Epidemiological and Clinical Characteristics of COVID-19 Inpatients and Potential Risk Factors for Mortality in Zahedan, Iran: A Multicenter Study

authors:

avatar Hassan Okati-Aliabad ORCID 1 , avatar Gholamreza Masoudy 1 , * , avatar Hamidreza Kouhpayeh 2 , avatar Alireza Ansari-Moghaddam ORCID 2 , avatar Mahdi Mohammadi ORCID 1 , avatar Seyed Mehdi Tabatabaei 2 , avatar Hossein Ansari 1 , avatar Sharareh Sanei-Sistani 3 , avatar Jalil Nejati ORCID 1 , avatar Roya Alavi-Naini ORCID 2 , avatar Mohammad Khammarnia ORCID 1 , avatar Fatemeh Setoodehzadeh ORCID 1 , avatar Fatemeh Ansari-Moghadam ORCID 1 , avatar Neda Sadat Torab Jahromi 1 , avatar Seideh Zeinab Almasi 1 , avatar Seyed Mohammad Nasiraldin Tabatabaei ORCID 4

Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
Research Center for Infectious Diseases and Tropical Medicine, Zahedan University of Medical Sciences, Zahedan, Iran
Department of Radiology, Zahedan University of Medical Sciences, Zahedan, Iran
Department of Anesthesiology, Zahedan University of Medical Sciences, Zahedan, Iran

how to cite: Okati-Aliabad H, Masoudy G, Kouhpayeh H, Ansari-Moghaddam A, Mohammadi M, et al. Epidemiological and Clinical Characteristics of COVID-19 Inpatients and Potential Risk Factors for Mortality in Zahedan, Iran: A Multicenter Study. Health Scope. 2023;12(3):e128078. https://doi.org/10.5812/jhealthscope-128078.

Abstract

Background:

The coronavirus disease 2019 (COVID-19) pandemic is an emerging global threat to public health.

Objectives:

This study examined the epidemiological and clinical characteristics and death-related factors of COVID-19 in inpatients in Zahedan, Iran.

Methods:

This multicenter study included all COVID-19 patients admitted to Zahedan hospitals within February to April 2020. Demographic, epidemiological, and clinical characteristics were extracted from medical records. Bivariate and multivariate logistic regression models were used to examine the risk factors associated with inpatient mortality.

Results:

Of 425 patients in this study, 237 and 188 were male and female, respectively. Moreover, 31 (7.29%) patients died. The patients with a severe stage of pneumonia and those with a severe/critical condition of COVID-19 were 35.8% and 35.1%, respectively. The most prevalent symptoms were cough (70.8%), shortness of breath (62.1%), fever (34.1%), bruising (28.7%), and shivering (28.5%). The most prevalent underlying diseases were hypertension (23.3%), diabetes (16.7%), cardiovascular disease (13.2%), chronic pulmonary disease (9.6%), and asthma (5.4%). Adjusted odds ratio (OR) of in-hospital mortality increased for patients with older age (OR = 3.74, 95% CI: 1.39 - 10.32), at least one underlying disease (OR = 1.16, 95% CI: 1.01 - 1.32), severe disease (OR = 30.9, 95% CI: 4.01 - 239.09), and critically severe disease (OR = 736.5, 95% CI: 74.75 - 7256.1) compared to mild/moderate disease.

Conclusions:

This study showed that older age, disease severity, and underlying diseases were mortality risk factors due to COVID‐19 infection. This finding indicates that priorities for hospital admission must be given to patients with a higher risk of mortality due to limited facilities, especially in less privileged areas.

1. Background

Coronavirus disease 2019 (COVID-19) is an emerging disease that started in China in December 2019 and is still a global health challenge (1). The clinical presentations of the virus are different depending on the mild or severe disease (2). COVID-19 has been declared an epidemic by the World Health Organization (WHO). The case fatality ratio of COVID-19 has been reported from 2% to 7.2%, with a higher risk for older adults with underlying diseases (3-6); however, it is dissimilar in different countries. The causes of fatality in COVID-19 have not been identified. However, some reported risk factors of mortality in COVID-19 patients are older age (7), late screening of critically ill patients and late intervention (8), other underlying diseases, secondary infections, elevated inflammatory indicators in the blood (9), and multiple comorbidities (6).

The evidence has shown that epidemiological and clinical characteristics of COVID-19 patients are the potential risk factors for mortality. Comorbidities were current in nearly half of the patients, with hypertension being the most common comorbidity, followed by diabetes and coronary heart disease (7). Additionally, the most communal symptoms on admission were fever and cough, followed by sputum production and fatigue (7). A study at Zahedan University of Medical Sciences, Zahedan, Iran, has shown that respiratory failure, acute respiratory distress, sepsis, heart failure, and acute kidney and liver injury were related to the severity and mortality of COVID-19 (8). However, these findings are based on the information acquired during the epidemic’s first three months in limited countries. The expectation is that the mortality pattern of COVID-19 and related risk factors change over time due to its nature. Therefore, it is essential to assess the disease mortality risk factors in different countries and periods.

The disadvantaged regions of Sistan and Balochistan lie in southeastern Iran, bordering Pakistan and Afghanistan, and have weak surveillance systems. The present study hypothesized that considering cultural and social differences, the features and risk factors of COVID-19 mortality dissimilar to other regions and familiarity with these differences are useful for disease management.

To the best of our knowledge, the current study is the first investigation to examine patients’ characteristics with COVID-19 in Zahedan. Identifying the risk factors of mortality and clinical features of COVID-19 can help clinicians better manage and effectively prioritize resources for the highest-risk patients (9).

2. Objectives

This study aimed to identify the potential risk factors of mortality in patients and describe the epidemiological and clinical characteristics of hospitalized patients.

3. Methods

3.1. Study Design

This study investigated 425 COVID-19 patients with a definite outcome (dead or discharged) admitted to all hospitals affiliated with Zahedan University of Medical Sciences. All patients diagnosed with COVID-19 conforming to the WHO interim guidance (10) were enrolled in the study between February 29, 2020, and April 31, 2020. Ethical approval was obtained from Zahedan University of Medical Sciences (IR.ZAUMS.REC.1399.020). Written informed consent was obtained from the patients or their relatives.

3.2. Diagnostic Criteria for COVID-19

The detection of COVID-19 in throat swab samples was performed through real-time reverse transcription polymerase chain reaction. According to the WHO guidance for the clinical management of COVID-19, this study classified symptomatic patients without signs of viral pneumonia or hypoxia, patients with clinical signs of pneumonia but no severe pneumonia symptoms, and patients with oxygen support as mild, moderate, and critical ill, respectively (10). In addition, the age of over 12 years and habitation in Zahedan were the inclusion criteria.

3.3. Data Collection

Demographic, epidemiological, and clinical characteristics were extracted from medical records. Complementary data were collected through interviews with patients or their relatives.

3.4. Statistical Analysis

Frequency and percentage were used to describe the categorical variables. In addition, mean, and standard deviation were employed to express continuous variables. The independent sample t-test, χ2 test, or Fisher’s exact test were used to compare the differences between survivors and nonsurvivors regarding epidemiological and clinical factors and underlying diseases. The univariate logistic regression model was used to explore the risk factors associated with in-hospital mortality, and an odds ratio (OR) and 95% confidence interval were reported. Considering the total number of mortalities, the exact logistic regression was used for the multiple models due to the small number of mortalities (n = 31). For bias reduction, the hospital’s cases and controls were selected from the same groups. In addition, the multivariate model was used for this purpose. Variables were excluded from the univariate analysis if the inter-group differences were not significant, when their accuracy was unconfirmed, or if there were very few events to calculate OR. The data were analyzed in Stata software (version 16), and α less than 0.05 (two-tailed) was considered statistically significant.

4. Results

4.1. Baseline Characteristics

Of the 425 patients, 55.8% and 38.6% were male and older than 50 years, respectively. The mean age of the patients was 45.05 ± 17.38 years (range: 10 - 92 years). In this study, 9.2% and 3.8% of the patients reported substance abuse and cigarette smoking, respectively. Furthermore, 13.6% of the patients traveled to high-risk areas in the last 2 weeks. Moreover, 12.2% of the patients visited a medical care center within 2 weeks before the onset of symptoms, and 11.5% reported close contact with a respiratory patient. The percentages of contacts with respiratory patients at medical centers, family members, and workplaces were 5.9%, 9.6%, and 2.6%, respectively. About 4% of the patients reported close contact with a COVID-19 patient 2 weeks before the onset of symptoms. Additionally, 41.4% of the patients used a face mask, and 54.7% personally visited a medical center.

4.2. Mortality and Associated Risk Factors

There was a significant association between patient age and mortality. The chance of mortality was 4.34 times higher in patients aged more than 50 years than those under 50 years. The patients referred to the hospital by ambulance (OR = 4.94), or other means (OR = 17.62) were more likely to die than those who went to the hospital personally (Table 1).

Table 1.

Demographic and Epidemiological Characteristics of Coronavirus Disease 2019 Survivors and Nonsurvivors in the Southeast of Iran a

Demographic and Epidemiological CharacteristicsTotalMortalityOR (95% CI)
Age (y)
Over 50164 (38.6)22 (13.4)4.34 (1.94 - 9.68) b
Under 50261 (61.4)9 (3.4)1.00
Gender
Male237 (55.8)18 (7.6)1.11 (0.53 - 2.32) c
Female188 (44.2)13 (6.9)1.00
Substance abuse
No386 (90.8)29 (7.5)1.50 (0.34 - 6.55) c
Yes39 (9.2)2 (5.1)1.00
Travel to high-risk areas
No367 (86.4)29 (7.9)2.40 (0.56 - 10.35) c
Yes58 (13.6)2 (3.4)1.00
Visiting a medical care center within 2 weeks before the onset of symptoms
No373 (87.8)29 (7.8)2.11 (0.49 - 9.11) c
Yes52 (12.2)2 (3.8)1.00
Close contact with a respiratory patient within 2 weeks before the onset of symptoms
No376 (88.5)28 (7.4)1.23 (0.36 - 4.1) c
Yes49 (11.5)3 (6.1)1.00
Close contact with a respiratory patient within 2 weeks before the onset of symptoms in medical centers
No400 (94.1)29 (7.3)1.00
Yes25 (5.9)2 (8.0)1.11 (0.25 - 4.95) c
Close contact with a respiratory patient within 2 weeks before the onset of symptoms in the family or workplace
No384 (90.4)27 (7.0)1.00
Yes41 (9.6)4 (9.8)1.43 (0.47 - 4.31) c
Unknown close contact with a respiratory patient within 2 weeks before the onset of symptoms
No138 (32.5)12 (8.7)1.00
Yes287 (67.5)19 (6.6)0.74 (0.35 - 1.58) c
History of close contact with a COVID-19 patient within 2 weeks before the onset of symptoms
No408 (96.0)28 (6.9)1.00
Yes17 (4.0)3 (17.6)2.91 (0.79 - 10.72) c
History of close contact with a suspected COVID-19 patient within 2 weeks before the onset of symptoms
No395 (92.9)30 (7.6)1.00
Yes30 (7.1)1 (3.3)0.42 (0.06 - 3.19) c
Using mask protection
No249 (58.6)15 (6.0)1.00
Yes176 (41.4)16 (9.1)1.56 (0.75 - 3.24) c
How transferred to a medical center
Personally231 (54.7)2 (0.9)1.00
By ambulance86 (20.4)14 (16.3)22.2 (4.9 - 100.2) d
By others105 (24.9)14 (13.3)17.6 (3.9 - 79.06) d

On admission, 37.2% of the patients had a temperature > 37.5, 35.8% were in the severe stage of pneumonia, and 35.1% had severe/critical conditions of COVID-19. The most prevalent symptoms were cough (70.8%), shortness of breath (62.1%), fever (34.1%), bruising (28.7%), and shivering (28.5%). There was an abnormal finding in 94.6% of patients’ chest computed tomography. The univariate analysis showed that pneumonia, disease severity, body temperature, loss of consciousness, and seizure were significantly related to mortality among COVID-19 patients. Compared to patients with mild/moderate pneumonia, the odds of mortality were higher in patients with severe (OR = 16.09) and critically ill (OR = 89.67) levels of pneumonia. For patients with a temperature of higher than 38.5°C, the odds of mortality were 3.65 times higher. The chance of mortality was higher in patients with seizures (OR = 27.1) and loss of consciousness (OR = 7.27) (Table 2). Diarrhea (4.9%), sore throat (5.9%), and runny nose (1.4%) were not significantly different between survivors and nonsurvivors (P > 0.05).

Table 2.

Clinical Characteristics of Coronavirus Disease 2019 Survivors and Nonsurvivors in the Southeast Iran a

Clinical Characteristics on AdmissionTotalMortalityOR (95% CI)
Pneumonia
Mild/moderate272 (64.2)3 (1.1)1.00
Severe138 (32.5)21 (15.2)16.09 (4.71 - 55.01) b
Critically ill14 (3.3)7 (50.0)89.67 (19.09 - 421.08) c
Temperature
≤ 37.5264 (62.9)13 (4.9)1.00
37.5 - 38.5112 (26.7)11 (9.8)2.10 (0.91 - 4.85) d
> 38.544 (10.5)7 (15.9)3.65 (1.37 - 9.75) b
Shivering
No304 (71.5)25 (8.2)1.00
Yes121 (28.5)6 (5.0)0.58 (0.23 - 1.46) d
Fever
No280 (65.9)22 (7.9)1.00
Yes145 (34.1)9 (6.2)0.78 (0.35 - 1.73) d
Cough
No124 (29.2)14 (11.3)1.00
Yes301 (70.8)17 (5.6)0.47 (0.22 - 0.99) d
Shortness of breath
No161 (37.9)11 (6.8)1.00
Yes264 (62.1)20 (7.6)1.12 (0.52 - 2.40) d
General weakness
No359 (84.5)26 (7.2)1.00
Yes66 (15.5)5 (7.6)1.05 (0.39 - 2.84) d
Bruising
No303 (71.3)26 (8.6)1.00
Yes122 (28.7)5 (4.1)0.46 (0.17 - 1.22) d
Irritability
No422 (99.3)30 (7.1)1.00
Yes3 (0.7)1 (33.3)6.53 (0.58 - 74.14) d
Nausea
No357 (84.0)28 (7.8)1.00
Yes68 (16.0)3 (4.4)0.54 (0.16 - 1.84) d
Headache
No352 (82.8)28 (8.0)1.00
Yes73 (17.2)3 (4.1)0.50 (0.15 - 1.68) d
Chest pain
No413 (97.2)30 (7.3)1.00
Yes12 (2.8)1 (8.3)1.16 (0.14 - 9.30) d
Abdominal pain
No415 (97.6)30 (7.2)1.00
Yes10 (2.4)1 (10.0)1.43 (0.18 - 11.63) d
Joint pain
No401 (94.4)29 (7.2)1.00
Yes24 (5.6)2 (8.3)1.17 (0.26 - 5.21) d
Abnormal lung sounds
No380 (89.4)27 (7.1)1.00
Yes45 (10.6)4 (8.9)1.28 (0.42 - 3.83) d
Abnormal computed tomography
No23 (5.4)1 (4.3)1.00
Yes402 (94.6)30 (7.5)1.77 (0.23 - 13.62) d
Seizure
No422 (99.3)29 (6.9)1.00
Yes3 (0.7)2 (66.7)27.10 (2.39 - 307.84) b
Lack of appetite
No371 (87.3)27 (7.3)1.00
Yes54 (12.7)4 (7.4)0.97 (0.34 - 3.04) d
Dizziness
No407 (95.8)30 (7.4)1.00
Yes18 (4.2)1 (5.6)0.74 (0.10 - 5.75) d
Loss of consciousness
No395 (92.9)22 (5.6)1.00
Yes30 (7.1)9 (30.0)7.27 (2.98 - 17.72) b
No sign
No418 (98.4)30 (7.2)1.00
Yes7 (1.6)1 (14.3)2.16 (0.25 - 18.49) d
Complications Arising During Treatment*TotalDeathOR (95% CI)
Sepsis
No399 (93.9)17 (4.3)1.00
Yes26 (6.1)14 (53.8)26.22 (10.54 - 65.22) b
Respiratory failure
No389 (91.5)6 (1.5)1.00
Yes36 (8.5)25 (69.4)145.08 (49.5 - 424.5) b
Acute respiratory distress syndrome
No409 (96.2)21 (5.1)1.00
Yes16 (3.8)10 (62.5)30.79 (10.22 - 92.81) b
Heart failure
No416 (97.9)27 (6.5)1.00
Yes9 (2.1)4 (44.4)11.53 (2.92 - 45.42) b
Coagulopathy
No421 (99.1)30 (7.1)1.00
Yes4 (0.9)1 (25.0)4.34 (0.44 - 43.05) d
Acute heart injury
No418 (98.4)27 (6.5)1.00
Yes7 (1.6)4 (57.1)19.31 (4.11 - 90.69) b
Acute kidney injury
No405 (95.3)22 (5.4)1.00
Yes20 (4.7)9 (45.0)14.24 (5.34 - 37.96) b
Acute liver injury
No422 (99.3)30 (7.1)1.00
Yes3 (0.7)1 (33.3)6.53 (0.58 - 74.14) d
Acidosis
No418 (98.4)25 (6.0)1.00
Yes7 (1.6)6 (85.7)94.32 (10.93 - 813.99) b
Ventilator pneumonia
No422 (99.3)29 (6.9)1.00
Yes3 (0.7)2 (66.7)27.10 (2.39 - 307.84) b
Sepsis
No399 (93.9)17 (4.3)1.00
Yes26 (6.1)14 (53.8)26.22 (10.54 - 65.22) b
Respiratory failure
No389 (91.5)6 (1.5)1.00
Yes36 (8.5)25 (69.4)145.08 (49.5 - 424.5) b
Acute respiratory distress syndrome
No409 (96.2)21 (5.1)1.00
Yes16 (3.8)10 (62.5)30.79 (10.22 - 92.81) b

The univariate analysis showed that complications arising during treatment, including acute respiratory distress syndrome, sepsis, respiratory failure, acute heart injury, heart failure, acute kidney injury, ventilator pneumonia, and acidosis, had a significant relationship with mortality among COVID-19 patients. The chance of mortality was higher in patients with sepsis (OR = 26.22), respiratory failure (OR = 145.08), acute respiratory distress syndrome (OR = 30.79), heart failure (OR = 11.53), acute heart injury (OR = 19.31), acute kidney injury (OR = 14.24), acidosis (OR = 94.32), and ventilator pneumonia (OR = 27.1) (Table 2). Only one patient developed seizures and arrhythmia.

In this study, the most prevalent underlying conditions were hypertension, diabetes, cardiovascular disease, chronic pulmonary disease, and asthma, respectively. Underlying conditions, such as dialysis (OR=6.34) and cerebrovascular disease (OR = 6.93), increased the chance of mortality (Table 3). Furthermore, only 2 (0.5%) patients had congestive heart disease and malignancy.

Table 3.

Distribution of Underlying Diseases Among Coronavirus Disease 2019 Survivors and Nonsurvivors in the Southeast of Iran a

Underlying DiseaseTotalMortalityOR (95% CI)
Dialysis
No412 (96.9)27 (6.6)1.00
Yes13 (3.1)4 (30.8)6.34 (1.83 - 21.92) b
Cardiovascular disease
No369 (86.8)28 (7.6)1.00
Yes56 (13.2)3 (5.4)0.69 (0.20 - 2.35) c
Diabetes
No354 (83.3)26 (7.3)1.00
Yes71 (16.7)5 (7.0)0.96 (0.35 - 2.58) c
Liver disease
No417 (98.1)30 (7.2)1.00
Yes8 (1.9)1 (12.5)1.84 (0.22 - 15.48) c
Chronic kidney disease
No411 (96.7)28 (6.8)1.00
Yes14 (3.3)3 (21.4)3.73 (0.98 - 14.15) c
Chronic neurological disease
No416 (97.9)30 (7.2)1.00
Yes9 (2.1)1 (11.1)1.61 (0.20 - 13.29) c
Chronic lung disease
No384 (90.4)28 (7.3)1.00
Yes41 (9.6)3 (7.3)1.00 (0.29 - 3.46) c
Hypertension
No326 (76.7)25 (7.7)1.00
Yes99 (23.3)6 (6.1)0.78 (0.31 - 1.95) c
Cerebrovascular disease
No416 (97.9)28 (6.7)1.00
Yes9 (2.1)3 (33.3)6.93 (1.64 - 29.18) b
Chronic blood disease
No417 (98.1)30 (7.2)1.00
Yes8 (1.9)1 (12.5)1.84 (0.22 - 15.48) c
Asthma
No402 (94.6)28 (7.0)1.00
Yes23 (5.4)3 (13.0)2.00 (0.56 - 7.15) c
Rheumatologic disease
No412 (96.9)29 (7.0)1.00
Yes13 (3.1)2 (15.4)2.40 (0.51 - 11.35) c
Organ transplant
No423 (99.5)30 (7.1)1.00
Yes2 (0.5)1 (50.0)13.10 (0.80 - 214.68) c
Defect immune system
No412 (96.9)29 (7.0)1.00
Yes13 (3.1)2 (15.4)2.40 (0.51 - 11.35) c

Of all patients admitted to the hospital, 36.9% had an oxygen saturation > 0.93%, 11.5% were admitted to the intensive care unit (ICU), and 8.7% underwent mechanical ventilation. Moreover, 90.6% and 8.2% of the patients underwent high-flow nasal cannulas and invasive mechanical ventilation, respectively. Admission to the ICU increased the odds of mortality (OR = 111.14). Oxygen saturation of more than 0.93% could prevent patients from dying by 77%. The mean heart rate and the respiratory rate were significantly different between COVID-19 survivors and nonsurvivors (P < 0.001) (Table 4). It should be mentioned that most patients took antibiotics (93.6%) and antiviral drugs (96.9%). However, taking such drugs was not related to survival among COVID-19 patients (P < 0.05).

Table 4.

Distribution of Oxygen Therapy and Vital Signs in Coronavirus Disease 2019 Survivors and Nonsurvivors in the Southeast of Iran a

IndicesTotalMortalityOR (95% CI)
High-flow nasal cannulaNo40 (9.4)26 (65.0)1.00
Yes385 (90.6)5 (1.3)0.007 (0.002 - 0.021) b
Non-invasive mechanical ventilationNo417 (98.1)29 (7.0)1.00
Yes8 (1.9)2 (25.0)4.46 (0.86 - 23.09) c
Invasive mechanical ventilationNo390 (91.8)2 (0.5)1.00
Yes35 (8.2)29 (82.9)937.6 (181.1 - 4854.09) b
Intensive care unit admissionNo376 (88.5)4 (1.1)1.00
Yes49 (11.5)27 (55.1)114.14 (36.70 - 355.0) b
Oxygen saturation≤ 0.93267 (63.1)27 (10.1)1.00
> 0.93156 (36.9)4 (2.6)0.23 (0.08 - 0.68) b
Vital SignsNonsurvivorsSurvivorsP-Value
Heart rate< 0.001107.6 ± 19.993.1 ± 14.5< 0.001
Respiratory rate< 0.00126.6 ± 9.1219.5 ± 4.6< 0.001
Systolic blood pressure0.655118.4 ± 23.6116.4 ± 16.80.655 c
Diastolic blood pressure0.46275.0 ± 14.073.3 ± 11.10.462 c

Multivariate regression showed increased odds of mortality associated with older age (OR = 3.74), having at least one underlying disease (OR = 1.16), and severity of COVID-19 (OR = 30.9 and OR = 736.5 for severe and critically ill patients compared to mild/moderate patients, respectively) (Table 5).

Table 5.

Multivariate Exact Logistic Regression to Determine Predictors of Survival in Coronavirus Disease 2019 Patients in the Southeast of Iran

Independent VariablesBStandard Error of BetaOR (95% CI)P-Value
Age (y) 0.513.74 (1.39 - 10.32)0.011
> 501.32
≤ 501
Having at least one underlying disease0.071.16 (1.01 - 1.32)0.032
Yes0.14
No1
History of close contact with COVID-19 patients0.388.07 (3.80-17.14)0.001
Yes2.08
No1
Severity
Critically ill6.601.16736.5 (74.75 - 7256.1)0.001
Severe3.431.0430.97 (4.01 - 239.09)0.001
Mild/moderate111

5. Discussion

The present study generally provides a comprehensive explanation of the clinical specifications of laboratory-confirmed COVID-19 patients and the risk factors for severe COVID-19. Additionally, the study investigated the clinical characteristics of 425 patients hospitalized with COVID-19 and potential risk factors of mortality in a deprived region in Iran. Univariate analysis revealed that age, means of transportation to a hospital, pneumonia severity, disease severity, fever (temperature higher than 38.5°C), seizures, loss of consciousness, sepsis, acute respiratory distress syndrome, and respiratory failure had a significant relationship with mortality in COVID-19 patients. Moreover, acute heart injury, heart failure, acute kidney injury, ventilator-associated pneumonia, dialysis, acidosis, cerebrovascular disease, ICU admission, mechanical ventilation, invasive mechanical ventilation, heart rate, and respiratory rate had a significant relationship with mortality in COVID-19 patients. Multivariate analysis revealed that age, disease severity, and underlying diseases were predictors of mortality in COVID-19 patients.

In this study, in-hospital mortality was 7.29%, and the odds of mortality were significantly greater in patients over 50 years. Other studies have reported different in-hospital mortality rates of 25.7%, 17%, and 3.77% (11-13), which might be due to differences in the social-demographic composition of different parts of the world (14). Older age has also been identified as a risk factor for the mortality of COVID-19 patients (15), indicating a lower immune response in older individuals (16).

A history of close contact with COVID-19 patients was associated with mortality in COVID-19 patients in this study, indicating that exposure to confirmed cases is among the risk factors for severe COVID-19 (17). Ambulance transportation, the severity of pneumonia, and the severity of the disease increased the risk of mortality in COVID-19 patients in the current study. The aforementioned data have a logical justification because severe COVID-19 patients need advanced respiratory services, such as synthetic respiratory services, which cannot be provided inside the ambulance. The aforementioned findings are in line with findings from China indicating the highest case fatality in patients with a critical condition (18). Irregularities in vital signs, including temperature, heart rate, and respiratory rate, were risk factors for mortality in the current study, confirmed by another study (19). Furthermore, this study identified seizure as a risk factor for mortality in COVID-19 patients. Epilepsy patients with COVID-19 might have a high fever, which might cause seizures. Moreover, epilepsy has a significant relationship with mortality in in-hospital patients (20, 21).

According to this study’s results, a decreased level of consciousness, hospitalization in the ICU, invasive mechanical ventilation, and ventilation-related pneumonia were risk factors for mortality, similar to other research findings (22). Most critically ill patients in the ICU require invasive mechanical ventilation, and ventilator-associated pneumonia worsens patients’ clinical condition, which both increase mortality in COVID-19 patients (23, 24). Furthermore, a high incidence of thrombotic complications in patients with COVID-19 pneumonia admitted to the ICU than in the wards can lead to more venous thromboembolism and death of patients admitted to the ICU (25, 26).

Complications developed during treatment, including sepsis, acute respiratory distress syndrome, respiratory failure, acute heart injury, heart failure, acute kidney injury, and acidosis, were risk factors for mortality in this study. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus can damage organs by affecting the renin-angiotensin system (27). Additionally, SARS-CoV-2 neuroinvasive potential might cause respiratory failure in COVID-19 patients (28). Severe respiratory failure in COVID-19 patients leads to hyper-inflammatory responses with either immune dysregulation or macrophage activation syndrome (29). Moreover, acute respiratory failure results in severe hyper-coagulability (30) associated with mortality due to novel coronavirus pneumonia (31). Cardiac injury in patients with COVID-19 is associated with a higher risk of in-hospital mortality, and inflammation can potentially explain cardiac injury (32, 33). An increased inflammatory burden of SARS‐CoV-2 can cause a cardiac event (34). Furthermore, COVID-19 infection can cause ketosis or ketoacidosis, and ketosis, in turn, will increase mortality (35).

Among comorbidities, cerebrovascular diseases and dialysis were risk factors for mortality in the current study. As mentioned in previous studies, cerebrovascular diseases had a relationship with an increased risk of mortality in COVID-19 patients (36, 37). The reason for the increased severity of COVID-19 in patients with cerebrovascular disease is unclear. It is also important that the association between cerebrovascular disease and poor outcomes in COVID-19 patients is not affected by age, gender, and other underlying conditions (38). Future studies should examine this relationship. In addition, the mortality rate in dialysis patients is higher than in the general population (39). This higher mortality rate in COVID-19 dialysis patients might be related to their age because most COVID-19 dialysis patients are older (40).

5.1. Limitations

This study has a structural limitation. This study was a secondary data analysis using hospital administrative records.

5.2. Conclusions

This study showed that older age, disease severity, and undelaying conditions were associated factors with increased mortality risk in COVID‐19 patients. These results might help clinicians identify risk factors associated with the mortality and management of patients with COVID-19, especially in disadvantaged areas where there are few medical facilities available.

Acknowledgements

References

  • 1.

    Phelan AL, Katz R, Gostin LO. The Novel Coronavirus Originating in Wuhan, China: Challenges for Global Health Governance. JAMA. 2020;323(8):709-10. [PubMed ID: 31999307]. https://doi.org/10.1001/jama.2020.1097.

  • 2.

    Liu Y, Yan LM, Wan L, Xiang TX, Le A, Liu JM, et al. Viral dynamics in mild and severe cases of COVID-19. Lancet Infect Dis. 2020;20(6):656-7. [PubMed ID: 32199493]. [PubMed Central ID: PMC7158902]. https://doi.org/10.1016/S1473-3099(20)30232-2.

  • 3.

    Porcheddu R, Serra C, Kelvin D, Kelvin N, Rubino S. Similarity in Case Fatality Rates (CFR) of COVID-19/SARS-COV-2 in Italy and China. J Infect Dev Ctries. 2020;14(2):125-8. [PubMed ID: 32146445]. https://doi.org/10.3855/jidc.12600.

  • 4.

    Lipsitch M, Swerdlow DL, Finelli L. Defining the Epidemiology of Covid-19 - Studies Needed. N Engl J Med. 2020;382(13):1194-6. [PubMed ID: 32074416]. https://doi.org/10.1056/NEJMp2002125.

  • 5.

    Wang Y, Wang Y, Chen Y, Qin Q. Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. J Med Virol. 2020;92(6):568-76. [PubMed ID: 32134116]. [PubMed Central ID: PMC7228347]. https://doi.org/10.1002/jmv.25748.

  • 6.

    Onder G, Rezza G, Brusaferro S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA. 2020;323(18):1775-6. [PubMed ID: 32203977]. https://doi.org/10.1001/jama.2020.4683.

  • 7.

    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. Lancet. 2020;395(10229):1054-62. [PubMed ID: 32171076]. [PubMed Central ID: PMC7270627]. https://doi.org/10.1016/S0140-6736(20)30566-3.

  • 8.

    Hashemi-Shahri SM, Tabatabaei SMN, Ansari-Moghaddam A, Mohammadi M, Okati-Aliabad H, Tabatabaei SM, et al. Epidemiological and clinical risk factors related to severe COVID-19 in Iran: a multi-center study. BMC Infect Dis. 2022;22(1):184. [PubMed ID: 35197013]. [PubMed Central ID: PMC8864589]. https://doi.org/10.1186/s12879-022-07165-0.

  • 9.

    Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46(5):846-8. [PubMed ID: 32125452]. [PubMed Central ID: PMC7080116]. https://doi.org/10.1007/s00134-020-05991-x.

  • 10.

    World Health Organization. Clinical management of COVID-19: interim guidance, 27 May 2020. ‎2020. Available from: https://apps.who.int/iris/handle/10665/332196.

  • 11.

    Xie J, Covassin N, Fan Z, Singh P, Gao W, Li G, et al. Association Between Hypoxemia and Mortality in Patients With COVID-19. Mayo Clin Proc. 2020;95(6):1138-47. [PubMed ID: 32376101]. [PubMed Central ID: PMC7151468]. https://doi.org/10.1016/j.mayocp.2020.04.006.

  • 12.

    Kim L, Garg S, O'Halloran A, Whitaker M, Pham H, Anderson EJ, et al. Risk Factors for Intensive Care Unit Admission and In-hospital Mortality Among Hospitalized Adults Identified through the US Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET). Clin Infect Dis. 2021;72(9):e206-14. [PubMed ID: 32674114]. [PubMed Central ID: PMC7454425]. https://doi.org/10.1093/cid/ciaa1012.

  • 13.

    Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020;323(20):2052-9. [PubMed ID: 32320003]. [PubMed Central ID: PMC7177629]. https://doi.org/10.1001/jama.2020.6775.

  • 14.

    Sannigrahi S, Pilla F, Basu B, Basu AS, Molter A. Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustain Cities Soc. 2020;62:102418. [PubMed ID: 32834939]. [PubMed Central ID: PMC7395296]. https://doi.org/10.1016/j.scs.2020.102418.

  • 15.

    Sun H, Ning R, Tao Y, Yu C, Deng X, Zhao C, et al. Risk Factors for Mortality in 244 Older Adults with COVID‐19 in Wuhan, China: A Retrospective Study. J Am Geriatr Soc. 2020;68(6):E19-23. https://doi.org/10.1111/jgs.16533.

  • 16.

    Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934-43. [PubMed ID: 32167524]. [PubMed Central ID: PMC7070509]. https://doi.org/10.1001/jamainternmed.2020.0994.

  • 17.

    Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care. 2020;24(1):108. [PubMed ID: 32188484]. [PubMed Central ID: PMC7081524]. https://doi.org/10.1186/s13054-020-2833-7.

  • 18.

    The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) - China, 2020. China CDC Wkly. 2020;2(8):113-22. https://doi.org/10.46234/ccdcw2020.032.

  • 19.

    Guo TM, Tong Y, Chen J, Huang L, Cheng B, Zhoue J. Clinical Features Predicting Mortality Risk in Older Patients with COVID-19. 2020. Available from: https://ssrn.com/abstract=3569846.

  • 20.

    Kuroda N. Epilepsy and COVID-19: Associations and important considerations. Epilepsy Behav. 2020;108:107122. [PubMed ID: 32361677]. [PubMed Central ID: PMC7174174]. https://doi.org/10.1016/j.yebeh.2020.107122.

  • 21.

    Cabezudo-Garcia P, Ciano-Petersen NL, Mena-Vazquez N, Pons-Pons G, Castro-Sanchez MV, Serrano-Castro PJ. Incidence and case fatality rate of COVID-19 in patients with active epilepsy. Neurology. 2020;95(10):e1417-25. [PubMed ID: 32554773]. https://doi.org/10.1212/WNL.0000000000010033.

  • 22.

    Zhang J, Wang X, Jia X, Li J, Hu K, Chen G, et al. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect. 2020;26(6):767-72. [PubMed ID: 32304745]. [PubMed Central ID: PMC7159868]. https://doi.org/10.1016/j.cmi.2020.04.012.

  • 23.

    Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345-55. [PubMed ID: 32667669]. [PubMed Central ID: PMC7364371]. https://doi.org/10.1001/jamainternmed.2020.3539.

  • 24.

    Povoa HCC, Chianca GC, Iorio NLPP. COVID-19: An Alert to Ventilator-Associated Bacterial Pneumonia. Infect Dis Ther. 2020;9(3):417-20. [PubMed ID: 32474891]. [PubMed Central ID: PMC7260454]. https://doi.org/10.1007/s40121-020-00306-5.

  • 25.

    Klok FA, Kruip M, van der Meer NJM, Arbous MS, Gommers D, Kant KM, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res. 2020;191:145-7. [PubMed ID: 32291094]. [PubMed Central ID: PMC7146714]. https://doi.org/10.1016/j.thromres.2020.04.013.

  • 26.

    Middeldorp S, Coppens M, van Haaps TF, Foppen M, Vlaar AP, Muller MCA, et al. Incidence of venous thromboembolism in hospitalized patients with COVID-19. J Thromb Haemost. 2020;18(8):1995-2002. [PubMed ID: 32369666]. [PubMed Central ID: PMC7497052]. https://doi.org/10.1111/jth.14888.

  • 27.

    Hess DC, Eldahshan W, Rutkowski E. COVID-19-Related Stroke. Transl Stroke Res. 2020;11(3):322-5. [PubMed ID: 32378030]. [PubMed Central ID: PMC7202903]. https://doi.org/10.1007/s12975-020-00818-9.

  • 28.

    Li YC, Bai WZ, Hashikawa T. The neuroinvasive potential of SARS-CoV2 may play a role in the respiratory failure of COVID-19 patients. J Med Virol. 2020;92(6):552-5. [PubMed ID: 32104915]. [PubMed Central ID: PMC7228394]. https://doi.org/10.1002/jmv.25728.

  • 29.

    Giamarellos-Bourboulis EJ, Netea MG, Rovina N, Akinosoglou K, Antoniadou A, Antonakos N, et al. Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe. 2020;27(6):992-1000-e3. [PubMed ID: 32320677]. [PubMed Central ID: PMC7172841]. https://doi.org/10.1016/j.chom.2020.04.009.

  • 30.

    Spiezia L, Boscolo A, Poletto F, Cerruti L, Tiberio I, Campello E, et al. COVID-19-Related Severe Hypercoagulability in Patients Admitted to Intensive Care Unit for Acute Respiratory Failure. Thromb Haemost. 2020;120(6):998-1000. [PubMed ID: 32316063]. [PubMed Central ID: PMC7295272]. https://doi.org/10.1055/s-0040-1710018.

  • 31.

    Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844-7. [PubMed ID: 32073213]. [PubMed Central ID: PMC7166509]. https://doi.org/10.1111/jth.14768.

  • 32.

    Guo T, Fan Y, Chen M, Wu X, Zhang L, He T, et al. Cardiovascular Implications of Fatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020;5(7):811-8. [PubMed ID: 32219356]. [PubMed Central ID: PMC7101506]. https://doi.org/10.1001/jamacardio.2020.1017.

  • 33.

    Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol. 2020;5(7):802-10. [PubMed ID: 32211816]. [PubMed Central ID: PMC7097841]. https://doi.org/10.1001/jamacardio.2020.0950.

  • 34.

    Madjid M, Safavi-Naeini P, Solomon SD, Vardeny O. Potential Effects of Coronaviruses on the Cardiovascular System: A Review. JAMA Cardiol. 2020;5(7):831-40. [PubMed ID: 32219363]. https://doi.org/10.1001/jamacardio.2020.1286.

  • 35.

    Li J, Wang X, Chen J, Zuo X, Zhang H, Deng A. COVID-19 infection may cause ketosis and ketoacidosis. Diabetes Obes Metab. 2020;22(10):1935-41. [PubMed ID: 32314455]. [PubMed Central ID: PMC7264681]. https://doi.org/10.1111/dom.14057.

  • 36.

    Patel U, Malik P, Shah D, Patel A, Dhamoon M, Jani V. Pre-existing cerebrovascular disease and poor outcomes of COVID-19 hospitalized patients: a meta-analysis. J Neurol. 2021;268(1):240-7. [PubMed ID: 32770412]. [PubMed Central ID: PMC7414251]. https://doi.org/10.1007/s00415-020-10141-w.

  • 37.

    Yu JN, Wu BB, Yang J, Lei XL, Shen WQ. Cardio-Cerebrovascular Disease is Associated With Severity and Mortality of COVID-19: A Systematic Review and Meta-Analysis. Biol Res Nurs. 2021;23(2):258-69. [PubMed ID: 32851851]. [PubMed Central ID: PMC7481655]. https://doi.org/10.1177/1099800420951984.

  • 38.

    Pranata R, Huang I, Lim MA, Wahjoepramono EJ, July J. Impact of cerebrovascular and cardiovascular diseases on mortality and severity of COVID-19-systematic review, meta-analysis, and meta-regression. J Stroke Cerebrovasc Dis. 2020;29(8):104949. [PubMed ID: 32410807]. [PubMed Central ID: PMC7221373]. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.104949.

  • 39.

    Nordio M, Limido A, Maggiore U, Nichelatti M, Postorino M, Quintaliani G, et al. Survival in patients treated by long-term dialysis compared with the general population. Am J Kidney Dis. 2012;59(6):819-28. [PubMed ID: 22361043]. https://doi.org/10.1053/j.ajkd.2011.12.023.

  • 40.

    Kikuchi K, Nangaku M, Ryuzaki M; COVID-19 Task Force Committee of the Japanese Association of Dialysis Physicians; The Japanese Society for Dialysis Therapy; The Japanese Society of Nephrology, et al. COVID-19 of dialysis patients in Japan: Current status and guidance on preventive measures. Ther Apher Dial. 2020;24(4):361-5. [PubMed ID: 32506762]. [PubMed Central ID: PMC7301044]. https://doi.org/10.1111/1744-9987.13531.

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