp\u003e0.319\u003c/p\u003e \u003cp\u003e0.212\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical signs and symptoms at the time of admission\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eFever\u003c/p\u003e \u003cp\u003eCough\u003c/p\u003e \u003cp\u003eSputum\u003c/p\u003e \u003cp\u003eSore throat\u003c/p\u003e \u003cp\u003eMyalgia\u003c/p\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003cp\u003eNausea\u003c/p\u003e \u003cp\u003eVomiting\u003c/p\u003e \u003cp\u003eDiarrhea\u003c/p\u003e \u003cp\u003eAbdominal pain\u003c/p\u003e \u003cp\u003eAnorexia\u003c/p\u003e \u003cp\u003eAnosmia\u003c/p\u003e \u003cp\u003eLoss of taste\u003c/p\u003e \u003cp\u003eLoss of consciousness\u003c/p\u003e \u003cp\u003eSeizure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 (69.8)\u003c/p\u003e \u003cp\u003e376 (72.9)\u003c/p\u003e \u003cp\u003e69 (13.4)\u003c/p\u003e \u003cp\u003e29 (5.6)\u003c/p\u003e \u003cp\u003e222 (43.0)\u003c/p\u003e \u003cp\u003e107 (20.7)\u003c/p\u003e \u003cp\u003e102 (19.8)\u003c/p\u003e \u003cp\u003e314 (61.0)\u003c/p\u003e \u003cp\u003e87 (16.9)\u003c/p\u003e \u003cp\u003e63 (12.2)\u003c/p\u003e \u003cp\u003e42 (8.1)\u003c/p\u003e \u003cp\u003e22 (4.3)\u003c/p\u003e \u003cp\u003e52 (10.1)\u003c/p\u003e \u003cp\u003e5 (1.0)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e15 (2.9)\u003c/p\u003e \u003cp\u003e2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (67.8)\u003c/p\u003e \u003cp\u003e62 (68.9)\u003c/p\u003e \u003cp\u003e16 (17.8)\u003c/p\u003e \u003cp\u003e6 (6.7)\u003c/p\u003e \u003cp\u003e31 (34.4)\u003c/p\u003e \u003cp\u003e25 (27.8)\u003c/p\u003e \u003cp\u003e11 (12.2)\u003c/p\u003e \u003cp\u003e72 (80.0)\u003c/p\u003e \u003cp\u003e12 (13.3)\u003c/p\u003e \u003cp\u003e9 (10.0)\u003c/p\u003e \u003cp\u003e6 (6.7)\u003c/p\u003e \u003cp\u003e4 (4.5)\u003c/p\u003e \u003cp\u003e9 (10.0)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e12 (13.3)\u003c/p\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e299 (70.2)\u003c/p\u003e \u003cp\u003e314 (73.7)\u003c/p\u003e \u003cp\u003e53 (12.4)\u003c/p\u003e \u003cp\u003e23 (5.4)\u003c/p\u003e \u003cp\u003e191 (44.8)\u003c/p\u003e \u003cp\u003e82 (19.2)\u003c/p\u003e \u003cp\u003e91 (21.4)\u003c/p\u003e \u003cp\u003e242 (56.9)\u003c/p\u003e \u003cp\u003e75 (17.6)\u003c/p\u003e \u003cp\u003e54 (12.7)\u003c/p\u003e \u003cp\u003e36 (8.5)\u003c/p\u003e \u003cp\u003e18 (4.2)\u003c/p\u003e \u003cp\u003e43 (10.1)\u003c/p\u003e \u003cp\u003e5 (1.2)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e3 (0.7)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003cp\u003e0.363\u003c/p\u003e \u003cp\u003e0.232\u003c/p\u003e \u003cp\u003e0.802\u003c/p\u003e \u003cp\u003e0.079\u003c/p\u003e \u003cp\u003e0.085\u003c/p\u003e \u003cp\u003e0.057\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.357\u003c/p\u003e \u003cp\u003e0.491\u003c/p\u003e \u003cp\u003e0.676\u003c/p\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e0.593\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs at the time of admission\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003cp\u003ePulse rate (per minute)\u003c/p\u003e \u003cp\u003eRespiratory rate (per minute)\u003c/p\u003e \u003cp\u003eBody temperature (\u0026deg;C)\u003c/p\u003e \u003cp\u003eOxygen saturation (%)\u003c/p\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.17\u0026thinsp;\u0026plusmn;\u0026thinsp;15.53\u003c/p\u003e \u003cp\u003e75.74\u0026thinsp;\u0026plusmn;\u0026thinsp;10.40\u003c/p\u003e \u003cp\u003e86.99\u0026thinsp;\u0026plusmn;\u0026thinsp;13.18\u003c/p\u003e \u003cp\u003e19.37\u0026thinsp;\u0026plusmn;\u0026thinsp;5.89\u003c/p\u003e \u003cp\u003e37.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e \u003cp\u003e90.91\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61\u003c/p\u003e \u003cp\u003e14.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.93\u0026thinsp;\u0026plusmn;\u0026thinsp;19.31\u003c/p\u003e \u003cp\u003e75.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.86\u003c/p\u003e \u003cp\u003e92.38\u0026thinsp;\u0026plusmn;\u0026thinsp;17.17\u003c/p\u003e \u003cp\u003e23.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58\u003c/p\u003e \u003cp\u003e37.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003cp\u003e82.94\u0026thinsp;\u0026plusmn;\u0026thinsp;15.00\u003c/p\u003e \u003cp\u003e13.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.02\u0026thinsp;\u0026plusmn;\u0026thinsp;14.64\u003c/p\u003e \u003cp\u003e75.82\u0026thinsp;\u0026plusmn;\u0026thinsp;10.08\u003c/p\u003e \u003cp\u003e85.87\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e \u003cp\u003e18.62\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003cp\u003e37.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003cp\u003e92.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003c/p\u003e \u003cp\u003e15.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003cp\u003e0.697\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.058\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs at the time of admission in categories\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eSystolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;100 mmHg\u003c/p\u003e \u003cp\u003ePulse rate\u0026thinsp;\u0026le;\u0026thinsp;60 per minute\u003c/p\u003e \u003cp\u003ePulse rate\u0026thinsp;\u0026ge;\u0026thinsp;90 per minute\u003c/p\u003e \u003cp\u003eRespiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;20 per minute\u003c/p\u003e \u003cp\u003eBody temperature\u0026thinsp;\u0026ge;\u0026thinsp;37.8\u0026deg;C (under 60 years)\u003c/p\u003e \u003cp\u003eBody temperature\u0026thinsp;\u0026ge;\u0026thinsp;37.5\u0026deg;C (over 60 years)\u003c/p\u003e \u003cp\u003eOxygen saturation (%)\u003c/p\u003e \u003cp\u003e\u0026le; 85\u003c/p\u003e \u003cp\u003e85\u0026ndash;89\u003c/p\u003e \u003cp\u003e90\u0026ndash;92\u003c/p\u003e \u003cp\u003e\u0026ge;93\u003c/p\u003e \u003cp\u003eGCS\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e\u0026gt;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (27.5)\u003c/p\u003e \u003cp\u003e41 (8.1)\u003c/p\u003e \u003cp\u003e243 (47.8)\u003c/p\u003e \u003cp\u003e196 (38.7)\u003c/p\u003e \u003cp\u003e149 (49.8)\u003c/p\u003e \u003cp\u003e144 (58.8)\u003c/p\u003e \u003cp\u003e131 (25.7)\u003c/p\u003e \u003cp\u003e178 (34.8)\u003c/p\u003e \u003cp\u003e264 (51.6)\u003c/p\u003e \u003cp\u003e463 (90.6)\u003c/p\u003e \u003cp\u003e491 (95.5)\u003c/p\u003e \u003cp\u003e79 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (79.5)\u003c/p\u003e \u003cp\u003e24 (27.3)\u003c/p\u003e \u003cp\u003e75 (85.2)\u003c/p\u003e \u003cp\u003e83 (94.3)\u003c/p\u003e \u003cp\u003e17 (70.8)\u003c/p\u003e \u003cp\u003e55 (76.4)\u003c/p\u003e \u003cp\u003e74 (84.1)\u003c/p\u003e \u003cp\u003e78 (88.6)\u003c/p\u003e \u003cp\u003e58 (65.9)\u003c/p\u003e \u003cp\u003e51 (58.6)\u003c/p\u003e \u003cp\u003e66 (75.0)\u003c/p\u003e \u003cp\u003e72 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (16.5)\u003c/p\u003e \u003cp\u003e17 (4.1)\u003c/p\u003e \u003cp\u003e168 (40.0)\u003c/p\u003e \u003cp\u003e113 (27.0)\u003c/p\u003e \u003cp\u003e132 (48.0)\u003c/p\u003e \u003cp\u003e89 (51.4)\u003c/p\u003e \u003cp\u003e57 (13.5)\u003c/p\u003e \u003cp\u003e100 (23.6)\u003c/p\u003e \u003cp\u003e206 (48.6)\u003c/p\u003e \u003cp\u003e412 (97.2)\u003c/p\u003e \u003cp\u003e425 (99.8)\u003c/p\u003e \u003cp\u003e7 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.129\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Statistically significant; COPD: chronic obstructive pulmonary disease; GCS: Glasgow coma scale; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGenerally, bilateral lung infiltration (90.5%), peripheral pulmonary lobes involvement (65.3%), Ground-glass opacification/opacity (GGO) (45%), and air bronchogram (43%) were the most common lung CT scan findings. Mixed GGO/consolidation (P\u0026thinsp;=\u0026thinsp;0.002), air bronchogram (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bilateral lung infiltration (P\u0026thinsp;=\u0026thinsp;0.039), mixed central/peripheral pulmonary lobes involvement (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lymphadenopathy (LAP) ((P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), crazy paving (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and septal thickening were observed more in non-survivors. Nonetheless, consolidation (P\u0026thinsp;=\u0026thinsp;0.018) and peripheral pulmonary lobes involvement (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were more in survivors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParaclinical findings of patients with definitive diagnosis of COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;573)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung CT scan findings\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003eGGO\u003c/p\u003e \u003cp\u003eConsolidation\u003c/p\u003e \u003cp\u003eMixed GGO/consolidation\u003c/p\u003e \u003cp\u003eAir Bronchogram\u003c/p\u003e \u003cp\u003eInfiltration\u003c/p\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003cp\u003ePulmonary lobe involvement\u003c/p\u003e \u003cp\u003eCentral\u003c/p\u003e \u003cp\u003eEnvironmental\u003c/p\u003e \u003cp\u003eCentral and peripheral composition\u003c/p\u003e \u003cp\u003eLymphadenopathy\u003c/p\u003e \u003cp\u003eNodules\u003c/p\u003e \u003cp\u003eCrazy paving\u003c/p\u003e \u003cp\u003eSeptal thickening\u003c/p\u003e \u003cp\u003ePleural effusion\u003c/p\u003e \u003cp\u003eSeverity of pulmonary involvement\u003c/p\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003eModerate\u003c/p\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (45.0)\u003c/p\u003e \u003cp\u003e44 (11.1)\u003c/p\u003e \u003cp\u003e171 (43.0)\u003c/p\u003e \u003cp\u003e56 (14.1)\u003c/p\u003e \u003cp\u003e34 (8.5)\u003c/p\u003e \u003cp\u003e360 (90.5)\u003c/p\u003e \u003cp\u003e3 (0.8)\u003c/p\u003e \u003cp\u003e260 (65.3)\u003c/p\u003e \u003cp\u003e131 (32.9)\u003c/p\u003e \u003cp\u003e37 (9.3)\u003c/p\u003e \u003cp\u003e6 (1.5)\u003c/p\u003e \u003cp\u003e72 (18.1)\u003c/p\u003e \u003cp\u003e72 (18.1)\u003c/p\u003e \u003cp\u003e27 (6.8)\u003c/p\u003e \u003cp\u003e11 (2.7)\u003c/p\u003e \u003cp\u003e43 (10.6)\u003c/p\u003e \u003cp\u003e145 (35.9)\u003c/p\u003e \u003cp\u003e149 (36.9)\u003c/p\u003e \u003cp\u003e56 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (36.8)\u003c/p\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003cp\u003e41 (60.3)\u003c/p\u003e \u003cp\u003e22 (32.4)\u003c/p\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003cp\u003e66 (97.1)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e28 (41.2)\u003c/p\u003e \u003cp\u003e40 (58.8)\u003c/p\u003e \u003cp\u003e14 (20.6)\u003c/p\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003cp\u003e30 (44.1)\u003c/p\u003e \u003cp\u003e30 (44.1)\u003c/p\u003e \u003cp\u003e7 (10.3)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e2 (3.0)\u003c/p\u003e \u003cp\u003e9 (13.4)\u003c/p\u003e \u003cp\u003e35 (52.2)\u003c/p\u003e \u003cp\u003e21 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (46.4)\u003c/p\u003e \u003cp\u003e38 (13.6)\u003c/p\u003e \u003cp\u003e108 (38.6)\u003c/p\u003e \u003cp\u003e26 (9.3)\u003c/p\u003e \u003cp\u003e28 (10.0)\u003c/p\u003e \u003cp\u003e248 (88.6)\u003c/p\u003e \u003cp\u003e2 (0.7)\u003c/p\u003e \u003cp\u003e200 (71.4)\u003c/p\u003e \u003cp\u003e74 (26.4)\u003c/p\u003e \u003cp\u003e20 (7.1)\u003c/p\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003cp\u003e35 (12.5)\u003c/p\u003e \u003cp\u003e35 (12.5)\u003c/p\u003e \u003cp\u003e18 (6.4)\u003c/p\u003e \u003cp\u003e7 (2.5)\u003c/p\u003e \u003cp\u003e32 (11.3)\u003c/p\u003e \u003cp\u003e118 (41.7)\u003c/p\u003e \u003cp\u003e97 (34.3)\u003c/p\u003e \u003cp\u003e29 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.088\u003c/p\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.294\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory findings\u003c/b\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003cp\u003ePlatelet (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003cp\u003eLymphocyte (10\u003csup\u003e2\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003cp\u003eBlood sugar (mg/dL)\u003c/p\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003cp\u003eSerum creatinine (mg/dL)\u003c/p\u003e \u003cp\u003eCPK (mcg/L)\u003c/p\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003cp\u003eALKP (IU/L)\u003c/p\u003e \u003cp\u003eBilirubin total (mg/dL)\u003c/p\u003e \u003cp\u003eBilirubin direct (mg/dL)\u003c/p\u003e \u003cp\u003eSerum sodium (mEq/L)\u003c/p\u003e \u003cp\u003eSerum potassium (mEq/L)\u003c/p\u003e \u003cp\u003eCalcium (mg/dL)\u003c/p\u003e \u003cp\u003ePhosphor (mg/dL)\u003c/p\u003e \u003cp\u003eMagnesium (mg/dL)\u003c/p\u003e \u003cp\u003eSerum albumin (g/dL)\u003c/p\u003e \u003cp\u003eINR\u003c/p\u003e \u003cp\u003eESR\u003c/p\u003e \u003cp\u003eCRP\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003ePoor positive\u003c/p\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003cp\u003e+\u0026thinsp;2\u003c/p\u003e \u003cp\u003e+3\u003c/p\u003e \u003cp\u003eTroponin\u003c/p\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003cp\u003ePH\u003c/p\u003e \u003cp\u003ePCO2\u003c/p\u003e \u003cp\u003eHCO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e \u003cp\u003e13.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e \u003cp\u003e177.34\u0026thinsp;\u0026plusmn;\u0026thinsp;67.01\u003c/p\u003e \u003cp\u003e22.24\u0026thinsp;\u0026plusmn;\u0026thinsp;11.34\u003c/p\u003e \u003cp\u003e143.01\u0026thinsp;\u0026plusmn;\u0026thinsp;78.30\u003c/p\u003e \u003cp\u003e32.55\u0026thinsp;\u0026plusmn;\u0026thinsp;26.10\u003c/p\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46\u003c/p\u003e \u003cp\u003e200.80\u0026thinsp;\u0026plusmn;\u0026thinsp;329.47\u003c/p\u003e \u003cp\u003e530.65\u0026thinsp;\u0026plusmn;\u0026thinsp;282.76\u003c/p\u003e \u003cp\u003e39.27\u0026thinsp;\u0026plusmn;\u0026thinsp;37.05\u003c/p\u003e \u003cp\u003e33.39\u0026thinsp;\u0026plusmn;\u0026thinsp;32.92\u003c/p\u003e \u003cp\u003e197.32\u0026thinsp;\u0026plusmn;\u0026thinsp;178.28\u003c/p\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003cp\u003e136.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003cp\u003e8.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003cp\u003e50.01\u0026thinsp;\u0026plusmn;\u0026thinsp;30.19\u003c/p\u003e \u003cp\u003e126 (25.4)\u003c/p\u003e \u003cp\u003e43 (8.7)\u003c/p\u003e \u003cp\u003e113 (22.7)\u003c/p\u003e \u003cp\u003e159 (32.0)\u003c/p\u003e \u003cp\u003e56 (11.3)\u003c/p\u003e \u003cp\u003e2 (1.1)\u003c/p\u003e \u003cp\u003e184 (98.9)\u003c/p\u003e \u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003cp\u003e37.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.55\u003c/p\u003e \u003cp\u003e22.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.12\u003c/p\u003e \u003cp\u003e12.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003cp\u003e177.75\u0026thinsp;\u0026plusmn;\u0026thinsp;70.45\u003c/p\u003e \u003cp\u003e14.72\u0026thinsp;\u0026plusmn;\u0026thinsp;13.72\u003c/p\u003e \u003cp\u003e172.33\u0026thinsp;\u0026plusmn;\u0026thinsp;88.35\u003c/p\u003e \u003cp\u003e54.85\u0026thinsp;\u0026plusmn;\u0026thinsp;48.70\u003c/p\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003cp\u003e338.47\u0026thinsp;\u0026plusmn;\u0026thinsp;545.62\u003c/p\u003e \u003cp\u003e760.05\u0026thinsp;\u0026plusmn;\u0026thinsp;510.24\u003c/p\u003e \u003cp\u003e55.18\u0026thinsp;\u0026plusmn;\u0026thinsp;48.22\u003c/p\u003e \u003cp\u003e37.58\u0026thinsp;\u0026plusmn;\u0026thinsp;42.08\u003c/p\u003e \u003cp\u003e249.45\u0026thinsp;\u0026plusmn;\u0026thinsp;375.91\u003c/p\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003cp\u003e135.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003cp\u003e7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e67.92\u0026thinsp;\u0026plusmn;\u0026thinsp;30.93\u003c/p\u003e \u003cp\u003e14 (18.4)\u003c/p\u003e \u003cp\u003e5 (6.6)\u003c/p\u003e \u003cp\u003e25 (32.9)\u003c/p\u003e \u003cp\u003e19 (25.0)\u003c/p\u003e \u003cp\u003e13 (17.1)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e34 (100.0)\u003c/p\u003e \u003cp\u003e7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003cp\u003e37.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e \u003cp\u003e20.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003cp\u003e14.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86\u003c/p\u003e \u003cp\u003e177.45\u0026thinsp;\u0026plusmn;\u0026thinsp;65.59\u003c/p\u003e \u003cp\u003e23.86\u0026thinsp;\u0026plusmn;\u0026thinsp;10.12\u003c/p\u003e \u003cp\u003e129.81\u0026thinsp;\u0026plusmn;\u0026thinsp;68.86\u003c/p\u003e \u003cp\u003e27.54\u0026thinsp;\u0026plusmn;\u0026thinsp;14.51\u003c/p\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e \u003cp\u003e166.94\u0026thinsp;\u0026plusmn;\u0026thinsp;248.42\u003c/p\u003e \u003cp\u003e484.29\u0026thinsp;\u0026plusmn;\u0026thinsp;191.58\u003c/p\u003e \u003cp\u003e34.92\u0026thinsp;\u0026plusmn;\u0026thinsp;30.91\u003c/p\u003e \u003cp\u003e32.0\u0026thinsp;\u0026plusmn;\u0026thinsp;30.08\u003c/p\u003e \u003cp\u003e185.56\u0026thinsp;\u0026plusmn;\u0026thinsp;76.29\u003c/p\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003cp\u003e137.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003cp\u003e46.44\u0026thinsp;\u0026plusmn;\u0026thinsp;28.64\u003c/p\u003e \u003cp\u003e102 (27.3)\u003c/p\u003e \u003cp\u003e35 (9.4)\u003c/p\u003e \u003cp\u003e76 (20.3)\u003c/p\u003e \u003cp\u003e123 (32.9)\u003c/p\u003e \u003cp\u003e38 (10.2)\u003c/p\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003cp\u003e132 (99.2)\u003c/p\u003e \u003cp\u003e7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003cp\u003e37.16\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e \u003cp\u003e24.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003cp\u003e0.087\u003c/p\u003e \u003cp\u003e0.970\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.553\u003c/p\u003e \u003cp\u003e0.021\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003cp\u003e0.284\u003c/p\u003e \u003cp\u003e0.168\u003c/p\u003e \u003cp\u003e0.329\u003c/p\u003e \u003cp\u003e0.180\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003cp\u003e0.467\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.259\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003cp\u003e0.999\u003c/p\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003cp\u003e0.952\u003c/p\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Statistically significant; ALT: alanine transaminase; ALKP: alkaline phosphatase; AST: aspartate transaminase; BUN: Blood urea nitrogen; CPK: creatine phosphokinase; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; GGO: Ground-glass opacification/opacity; INR: international normalized ratio; SD: standard deviation; WBC: white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe laboratory findings are also shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of white blood cell (WBC) (P\u0026thinsp;=\u0026thinsp;0.002), blood sugar (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), urea (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lactate dehydrogenase (LDH) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AST (P\u0026thinsp;=\u0026thinsp;0.001), serum potassium (P\u0026thinsp;=\u0026thinsp;0.004), phosphor (P\u0026thinsp;=\u0026thinsp;0.034), erythrocyte sedimentation rate (ESR) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were statistically higher in non-survivors. On the other hand, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of lymphocyte (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum sodium (P\u0026thinsp;=\u0026thinsp;0.004), calcium (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and serum albumin (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were higher in survivors.\u003c/p\u003e \u003cp\u003eMost frequent administrated treatment were lopinavir/ritonavir (Kalletra) (91.8%), hydroxychloroquine (88.7%), and oseltamivir (73%), respectively. In non-survivors vitamin D3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), levofloxacin (tavanex) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), corticosteroids (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non-invasive mechanical ventilation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), invasive mechanical ventilation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and renal replacement therapy (RRT) were more administered (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). But, administration of hydroxychloroquine was more in survivors (P\u0026thinsp;=\u0026thinsp;0.028) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatments in patients with definitive diagnosis of COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;573)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxychloroquine\u003c/p\u003e \u003cp\u003eKalletra\u003c/p\u003e \u003cp\u003eOseltamivir\u003c/p\u003e \u003cp\u003eVitamin D3\u003c/p\u003e \u003cp\u003eAntibiotics\u003c/p\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003cp\u003eCeftriaxone\u003c/p\u003e \u003cp\u003eTavanex\u003c/p\u003e \u003cp\u003eCorticosteroids\u003c/p\u003e \u003cp\u003eNoninvasive mechanical ventilation\u003c/p\u003e \u003cp\u003eInvasive mechanical ventilation\u003c/p\u003e \u003cp\u003eRenal replacement therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456 (88.7)\u003c/p\u003e \u003cp\u003e471 (91.8)\u003c/p\u003e \u003cp\u003e374 (73.0)\u003c/p\u003e \u003cp\u003e300 (58.6)\u003c/p\u003e \u003cp\u003e176 (34.4)\u003c/p\u003e \u003cp\u003e157 (30.7)\u003c/p\u003e \u003cp\u003e83 (16.2)\u003c/p\u003e \u003cp\u003e87 (17.0)\u003c/p\u003e \u003cp\u003e104 (20.3)\u003c/p\u003e \u003cp\u003e43 (8.4)\u003c/p\u003e \u003cp\u003e71 (13.8)\u003c/p\u003e \u003cp\u003e17 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (81.8)\u003c/p\u003e \u003cp\u003e79 (89.8)\u003c/p\u003e \u003cp\u003e59 (67.0)\u003c/p\u003e \u003cp\u003e46 (52.9)\u003c/p\u003e \u003cp\u003e57 (64.8)\u003c/p\u003e \u003cp\u003e62 (70.5)\u003c/p\u003e \u003cp\u003e18 (20.5)\u003c/p\u003e \u003cp\u003e19 (21.6)\u003c/p\u003e \u003cp\u003e54 (61.4)\u003c/p\u003e \u003cp\u003e32 (36.4)\u003c/p\u003e \u003cp\u003e70 (79.5)\u003c/p\u003e \u003cp\u003e16 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384 (90.1)\u003c/p\u003e \u003cp\u003e392 (92.2)\u003c/p\u003e \u003cp\u003e315 (74.3)\u003c/p\u003e \u003cp\u003e254 (59.8)\u003c/p\u003e \u003cp\u003e119 (28.1)\u003c/p\u003e \u003cp\u003e95 (22.5)\u003c/p\u003e \u003cp\u003e65 (15.4)\u003c/p\u003e \u003cp\u003e68 (16.1)\u003c/p\u003e \u003cp\u003e50 (11.8)\u003c/p\u003e \u003cp\u003e11 (2.6)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003cp\u003e0.520\u003c/p\u003e \u003cp\u003e0.187\u003c/p\u003e \u003cp\u003e0.282\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.266\u003c/p\u003e \u003cp\u003e0.214\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Statistically significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs was shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respiratory failure (15.1%), sepsis (13.1%), and acidosis (9.2%) were the most common complications, totally. All the complications were more in non-survivors. Intensive care unit (ICU) admission was in 20.5% of the patients which was more in non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of hospitalization was 6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98 days, which was more in non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). But, the duration of ICU admission was similar in both groups (P\u0026thinsp;=\u0026thinsp;0.201).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComplications and outcomes in patients with definitive diagnosis of COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;573)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;426)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplications (%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003cp\u003eSeptic shock\u003c/p\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003cp\u003eARDS\u003c/p\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003cp\u003eAcidosis\u003c/p\u003e \u003cp\u003eCoagulopathy\u003c/p\u003e \u003cp\u003eAKI\u003c/p\u003e \u003cp\u003eAcute heart injury\u003c/p\u003e \u003cp\u003eSecondary infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (13.1)\u003c/p\u003e \u003cp\u003e35 (6.8)\u003c/p\u003e \u003cp\u003e77 (15.1)\u003c/p\u003e \u003cp\u003e40 (7.8)\u003c/p\u003e \u003cp\u003e4 (0.8)\u003c/p\u003e \u003cp\u003e47 (9.2)\u003c/p\u003e \u003cp\u003e20 (3.9)\u003c/p\u003e \u003cp\u003e32 (6.3)\u003c/p\u003e \u003cp\u003e3 (0.6)\u003c/p\u003e \u003cp\u003e7 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (69.0)\u003c/p\u003e \u003cp\u003e35 (40.2)\u003c/p\u003e \u003cp\u003e73 (83.9)\u003c/p\u003e \u003cp\u003e39 (45.3)\u003c/p\u003e \u003cp\u003e4 (4.7)\u003c/p\u003e \u003cp\u003e38 (44.2)\u003c/p\u003e \u003cp\u003e19 (22.1)\u003c/p\u003e \u003cp\u003e31 (36.0)\u003c/p\u003e \u003cp\u003e3 (3.5)\u003c/p\u003e \u003cp\u003e7 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (1.7)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e4 (0.9)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e9 (2.1)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eHospital readmission\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdmission to the intensive care unit\u003c/b\u003e (%)\u003c/p\u003e \u003cp\u003e\u003cb\u003eDuration of hospitalization\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e\u003cb\u003eDuration of hospitalization in the intensive care unit\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e\u003cb\u003eTime between onset of clinical symptoms and outcome\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (5.2)\u003c/p\u003e \u003cp\u003e103 (20.5)\u003c/p\u003e \u003cp\u003e6.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/p\u003e \u003cp\u003e7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003c/p\u003e \u003cp\u003e13.02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11.1)\u003c/p\u003e \u003cp\u003e79 (91.9)\u003c/p\u003e \u003cp\u003e10.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e \u003cp\u003e8.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003cp\u003e15.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (4.0)\u003c/p\u003e \u003cp\u003e24 (5.8)\u003c/p\u003e \u003cp\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e \u003cp\u003e12.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003e0.201\u003c/p\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration between the onset of clinical symptoms and admission to the intensive care unit\u003c/b\u003e (day) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e\u003cb\u003eDuration between onset of fever and hospital admission\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e\u003cb\u003eDuration between cough onset and hospital admission\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e\u003cb\u003eDuration between the onset of dyspnea and hospital admission\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e\u003cb\u003eTime between onset of symptoms and onset of corticosteroid treatment\u003c/b\u003e (days) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.24\u003c/p\u003e \u003cp\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e \u003cp\u003e6.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e \u003cp\u003e6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e \u003cp\u003e12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.90\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12\u003c/p\u003e \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003cp\u003e6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003cp\u003e12.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.97\u003c/p\u003e \u003cp\u003e7.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003cp\u003e6.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003cp\u003e6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003cp\u003e13.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003cp\u003e0.310\u003c/p\u003e \u003cp\u003e0.463\u003c/p\u003e \u003cp\u003e0.847\u003c/p\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Statistically significant; AKI: Acute kidney injury; ARDS: acute respiratory distress syndrome; SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of multivariate logistic regression test in each cluster was shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Plural effusion in lung CT scan (OR\u0026thinsp;=\u0026thinsp;0.055, P\u0026thinsp;=\u0026thinsp;0.009), WBC (OR\u0026thinsp;=\u0026thinsp;1.417, P\u0026thinsp;=\u0026thinsp;0.022), albumin (OR\u0026thinsp;=\u0026thinsp;0.009, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non-invasive mechanical ventilation (OR\u0026thinsp;=\u0026thinsp;34.315, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and acute respiratory distress syndrome (ARDS) (OR\u0026thinsp;=\u0026thinsp;66.039, P\u0026thinsp;=\u0026thinsp;0.001) were achieved as the predictive factors for in-hospital mortality (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multivariate logistic regression test in each category (medical findings, paraclinical findings, treatments, and complications)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI for odds ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eMedical findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e saturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eParaclinical findings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlural effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTreatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroxychloroquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntibiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-invasive mechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorticosteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eComplications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e179.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e945.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1039.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReadmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e* Statistically significant; ARDS: acute respiratory distress syndrome; AKI: Acute kidney injury; CI: confidence interval; GCS: Glasgow coma scale; ICU: intensive care unit; OR: odds ratio; WBC: white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multivariate logistic regression test to determine the predictive factors for in-hospital mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% CI for odds ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlural effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-invasive mechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e164.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e802.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Statistically significant; ARDS: acute respiratory distress syndrome; CI: confidence interval; OR: odds ratio; WBC: white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe comparison of confirmed COVID-19 patients in regards of ICU and regular wards' admissions was shown in supplementary Table\u0026nbsp;1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study investigated the factors affecting in-hospital mortality of patients with COVID-19 hospitalized in one of the main teaching hospital in central Iran. The results showed that the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age was 17.53\u0026thinsp;\u0026plusmn;\u0026thinsp;56.29 years, and about 60% of inpatients were male. In line with our findings, in a study by Chen et al., the mean age of inpatients was reported as 55 years, of which 67% were male (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In another study by Wu et al., the highest mortality rate was reported in older men with underlying disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). During a systematic review and a meta-analysis, Li et al. epidemiologically investigated clinical features, risk factors, and treatment outcomes in patients with COVID-19. The results showed that the mean age of all patients with COVID-19 was 46.7 years, of which 51.8% were male (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, 93 patients (16.23%) died in the hospital. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age of these patients was 14.36\u0026thinsp;\u0026plusmn;\u0026thinsp;69.71 years. However, the results showed that age, gender, marital status, place of residence, cigarette, hookah, and drug use, recent travel history, and the history of contact with the suspect person were not different between survivors and non-survivors. In a study conducted in China, Wei et al. showed that the mean age of individuals who died from COVID-19 was 51 years, and most of them were elderly men (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In a study by Zhou et al., the mean age of individuals who died from COVID-19 was reported as 69 years, which was significantly higher than the survived group, and most of the deceased patients were male (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In a study by Tang et al., this rate was obtained 64 years which was significantly higher than the discharged group (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most common underlying diseases in patients with COVID-19 in the present study were hypertension (36.4%), DM (26.6%), and CHD (12.8%), respectively, all of which were observed in non-survivors. Chen et al. also reported that 51% of patients with a definitive diagnosis of COVID-19 had an underlying disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Similarly, Liang et al. showed that 40% of patients with COVID-19 had an underlying disease, including cardiovascular, pulmonary, and cerebrovascular diseases, as well as DM and cancer, respectively (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In another study performed by Zou et al., 51.59% of individuals had an underlying disease, including hypertension, cardiovascular diseases, DM, chronic respiratory disease, or cancer (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In another study, the most common clinical manifestations of COVID-19 were reported as fever, cough, and fatigue (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In Zhang et al.\u0026rsquo;s study, gastrointestinal symptoms, hypertension, and DM were reported as the main underlying diseases in these patients (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study showed that the most common clinical symptoms in patients were cough, fever, shortness of breath, and myalgia. Shortness of breath and loss of consciousness were significantly more common in non-survivors. Previous similar studies have shown that the most common symptoms observed in patients with COVID-19 were fever and chills, shortness of breath, cough, myalgia, weakness, lethargy, and gastrointestinal symptoms such as nausea, vomiting, and diarrhea (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Common symptoms in patients with COVID-19 were reported by Wei et al. as fever and cough (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Zou et al. stated that the most common symptom present in patients included fever, cough, shortness of breath, hemoptysis, and diarrhea, respectively (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In a meta-analysis, Cao et al. showed that the most prevalent clinical manifestations in patients with COVID-19 were fever, cough, shortness of breath, myalgia or fatigue, and respiratory distress (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of PR and RR per minute in this study were significantly higher in non-survivors. On the other hand, the percentage of O\u003csub\u003e2\u003c/sub\u003e saturation in non-survivors was lower. GCS of 15 was more common in survived patients. In a study by Liu et al., the means of heart rate and RR per minute were 24 and 94, respectively (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A RR\u0026thinsp;\u0026gt;\u0026thinsp;24 per minute was reported 29% in Chen et al.\u0026rsquo;s study, which was significantly higher in deceased patients (63% versus 16%). Also, a heart rate\u0026thinsp;\u0026gt;\u0026thinsp;125 beats per minute was observed in only 1% of patients. Fever was recorded in 94% of patients and it was similar in survivors and non-survivors (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, the most findings of lung CT scan were bilateral infiltration (90.5%), peripheral lobes involvement (65.3%), GGO (45%), and air bronchogram (43%), generally. In non-survivors, mixed GGO/consolidation, air bronchogram, bilateral infiltration, mixed central-peripheral lobe involvement, LAP, crazy paving, and septal thickening were observed, but consolidation and peripheral lobe involvement were significantly higher in survivors. Chen et al. found that pulmonary involvement was mostly as bilateral pneumonia followed by GGO lesions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Francone et al. showed that the most common view observed on CT scan (less than 7 days from the onset of symptoms) was the GGO; and after 7 days, crazy paving, consolidation, and fibrosis were the most common views, respectively (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In a study by Huang et al., 98% had bilateral lung involvement, and in general GGO was more common (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Cao et al. mentioned the main findings of imaging as bilateral pneumonia and GGO (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Salehi et al. stated that one of the known features of COVID-19 in patients\u0026rsquo; early lung CT scans is GGO with peripheral or posterior distribution, mainly in the lower lobes and less in the middle lobe. Septal thickening, bronchiectasis, pleural thickening, and subpleural involvement are some of the less prevalent findings that are mainly seen in the later stages of the disease. Pleural effusions, pericardial effusions, lymphadenopathy, cavitation, halo symptoms, and pneumothorax are very rare but may be seen as the disease progresses. Imaging patterns related to clinical improvement usually occur after 2 weeks of illness and include the gradual removal of opacities and the decrease in the number of lesions and involved lobes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of WBC, blood sugar, urea, LDH, aspartate transaminase (AST), serum potassium, phosphorus, and ESR were higher in non-survivors, but the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of lymphocytes, serum potassium, calcium, and albumin were higher in survivors. In Huang et al.\u0026rsquo;s study, laboratory features in patients with COVID-19 included leukopenia (25%), lymphopenia (25%), and increased AST (37%) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Zhang et al. also found that prothrombin and D-dimer levels were higher in patients with ICU than (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In a meta-analysis, Lippi et al. reported that in almost all patients with COVID-19 (99%), the troponin level increased to the maximum normal range. In addition, troponin levels highly increased in patients with severe infection than those with milder disease; and it could predict the likelihood of heart damage and disease progression toward worse clinical signs, and protective cardiac treatments may be helpful in these patients (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In another systematic review and meta-analysis, it was reported that out of 4,663 patients, the most common laboratory finding related to COVID-19 was C-reactive protein (CRP), followed by decreased albumin, increased ESR, decreased eosinophil, increased interleukin 6, decreased lymphocyte count, and finally increased LDH, respectively. Their meta-analytic findings on 1905 patients also showed that the increased CRP and LDH levels, as well as decreased lymphocyte in the patients\u0026rsquo; blood samples, would be significantly associated with increased disease severity and mortality (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings demonstrated that the most treatments performed were Kaletra, hydroxychloroquine, and oseltamivir, respectively. In non-survivors, vitamin D3, antibiotics, tavanex, corticosteroids, non-invasive mechanical ventilation, invasive mechanical ventilation, and RRT were further used. However, hydroxychloroquine was more administered to survivors. Zhou et al. stated antibiotics and corticosteroids as the most commonly used treatments (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Moreover, Chen et al. mentioned the most commonly used treatments for patients as antiviral drugs, oxygen therapy, and antibiotics, but found no evidence of their effectiveness (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Various treatments have been suggested for patients over time, which the reason for the observed differences may be due to the increased knowledge and experience of physicians regarding drugs effectiveness in the treatment of COVID-19 and its complications.\u003c/p\u003e \u003cp\u003eThe most common complications observed in our studied patients included respiratory failure, sepsis, and acidosis, respectively. All complications were more common in non-survivors. Zhou et al. mentioned sepsis, respiratory failure, ARDS, and heart failure as the most common complications, all of which were significantly higher in non-survivors. The results regarding the difference between the onset of symptoms and the onset of complications in our study were similar to the obtained results by Zhou et al.\u0026rsquo;s(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Chen et al.\u0026rsquo;s study, ARDS was observed in 17% of patients, which was the most common complication (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHospital readmission was observed in 5.2% of patients in the present study, which was higher in non-survivors. ICU admission was observed in 20.5% of patients, which was higher in non-survivors. In Zhou et al.\u0026rsquo;s study, 26% of patients were admitted to the ICU, which was significantly higher in non-survivors (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Also, in our study, the mean of hospital stay were higher in patients with in-hospital mortality, but it was lower in deceased patients in Zhou et al.\u0026rsquo;s study. However, similar to their results (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), the duration of ICU admission in patients of both groups was not significantly different in the present research. Also, in line with their results, the means of the time between the onset of clinical symptoms and outcome were more common in non-survivors (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the present study showed that 76.7% of patients with ICU admission died, which was significantly higher than survivors. In the study by Auld et al., the mortality rate of patients with ICU admission was reported as 33.9%, which was lower than the result obtained in the present study. This rate was reported as 52\u0026ndash;62% in other similar studies (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Another study in the United States found that 50\u0026ndash;67% of patients with ICU admission died (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of this study showed that plural effusion in lung CT scan, WBC, albumin, non-invasive mechanical ventilation, and ARDS were the predictive factors for in-hospital mortality in patients with COVID-19. Wang et al. found that CRP could be a valuable marker for predicting the likelihood of exacerbation of the disease in adult patients with non-severe COVID-19 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). By examining the clinical findings of 82,719 patients with coronavirus that resulted in the treatment of 4632 patients who died, Deng et al. considered old age and male gender as risk factors for mortality. It was also observed that the time from the onset of symptoms to the treatment center, the time from the onset of symptoms to laboratory confirmation of COVID-19, and the duration of onset of symptoms to the patients' hospitalization of were directly related to higher mortality (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe retrospective nature of the study, lack of recording all data accurately, and lack of follow-up of discharged patients were among the limitations of this study. On the other hand, the high sample size of patients and the study of various factors were among the strengths of this study. Using the results of the current study can be effective in physicians' clinical decisions and also policy makers. However, performing multicenter and prospective studies with larger sample sizes and assessing other factors, especially the effect of vaccination, as well as the drug doses and their complications, can be valuable.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study showed that most inpatients were male. In-hospital mortality was obtained at about 16% and ICU admission was observed in about 20% of patients with COVID-19. Plural effusion in lung CT scan, WBC, albumin, non-invasive mechanical ventilation, and ARDS were obtained as predictive factors for in-hospital mortality in these patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute respiratory distress syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALKP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealkaline phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic heart disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatine phosphokinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT scan\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography scan\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eerythrocyte sedimentation rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlasgow coma scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGround-glass opacification/opacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV/AIDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman immunodeficiency virus/ acquired immunodeficiency syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphadenopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elactate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einternational normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulse rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erespiratory rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erenal replacement therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was approved by Shahid Sadoughi University of Medical Sciences (grant No. 7745), as well as the local Ethic Committee of Shahid Sadoughi University of Medical Sciences (IR.SSU.REC.1399.028).\u003c/p\u003e\n\u003cp\u003eThis was a retrospective cross-sectional study, which was conducted on patients\u0026apos; medical files. So informed consent was not required for this survey. To consider ethical issue, the collected data were not revealed to anyone, except for the researchers; hence, patients\u0026rsquo; names were kept confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available on logical request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was approved and financially supported by Yazd Shahid Sadoughi University of Medical Sciences (grant No. 7745).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSAM and RSM contributed to supervision, conception, design, acquisition of data, and writing up the manuscript. RSM, FN, SP, AG, HP, KA, RSM contributed to search literature and related studies. RSM, FN, SP, AG, HP, KA, ASY contributed to data acquisition. RSM and RSM contributed to data analysis. All authors contributed to write the first draft of the manuscript, reviewed and edited it. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was approved and financially supported by Shahid Sadoughi University of Medical Sciences (grant No. 7745), as well as the local Ethic Committee of Shahid Sadoughi University of Medical Sciences (IR.SSU.REC.1399.028).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health O. WHO Coronavirus Disease (COVID-19) Dashboard. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. 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PubMed PMID: 32449374; PubMed Central PMCID: PMCPMC7256350.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai Q, Huang D, Yu H, Zhu Z, Xia Z, Su Y, et al. COVID-19: Abnormal liver function tests. J Hepatol. 2020;73(3):566\u0026ndash;74. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2020.04.006\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 32298767; PubMed Central PMCID: PMCPMC7194951. Epub 2020/04/17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmrani-Nava V, Maleki I, Ahmadi A, Moosazadeh M, Hedayatizadeh-Omran A, Roozbeh F, et al. Evaluation of Hepatic Enzymes Changes and Association with Prognosis in COVID-19 Patients. Hepat Mon. 2020;20(4):e103179. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5812/hepatmon.103179\u003c/span\u003e\u003c/span\u003e. Epub 2020-04-01.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuld SC, Caridi-Scheible M, Blum JM, Robichaux C, Kraft C, Jacob JT, et al. ICU and Ventilator Mortality Among Critically Ill Adults With Coronavirus Disease 2019. Crit Care Med. 2020;48(9):e799-e804. Epub 2020/05/27. doi: 10.1097/ccm.0000000000004457. PubMed PMID: 32452888; PubMed Central PMCID: PMCPMC7255393 Institutes of Health (NIH). Dr. Blum\u0026rsquo;s institution received funding from the NIH received funding from Clew Medical. Dr. Jacob received funding from UptoDate. Dr. Martin received funding from Grifols. The remaining authors have disclosed that they do not have any potential conflicts of interest.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatraju PK, Ghassemieh BJ, Nichols M, Kim R, Jerome KR, Nalla AK, et al. Covid-19 in Critically Ill Patients in the Seattle Region - Case Series. N Engl J Med. 2020;382(21):2012\u0026ndash;22. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2004500\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 32227758; PubMed Central PMCID: PMCPMC7143164. Epub 2020/04/01.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng X, Yang J, Wang W, Wang X, Zhou J, Chen Z, et al. Case fatality risk of novel coronavirus diseases 2019 in China. medRxiv. 2020. Epub 2020/06/09. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.03.04.20031005\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 32511425; PubMed Central PMCID: PMCPMC7217011.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Prognosis factors, Mortality, Prevalence","lastPublishedDoi":"10.21203/rs.3.rs-819065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-819065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSince December 2019, a type of coronavirus has emerged in Wuhan, China, which has become the focus of global attention due to an epidemic of pneumonia of unknown cause, called COVID-19. This study aimed to investigate the factors affecting in-hospital mortality of patients with COVID-19 hospitalized in one of the main hospital in central Iran.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cross-sectional study (February 2019-May 2020) was conducted on patients with confirmed diagnosis COVID-19, who were admitted in Yazd Shahid Sadoughi Hospital, in middle of Iran. The patients with uncompleted or missed medical files were excluded from the study. Data were extracted from the patients' medical files and then analyzed. The patients were categorized as survivors and non-survivors groups, and they were compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTotally, 573 patients were enrolled, that 356 (62.2%) were male. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of age was 56.29\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53 years, and 93 (16.23%) were died. All the complications were more in non-survivors. Intensive care unit (ICU) admission was in 20.5% of the patients which was more in non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of multivariate logistic regression test showed that plural effusion in lung computed tomography (CT) scan (OR\u0026thinsp;=\u0026thinsp;0.055, P\u0026thinsp;=\u0026thinsp;0.009), white blood cell (WBC) (OR\u0026thinsp;=\u0026thinsp;1.417, P\u0026thinsp;=\u0026thinsp;0.022), serum albumin (OR\u0026thinsp;=\u0026thinsp;0.009, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non-invasive mechanical ventilation (OR\u0026thinsp;=\u0026thinsp;34.315, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and acute respiratory distress syndrome (ARDS) (OR\u0026thinsp;=\u0026thinsp;66.039, P\u0026thinsp;=\u0026thinsp;0.001) were achieved as the predictive factors for in-hospital mortality were the predictive factors for in-hospital mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn-hospital mortality in patients with COVID-19 was about 16%. Plural effusion in lung CT scan, WBC, albumin, non-invasive mechanical ventilation, and ARDS were obtained as the predictive factors for in-hospital mortality.\u003c/p\u003e","manuscriptTitle":"Clinical and paraclinical predictive factors for in-hospital mortality in adult patients with COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-24 16:27:59","doi":"10.21203/rs.3.rs-819065/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"582167cf-1553-4fa7-ba6d-1a1eed3d0c80","owner":[],"postedDate":"August 24th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6659798,"name":"Tropical Medicine"},{"id":6659799,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2021-09-09T05:40:44+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-24 16:27:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-819065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-819065","identity":"rs-819065","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}