Introduction

cute Respiratory Syndrome Corona Virus-2 (SARS-Covid 2) is a new type of coronavirus that was first identified in Wuhan, China, which led to the development of Coronavirus 2019 (COVID-19) and has spread to other parts of China and 37 countries, including the United States, Japan, Australia, and France (1, 2). Since the virus was first diagnosed, more than 4000 COVID-19 cases have been confirmed worldwide, and the first case reported in the United States was on January 19, 2020, in Washington. Early reports of the disease in China and Italy point to high
mortality and an emphasis on the capacity
of the intensive care unit bed (1). SARS-CoV-2 belongs to the subgenus sarbecovirus subfamily Orthocoronavirinae, known as the pathogen of the coronavirus 2019 (COVID-19) in January 2020(2).

Studies in several human pathogens have been most closely linked to severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV) SARS, a new bacterivorous in Guangdong in southern China in 2002, which has caused 8,000 infections and 774 deaths in 37 countries during 2002-2003; and the Middle East respiratory syndrome (MERS) coronavirus (MERS-CoV), which was reported in 2012 in Saudi Arabia, and caused 2949 infections according to laboratory findings. In December 2012, there were 858 deaths, 38 of which were in South Korea (3). On January 30, 2020, the World Health Organization (WHO) announced the outbreak of the new coronavirus as the sixth public health emergency of international concern, posing a threat not only to China but to all countries. On February 11, 2020, the World Health Organization officially chose the new name for coronavirus, COVID-19 (4). The International Committee on Taxonomy of Viruses renamed the disease-causing virus from COVID-19 to (SARS- Covid- 2) (5). Transmission of SARS Covid 2 is through respiratory droplets and physical contact, and the latency period is 3-7 days but can be more than 24 days (6(. According to a study of patients with the new coronavirus SARS Covid 2, the latency period of the virus has been announced as five days with an average range of 4-7 days (7).

The time interval between exposure to the virus and the onset of clinical symptoms is called the incubation period or latent period of the virus. The World Health Organization estimates the incubation period for the virus between 2 to 10 days (8), the Chinese Health Commission between 10 to 14 days (9), and the US Centers for Disease Control and Prevention between 2 to 14 days (10).

Presenting symptoms (SARS-CoV-2) typically include fever, dyspnea, myalgia, and cough. Although most patients are thought to have a desirable prognosis, older patients with chronic and underlying conditions may eventually have a worse outcome. Patients with underlying and severe problems may develop shortness of breath and hypoxia one week after the disease's onset, which may lead to acute respiratory distress syndrome or end-organ failure. Since studies have been performed on relatively few samples, the risk factors are unknown (2). Given that in recent months the disease has caused severe anxiety in society and changes in people's lifestyles, disability, rising costs, recession, and increased mortality, and now there is no sufficient knowledge, effective drugs, and vaccines to eliminate the virus. The aim of this study was to compare the demographic indicators and underlying risk factors among discharged and dead patients with COVID-19 to provide a more straightforward response by increasing awareness of risk factors and reduce the mortality

Materials and Methods

Participants and sampling

This case-control study was performed on people suspected of having the COVID-19 virus. A total of 274 patients were enrolled in the study from May 1 to April 30, 2020. Only 138 patients with critical illness were tested with PCR, as the corona diagnosis kit was not readily available. Out of 274 patients, only 42 were positive. However, negative PCR can be due to inaccurate sampling. In the case group, 137 patients with COVID-19 who were discharged from the hospital were compared with 137 patients in the control group who died from COVID-19 regarding demographic indicators and underlying risk factors. All participants were selected by convenience sampling methods from Tamin Ejtemaee hospital, which only admitted COVID-19 patients during these days. The hospital's CCU and ICU wards admitted critically ill patients with eight beds in each ward. The ICU patients were hospitalized for 7-10 days. Eighty patients were in the hospital's CCU and ICU. A total of 11 ventilators were available in ICU wards and 6 in the hospital emergency and general wards. There were 6 BIPAP devices in the hospital. Demographics and clinical risk factors information of individuals were collected by a researcher-made questionnaire and medical records. Inclusion criteria included all patients with a diagnosis of COVID-19 who have been referred to a hospital with clinical, laboratory, and radiological symptoms and admitted with the opinion of an infectious or internal medicine specialist. Demographic characteristics, social status, social habits, underlying diseases, clinical manifestations, and laboratory indicators of all patients were examined. The present study had no Code of Ethics.

Data analysis

In the present study, categorical variables were presented as numbers (percentage). Simple and

multiple logistic regression analyses were undertaken to determine the predictors of death among patients with COVID-19. The crude and adjusted odds ratio (OR) and 95% confidence interval (CI) were calculated. Statistical analysis was performed with IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA), and the level of significance was set at 0.05

Results

A total of 274 people were included in the study. Demographic indices and risk factors were compared between the two groups, discharged and died from COVID-19, as case and control groups, respectively.

In the case group, out of 137 discharged patients, 62.8 % and in the control group, 57.7% were men. In fact, the majority of people in both groups were men. In terms of marital status, the majority of people (96.4%) in both groups were married. Unadjusted analysis of the data showed that the odds of death increased with rising age. As compared to patients aged < 50 years, those aged 50-70 and >70 years were 3.26 (95% CI: 1.77-6.01) and 5.22 (95% CI: 2.57-10.61) times more likely to die from COVID-19, respectively. In addition, people who died from COVID-19 were more likely to be more overweight and obese than those who were discharged. Overweight and obesity compared to the normal body mass index significantly increase the odds of death by 2.43 (95% CI: 1.16-5.11) and 3.01 (95% CI: 1.56-5.82) times, respectively. Compared with employed patients, patients who were retired were at significantly increased risk of death (OR=1.83, 95% CI: 1.01-3.33(

The percentage of opium use in people who died from COVID-19 was higher than those who were discharged (13.9% vs. 5.1%). In fact, opium use increases the odds of death due to COVID-19 by 3.23 times, which was statistically significant (95% CI: 1.30-8.01 P=0.011(

Examining the relationship between chronic diseases with discharge and death due to COVID-19 showed that the percentage of chronic diseases in dead patients was higher than those discharged. Chronic diseases significantly increased the odds of death by 1.84 times (95% CI: 1.10-3.08 P = 0.028). Other variables were not significantly associated with death. Based on multiple logistic regression analysis (i.e., adjusted analysis), age of 50-70 and >70 years (OR_Adj=2.82, 95% CI: 1.34-5.94 and OR_Adj=5.35, 95% CI: 2.15-13.29, respectively) and being overweight and obese (OR_Adj=2.42, 95% CI: 1.07-5.45 and OR_Adj=3.26, 95% CI: 1.58-6.72, respectively) increased the likelihood of death. Opium consumption increased the odds of death (OR=2.33, 95% CI: 0.87-6.26), although the difference was not statistically significant (P=0.094) [Table 1].

Table 1. Logistic regression analyses for relationship between demographic/clinical factors and death among patients with COVID-19

BMI: Body Mass Index

As presented in Table 2, underlying diseases such as diabetes (OR=1.58, 95% CI: 0.97-2.58) and respiratory diseases (OR=2.28, 95% CI: 0.90-5.79) increased the likelihood of death, although these increases were not statistically significant (P=0.065 and P=0.082, respectively).

No significant association was found between death and discharged patients in other underlying diseases such as cardiovascular disease, cancer, immune deficiency, respiratory diseases, myocardial infarction, and stroke (p>0.05)
[Table 2].

In examining the relationship between
clinical manifestations of COVID-19 with death and discharge, having dyspnea significantly increased the odds of death among patients with COVID-19 by 2.01(95% CI: 1.09-3.70) times.

Fever and myalgia were higher among discharged patients than dead patients due to COVID-19. In fact, fever and myalgia significantly increased hospital discharge by 0.52 (95% CI: 0.29-0.93) and 0.5 (95% CI: 0.27-0.92) times, respectively
[Table 3].

In terms of laboratory, positive PCR, decreased WBC, increased troponin, increased CPK, decreased lymphocytes significantly increased the odds of death by 8.38, 6.18, 7.9, 3.21, 3.04 times, respectively (P <0.05) [Table 4].

Table 2. Simple logistic regression analysis for relationship between chronic diseases and death among patients with COVID-19

OR: Odds Ratio; CI: Confidence Interval.

Table 3. Simple logistic regression analysis for relationship between clinical manifestations and death among patients with COVID-19

     OR: Odds Ratio; CI: Confidence Interval. ;  NA: Logistic regression analysis not applicable due to small cell count.

Table 4. Simple logistic regression analysis for relationship between laboratory tests and death among patients with COVID-19

OR: Odds Ratio; CI: Confidence Interval.;

NA: Logistic regression analysis not applicable due to small cell count.