Volume 9, Issue 4 (12-2021)
Jorjani Biomed J 2021, 9(4): 13-21 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
monfared A, moghadamnia M T, Karkhah S, maroufizadeh S, Asadian Rad M, kheirkhah J et al . A Survey for Predictors of Mortality among COVID-19 Patients: A Retrospective Study from Iran. Jorjani Biomed J 2021; 9 (4) :13-21
URL: http://goums.ac.ir/jorjanijournal/article-1-850-en.html
URL: http://goums.ac.ir/jorjanijournal/article-1-850-en.html
Arezoo Monfared1 
, Mohammad taghi Moghadamnia2 , Samad Karkhah3 , Saman Maroufizadeh4 , Mohammad Asadian Rad5 , Jalal Kheirkhah5 , Fatemeh Jafaraghayee 6
, Mohammad taghi Moghadamnia2 , Samad Karkhah3 , Saman Maroufizadeh4 , Mohammad Asadian Rad5 , Jalal Kheirkhah5 , Fatemeh Jafaraghayee 6
1- PhD Candidate, Department of Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
2- Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
3- Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran/Social Determinants of Health Research Center (SDHRC), Guilan University of Medical Sciences, Rasht, Iran/Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran
4- Department of Biostatistics, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
5- Department of Cardiovascular Medicine, Heshmat Heart Hospital, Guilan University of Medical Sciences, Guilan, Iran
6- Associate Professor, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran , fja_a80@yahoo.com
2- Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
3- Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran/Social Determinants of Health Research Center (SDHRC), Guilan University of Medical Sciences, Rasht, Iran/Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran
4- Department of Biostatistics, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran
5- Department of Cardiovascular Medicine, Heshmat Heart Hospital, Guilan University of Medical Sciences, Guilan, Iran
6- Associate Professor, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran , fja_a80@yahoo.com
Full-Text [PDF 596 kb]
(1310 Downloads)
| Abstract (HTML) (5155 Views)
Table 1. Demographic characteristics, clinical features, cardiac manifestations, and treatments of COVID-19 patients (N=522).
Discussion
Based on findings of the present study, older age, decreased O2 saturation, having a dysrhythmia, symptoms associated with heart failure, and mixed drug antiviruses were mortality predictor variables among COVID-19 patients.
In the present study, older age was mortality predictor variables among COVID-19 patients. Consistent with this finding, a cohort study in the USA found a direct and significant association between age and mortality among COVID-19 patients. They also found that patients over the age of 60 had a higher probability of mortality than others (11). Another study in China showed that old age is a risk factor for poor prognosis in COVID-19 patients (12). Also, the results of a study showed that age-based frailty is one of the important predictors of mortality and complications from COVID-19 (13). Obviously, age-dependent defects in immune cells occur with age, which has been proposed as a theory for higher mortality in older people. Also, the presence of various types of comorbidities in older people is another cause of this high mortality (12). Therefore, it is important to pay attention to the high-risk populations of COVID-19 patients due to the high probability of mortality. This pandemic has suggested that the management of vulnerable populations might be successful when data-driven policies and strategies are adopted.
Another predictor of mortality among COVID-19 patients was decreased O2 saturation. Consistent with this finding, studies from the USA (14) and Iran (15) showed that decreased O2 saturation is an important predictor of mortality among COVID-19 patients. Also, one study found that the mortality rate was higher among patients with O2 saturation less than 93% compared with O2 saturation above 93% (16). Therefore, due to the importance of assessing O2 saturation in predicting mortality among COVID-19 patients, it is suggested that patients with lower levels of O2 saturation receive special attention when receiving emergency care.
In this study, having a dysrhythmia was mortality predictor variables among COVID-19 patients. In this regard, the findings of a study in Germany showed that dysrhythmias were the most common cardiac complication among COVID-19 patients. On the other hand, the presence of dysrhythmias has been associated with increased length of stay in hospital and higher mortality among COVID-19 patients (17). On the other hand, some reports indicate a significant increase in QT interval by hydroxychloroquine, which is even greater when combined with azithromycin (18, 19). However, due to the discrepancy between these findings, dysrhythmias cannot be considered as the main cause of higher mortality among COVID-19 patients but can be confirmed as an effective factor. Therefore, further studies in this area are necessary.
Another predictor of mortality among COVID-19 patients was symptoms associated with heart failure. Consistent with this finding, a study showed that mortality was higher among COVID-19 patients with heart failure than in other patients (20). SARS-COV-2 Found inside macrophages and endothelial cells. In addition, evidence of active virus replication within the myocardium has been shown at autopsy. Thus, widespread inflammation and increased micro and macrovascular thrombosis may predispose to cardiac manifestations and myocarditis. However, the level of myocardial damage is reflected by an increase in troponin concentration, which has been associated with an increased risk of mortality among COVID-19 patients (21). In general, it is strongly recommended that patients with chronic or underlying diseases avoid any close contact with others in the community, especially in patients with COVID-19 due to the long incubation period of COVID-19 and asymptomatic carriers of COVID-19. Therefore, special attention to patients with underlying diseases during the COVID-19 pandemic is very important (22).
In the present study, mixed drug antiviruses were mortality predictor variables among COVID-19 patients. Consistent with this finding, the results of a systematic review and meta-analysis showed that the use of chloroquine and hydroxychloroquine for COVID-19 patients not only did not reduce mortality but also increased mortality when combined with azithromycin (23). In contrast, studies in Belgium (24) and the USA (25) have shown that treatment with hydroxychloroquine combined with azithromycin reduces mortality among COVID-19 patients. Therefore, due to the contradictions in this field, further studies are needed.
The present study has several limitations. Due to incomplete clinical records, there is no history of symptom changes that could limit our analysis. Also, the present study had a retrospective design in which the effect of treatments in patients is not reported. Additional large-scale studies are required to determine mortality among hospitalized COVID-19 patients with other associated comorbid conditions. It is suggested that researchers evaluate other predictors of mortality in COVID-19 patients in future studies, including laboratory tests.
Conclusion
Overall, special attention should be paid to the factors influencing the mortality of COVID-19 patients. It is recommended that older patients, dysrhythmia, and symptoms associated with heart failure be treated with extreme caution.
Acknowledgments
There was no financial support for this work. All authors declare no potential conflicts of interest relevant to this work.
Funding statement: There was no financial support for these studies.
Conflict of interest disclosure
The authors have no conflicts of interest to declare for this study.
Ethics approval statement
This study was approved by Guilan University of Medical Sciences (IR.GUMS.REC.1399.206). After obtaining permission from the hospital administration, the researchers visited the hospital.
Patient consent statement
The researchers explained the objectives of the present study to the participants and received informed consent from them.
How to cite:
Monfared A, Moghadamnia MT, Karkhah S, Maroufizadeh S, Asadian Rad M, kheirkhah J, Jafaraghaee F. A Survey for Predictors of Mortality among COVID-19 Patients: A Retrospective Study from Iran; Jorjani Biomedicine Journal. 2021; 9(4):13-21.
Full-Text: (721 Views)
Highlights
Introduction
On December 8, 2019, some hospitals reported cases of idiopathic pneumonia in Wuhan, Hubei Province, China, which caused concern among public health professionals about the spread of a new disease in the world (1). The spread of the virus via human-to-human transmission led to a global pandemic (2). This pandemic has led to a major global challenge that has imposed significant costs on countries' economies and health systems (3).
Based on the World Health Organization, a total of 182,101,209 confirmed cases of COVID-19 had been identified around the world by July 2, 2021, out of which 3,950,876 died. In Iran, a total of 3,204,557 confirmed cases of COVID-19 had been identified around the world by July 2, 2021, out of which 84,264 died (4). Epidemiological evidence shows that the main route of transmission of the virus is through coughing or sneezing from person to person (2, 5, 6). The main clinical manifestations of COVID-19 include fever, dyspnea, and cough (7). However, these manifestations are various in different populations (8, 5). Comorbid conditions such as diabetes mellitus, respiratory, cardiovascular, and renal diseases, obesity and an older age group are known to be positively associated with poor outcomes among COVID-19 patients. One of the major concerns in COVID-19 patients is the involvement of the cardiovascular system, leading to plaque instability, vascular and myocardial inflammation. On the other hand, a previous history of cardiovascular disease increases the risk of adverse outcomes such as plaque instability, vascular and myocardial inflammation among COVID_19 patients (9). However, so far there is no effective and definitive drug treatment for COVID-19 patients (10). To optimize patient care and appropriately deploy health care resources during this pandemic, effective patient risk stratification is essential. However, the factors that predispose an individual to a higher risk of death from COVID‐19 are poorly understood. To optimize patient care and appropriately deploy health care resources during this pandemic, effective patient risk stratification is essential.
Although the COVID-19 pandemic is still rampant and lots of information have been unfolding, narrow gaps in our understanding of the disease, and thus, aid decision-making by health care providers and administrators, more evidence is needed on the prevention, control, and treatment of COVID-19. Therefore, the present study aimed to assess of predictors of mortality among COVID-19 patients.
Materials and Methods
- Older age, decreased O2 saturation, having a dysrhythmia, symptoms associated with heart failure, and mixed drug antiviruses were mortality predictor variables among COVID-19 patients.
Introduction
On December 8, 2019, some hospitals reported cases of idiopathic pneumonia in Wuhan, Hubei Province, China, which caused concern among public health professionals about the spread of a new disease in the world (1). The spread of the virus via human-to-human transmission led to a global pandemic (2). This pandemic has led to a major global challenge that has imposed significant costs on countries' economies and health systems (3).
Based on the World Health Organization, a total of 182,101,209 confirmed cases of COVID-19 had been identified around the world by July 2, 2021, out of which 3,950,876 died. In Iran, a total of 3,204,557 confirmed cases of COVID-19 had been identified around the world by July 2, 2021, out of which 84,264 died (4). Epidemiological evidence shows that the main route of transmission of the virus is through coughing or sneezing from person to person (2, 5, 6). The main clinical manifestations of COVID-19 include fever, dyspnea, and cough (7). However, these manifestations are various in different populations (8, 5). Comorbid conditions such as diabetes mellitus, respiratory, cardiovascular, and renal diseases, obesity and an older age group are known to be positively associated with poor outcomes among COVID-19 patients. One of the major concerns in COVID-19 patients is the involvement of the cardiovascular system, leading to plaque instability, vascular and myocardial inflammation. On the other hand, a previous history of cardiovascular disease increases the risk of adverse outcomes such as plaque instability, vascular and myocardial inflammation among COVID_19 patients (9). However, so far there is no effective and definitive drug treatment for COVID-19 patients (10). To optimize patient care and appropriately deploy health care resources during this pandemic, effective patient risk stratification is essential. However, the factors that predispose an individual to a higher risk of death from COVID‐19 are poorly understood. To optimize patient care and appropriately deploy health care resources during this pandemic, effective patient risk stratification is essential.
Although the COVID-19 pandemic is still rampant and lots of information have been unfolding, narrow gaps in our understanding of the disease, and thus, aid decision-making by health care providers and administrators, more evidence is needed on the prevention, control, and treatment of COVID-19. Therefore, the present study aimed to assess of predictors of mortality among COVID-19 patients.
Materials and Methods
- Study design and subjects
Five hundred and twenty-two (291 males and 231 females) with a diagnosis of COVID-19, hospitalized in Razi hospital, Guilan Province, Iran, from March to August 2020, were enrolled in a retrospective study. This hospital was the main center for the treatment of COVID-19 patients in Guilan province. In this study, patients with a definitive diagnosis of COVID-19 via Reverse transcription-polymerase chain reaction (RT-PCR) were included. Also, patients with incomplete medical records, who have been hospitalized more than once, or are still hospitalized, were excluded from the study.
- Ethical consideration
This study was approved by Guilan University of Medical Sciences (IR.GUMS.REC.1399.206). After obtaining permission from the hospital administration, the researchers visited the hospital.
- Data collection
Demographic and clinical characteristics, including age, sex, marital status, addiction, smoking, alcoholism, history of hospitalization, number of comorbidities, cardiac diseases, systolic blood pressure [SBP], diastolic blood pressure [DBP], heart rate, O2 saturation, temperatures, respiration rate, cardiac manifestations (dysrhythmia, typical chest pain, pericardial effusion, inflammatory disorders of heart tissue, and symptoms associated with heart failure), treatments (antibiotics, antivirals, and bronchodilators), and clinical outcomes (survived and deceased) were collected.
- Statistical analysis
Data analysis was carried out using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA). Nominal variables were presented as numbers of cases and percentages, and continuous variables as median (interquartile range). Simple and Multiple logistic regression analysis (Enter method) were also applied to examine the relationships of clinical and demographic characteristics with in-hospital mortality. The odds ratio (OR) and 95% confidence interval (CI) were calculated. All statistical tests were two-sided and level of significance was set at 0.05.
Results
Results
- Participant’s characteristics
The demographic characteristics, clinical features, cardiac manifestations, and treatments of the 522 study subjects were provided in Table 1. Of the survived patients, 51.8% were male, 15.4% were smokers, 11.7% were opium addicts, 40.1% had 2 or more than 2 comorbidities, 30.4% had cardiac disease, and 11.3% had dysrhythmia. Of the deceased patients, 59.3% were male, 6.5% were smokers, 7.3% were opium addicts, 35.3% had 2 or more than 2 comorbidities, 20.7% had cardiac disease, and 25.1% had dysrhythmia. The median age in the deceased group was greater than in the survived group (65.0 years vs. 58.0 years).
- Mortality predictor variables among COVID-19 patients
As presented in Table 2, simple logistic regression showed that older age (OR= 1.03, 95%CI: 1.02 to 1.05, P <0.001), decreased O2 saturation (OR= 0.89, 95%CI= 0.87 to 0.92, P <0.001), having a dysrhythmia (OR= 2.62, 95%CI: 1.62 to 4.23, P <0.001), symptoms associated with heart failure (OR= 0.50, 95%CI: 0.27 to 0.90, P =0.022), and mixed drug antiviruses (OR= 2.05, 95%CI: 1.24 to 3.40, P =0.005) were important mortality predictor variables among COVID-19 patients. Older age increases the chance of mortality by 0.03. Decreased O2 saturation increases the probability of death by 0.11. Having a dysrhythmia increases 1.62 chance of death. Having symptoms associated with heart failure reduces the chance of death by 0.50. Also, mixed drug antiviruses increase the 1.05 chance of death
Multiple logistic regression showed that older age (aOR= 1.04, 95%CI: 1.02 to 1.06, P <0.001), decreased O2 saturation (aOR= 0.89, 95%CI: 0.86 to 0.92, P <0.001), having a dysrhythmia (aOR= 2.97, 95%CI: 1.46 to 6.05, P =0.003), symptoms associated with heart failure (aOR= 0.43, 95%CI: 0.18 to 0.99, P =0.048), and mixed drug antiviruses (aOR= 2.44, 95%CI: 1.22 to 4.90, P =0.012) were mortality predictor variables among COVID-19 patients. Older age increases the chance of mortality by 0.04. Decreased O2 saturation increases the probability of death by 0.11. Having a dysrhythmia increases 1.97 chance of death. Having symptoms associated with heart failure reduces the chance of death by 0.57. Also, mixed drug antiviruses increase the 1.44 chance of death (Table 2).
Multiple logistic regression showed that older age (aOR= 1.04, 95%CI: 1.02 to 1.06, P <0.001), decreased O2 saturation (aOR= 0.89, 95%CI: 0.86 to 0.92, P <0.001), having a dysrhythmia (aOR= 2.97, 95%CI: 1.46 to 6.05, P =0.003), symptoms associated with heart failure (aOR= 0.43, 95%CI: 0.18 to 0.99, P =0.048), and mixed drug antiviruses (aOR= 2.44, 95%CI: 1.22 to 4.90, P =0.012) were mortality predictor variables among COVID-19 patients. Older age increases the chance of mortality by 0.04. Decreased O2 saturation increases the probability of death by 0.11. Having a dysrhythmia increases 1.97 chance of death. Having symptoms associated with heart failure reduces the chance of death by 0.57. Also, mixed drug antiviruses increase the 1.44 chance of death (Table 2).
Table 1. Demographic characteristics, clinical features, cardiac manifestations, and treatments of COVID-19 patients (N=522).
| Survived (n=247) | Deceased (n=275) | |
| Demographic characteristics | ||
| Age | 58.0 (45.0 – 70.0) | 65.0 (56.0 – 76.0) |
| Male sex | 128 (51.8) | 163 (59.3) |
| Marital Status (married) | 236 (95.5) | 273 (99.3) |
| Smoking | 38 (15.4) | 18 (6.5) |
| Alcohol | 5 (2.0) | 2 (0.7) |
| Opium | 29 (11.7) | 20 (7.3) |
| Clinical features | ||
| History of hospitalization | 43 (17.4) | 56 (20.4) |
| No. of comorbidities | ||
| 0 | 61 (24.7) | 89 (32.4) |
| 1 | 87 (35.2) | 89 (32.4) |
| ≥2 | 99 (40.1) | 97 (35.3) |
| Cardiac diseases | 75 (30.4) | 57 (20.7) |
| SBP (mmHg) | 120.0 (110.0 – 130.0) | 110.0 (100.0 – 130.0) |
| DBP (mmHg) | 70.0 (60.0 – 80.0) | 70.0 (60.0 – 80.0) |
| Heart rate (beats per minute) | 82.0 (78.0 – 90.0) | 84.0 (79.0 – 90.0) |
| O2 Saturation | 92.0 (90.0 – 96.0) | 84.5 (70.2 – 92.0) |
| Temperatures (°C) | 37.0 (37.0 – 38.0) | 37.0 (37.0 – 37.8) |
| Respiration rate | 18.0 (16.0 – 20.0) | 20.0 (18.0 – 22.0) |
| Cardiac manifestations | ||
| Dysrhythmia | 28 (11.3) | 69 (25.1) |
| Typical chest pain | 6 (2.4) | 3 (1.1) |
| Pericardial effusion | 4 (1.6) | 2 (0.7) |
| Inflammatory disorders of heart tissue | 1 (0.4) | 2 (0.7) |
| Symptoms associated with heart failure | 32 (13.0) | 19 (6.9) |
| Treatments | ||
| Antibiotic drug | ||
| No | 32 (13.0) | 55 (20.0) |
| Single | 109 (44.1) | 78 (28.4) |
| Mixed | 106 (42.9) | 142 (51.6) |
| Antiviral drugs | ||
| No | 42 (17.0) | 51 (18.5) |
| Single | 144 (58.3) | 72 (26.2) |
| Mixed | 61 (24.7) | 152 (55.3) |
| Bronchodilator drugs | 104 (42.1) | 129 (46.9) |
SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure.
Values are given as number (percentage) for categorical variables and as median (interquartile range) for continuous variables.
Table 2. Mortality predictor variables among COVID-19 patients (N=522).
Values are given as number (percentage) for categorical variables and as median (interquartile range) for continuous variables.
Table 2. Mortality predictor variables among COVID-19 patients (N=522).
| Simple Logistic Regression | Simple Logistic Regression | |||
| OR (95% CI) | p | aOR (95% CI) | p | |
| Demographic characteristics | ||||
| Age | 1.03 (1.02 – 1.05) | <0.001 | 1.04 (1.02 – 1.06) | <0.001 |
| Male sex | 1.35 (0.96 – 1.91) | 0.087 | 1.55 (0.95 – 2.51) | 0.077 |
| Marital Status (married vs single) | 6.36 (1.40 – 28.99) | 0.017 | 3.60 (0.41 – 31.60) | 0.248 |
| Current smoker | 0.39 (0.21 – 0.69) | 0.002 | 0.56 (0.21 – 1.47) | 0.239 |
| Alcohol drinker | 0.35 (0.07 – 1.84) | 0.218 | 2.45 (0.33 – 18.26) | 0.383 |
| Opium user | 0.59 (0.32 – 1.07) | 0.083 | 0.66 (0.25 – 1.72) | 0.066 |
| Clinical features | ||||
| History of hospitalization | 1.21 (0.78 – 1.89) | 0.390 | 1.13 (0.64 – 2.00) | 0.682 |
| No. of comorbidities | ||||
| 0 | 1 | 1 | ||
| 1 | 0.70 (0.45 – 1.09) | 0.114 | 0.79 (0.43 – 1.46) | 0.456 |
| ≥2 | 0.67 (0.44 – 1.03) | 0.069 | 0.72 (0.39 – 1.33) | 0.295 |
| SBP (mmHg) | 0.99 (0.98 – 1.00) | 0.007 | 0.99 (0.98 – 1.01) | 0.360 |
| DBP (mmHg) | 0.98 (0.96 – 0.99) | 0.002 | 0.99 (0.96 – 1.02) | 0.430 |
| Heart rate (beats per minute) | 1.02 (1.00 – 1.03) | 0.007 | 1.01 (0.99 – 1.02) | 0.479 |
| O2 Saturation | 0.89 (0.87 – 0.92) | <0.001 | 0.89 (0.86 – 0.92) | <0.001 |
| Temperatures (°C) | 0.82 (0.65 – 1.04) | 0.100 | 0.98 (0.72 – 1.35) | 0.915 |
| Respiration rate | 1.11 (1.06 – 1.16) | <0.001 | 1.02 (0.95 – 1.08) | 0.635 |
| Cardiac manifestations | ||||
| Dysrhythmia | 2.62 (1.62 – 4.23) | <0.001 | 2.97 (1.46 – 6.05) | 0.003 |
| Typical chest pain | 0.44 (0.11 – 1.79) | 0.253 | 0.43 (0.07 – 2.64) | 0.363 |
| Pericardial effusion | 0.45 (0.08 – 2.45) | 0.352 | 1.14 (0.16 – 8.08) | 0.893 |
| Inflammatory disorders of heart tissue | 1.80 (0.16 – 20.00) | 0.631 | 1.59 (0.09 – 26.83) | 0.750 |
| Symptoms associated with heart failure | 0.50 (0.27 – 0.90) | 0.022 | 0.43 (0.18 – 0.99) | 0.048 |
| Treatments | ||||
| Antibiotic drug | ||||
| No | 1 | 1 | ||
| Single | 0.42 (0.25 – 0.70) | 0.001 | 0.55 (0.27 – 1.11) | 0.094 |
| Mixed | 0.78 (0.47 – 1.29) | 0.332 | 1.34 (0.68 – 2.64) | 0.401 |
| Antiviral drugs | ||||
| No | 1 | 1 | ||
| Single | 0.41 (0.25 – 0.68) | <0.001 | 0.80 (0.41 – 1.56) | 0.510 |
| Mixed | 2.05 (1.24 – 3.40) | 0.005 | 2.44 (1.22 – 4.90) | 0.012 |
| Bronchodilator drugs | 1.21 (0.86 – 1.72) | 0.271 | 1.10 (0.69 – 1.77) | 0.691 |
OR: Odds Ratio; aOR: Adjusted Odds Ratio; CI: Confidence Interval; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure.
Discussion
Based on findings of the present study, older age, decreased O2 saturation, having a dysrhythmia, symptoms associated with heart failure, and mixed drug antiviruses were mortality predictor variables among COVID-19 patients.
In the present study, older age was mortality predictor variables among COVID-19 patients. Consistent with this finding, a cohort study in the USA found a direct and significant association between age and mortality among COVID-19 patients. They also found that patients over the age of 60 had a higher probability of mortality than others (11). Another study in China showed that old age is a risk factor for poor prognosis in COVID-19 patients (12). Also, the results of a study showed that age-based frailty is one of the important predictors of mortality and complications from COVID-19 (13). Obviously, age-dependent defects in immune cells occur with age, which has been proposed as a theory for higher mortality in older people. Also, the presence of various types of comorbidities in older people is another cause of this high mortality (12). Therefore, it is important to pay attention to the high-risk populations of COVID-19 patients due to the high probability of mortality. This pandemic has suggested that the management of vulnerable populations might be successful when data-driven policies and strategies are adopted.
Another predictor of mortality among COVID-19 patients was decreased O2 saturation. Consistent with this finding, studies from the USA (14) and Iran (15) showed that decreased O2 saturation is an important predictor of mortality among COVID-19 patients. Also, one study found that the mortality rate was higher among patients with O2 saturation less than 93% compared with O2 saturation above 93% (16). Therefore, due to the importance of assessing O2 saturation in predicting mortality among COVID-19 patients, it is suggested that patients with lower levels of O2 saturation receive special attention when receiving emergency care.
In this study, having a dysrhythmia was mortality predictor variables among COVID-19 patients. In this regard, the findings of a study in Germany showed that dysrhythmias were the most common cardiac complication among COVID-19 patients. On the other hand, the presence of dysrhythmias has been associated with increased length of stay in hospital and higher mortality among COVID-19 patients (17). On the other hand, some reports indicate a significant increase in QT interval by hydroxychloroquine, which is even greater when combined with azithromycin (18, 19). However, due to the discrepancy between these findings, dysrhythmias cannot be considered as the main cause of higher mortality among COVID-19 patients but can be confirmed as an effective factor. Therefore, further studies in this area are necessary.
Another predictor of mortality among COVID-19 patients was symptoms associated with heart failure. Consistent with this finding, a study showed that mortality was higher among COVID-19 patients with heart failure than in other patients (20). SARS-COV-2 Found inside macrophages and endothelial cells. In addition, evidence of active virus replication within the myocardium has been shown at autopsy. Thus, widespread inflammation and increased micro and macrovascular thrombosis may predispose to cardiac manifestations and myocarditis. However, the level of myocardial damage is reflected by an increase in troponin concentration, which has been associated with an increased risk of mortality among COVID-19 patients (21). In general, it is strongly recommended that patients with chronic or underlying diseases avoid any close contact with others in the community, especially in patients with COVID-19 due to the long incubation period of COVID-19 and asymptomatic carriers of COVID-19. Therefore, special attention to patients with underlying diseases during the COVID-19 pandemic is very important (22).
In the present study, mixed drug antiviruses were mortality predictor variables among COVID-19 patients. Consistent with this finding, the results of a systematic review and meta-analysis showed that the use of chloroquine and hydroxychloroquine for COVID-19 patients not only did not reduce mortality but also increased mortality when combined with azithromycin (23). In contrast, studies in Belgium (24) and the USA (25) have shown that treatment with hydroxychloroquine combined with azithromycin reduces mortality among COVID-19 patients. Therefore, due to the contradictions in this field, further studies are needed.
The present study has several limitations. Due to incomplete clinical records, there is no history of symptom changes that could limit our analysis. Also, the present study had a retrospective design in which the effect of treatments in patients is not reported. Additional large-scale studies are required to determine mortality among hospitalized COVID-19 patients with other associated comorbid conditions. It is suggested that researchers evaluate other predictors of mortality in COVID-19 patients in future studies, including laboratory tests.
Conclusion
Overall, special attention should be paid to the factors influencing the mortality of COVID-19 patients. It is recommended that older patients, dysrhythmia, and symptoms associated with heart failure be treated with extreme caution.
Acknowledgments
There was no financial support for this work. All authors declare no potential conflicts of interest relevant to this work.
Funding statement: There was no financial support for these studies.
Conflict of interest disclosure
The authors have no conflicts of interest to declare for this study.
Ethics approval statement
This study was approved by Guilan University of Medical Sciences (IR.GUMS.REC.1399.206). After obtaining permission from the hospital administration, the researchers visited the hospital.
Patient consent statement
The researchers explained the objectives of the present study to the participants and received informed consent from them.
How to cite:
Monfared A, Moghadamnia MT, Karkhah S, Maroufizadeh S, Asadian Rad M, kheirkhah J, Jafaraghaee F. A Survey for Predictors of Mortality among COVID-19 Patients: A Retrospective Study from Iran; Jorjani Biomedicine Journal. 2021; 9(4):13-21.
Type of Article: Original article |
Subject:
General medicine
Received: 2021/08/14 | Accepted: 2021/11/6 | Published: 2021/12/28
Received: 2021/08/14 | Accepted: 2021/11/6 | Published: 2021/12/28
References
1. Singh R, Kang A, Luo X, Jeyanathan M, Gillgrass A, Afkhami S, et al. COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development. FASEB J 2021;35(3):e21409. [view at publisher] [DOI] [Google Scholar]
2. Khaneshpour H, Pashazadeh M, Nasiri R. Coronavirus Disease 2019 (COVID-19): review study. Jorjani Biomed J 2020;8(1):4-10. [view at publisher] [DOI] [Google Scholar]
3. Banerjee A, Pasea L, Harris S, Gonzalez-Izquierdo A, Torralbo A, Shallcross L, et al. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet 2020;395(10238):1715-1725. [view at publisher] [DOI] [Google Scholar]
4. World Health Organization. Coronavirus disease (COVID-19) weekly epidemiological update and weekly operational update [cited 2021 July 2]. [Google Scholar]
5. Karkhah S, Ghazanfari MJ, Shamshirian A, Panahi L, Molai M, Emami Zeydi A. Clinical features of patients with probable 2019 novel coronavirus infected pneumonia in Rasht, Iran: A retrospective case series. Open Access Maced J Med Sci 2020;8(T1):16-22. [view at publisher] [DOI] [Google Scholar]
6. Emami Zeydi A, Ghazanfari MJ, Panahi R, Mortazavi H, Karimifar K, Karkhah S, et al. Coronavirus disease 2019 (COVID-19): A literature review from a nursing perspective. BioMedicine 2021;11(3):5-14. [view at publisher] [DOI] [Google Scholar]
7. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323(11):1061-1069. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
8. Jiang F, Deng L, Zhang L, Cai Y, Cheung CW, Xia Z. Review of the clinical characteristics of coronavirus disease 2019 (COVID-19). J Gen Intern Med 2020;35(5):1545-1549. [DOI] [PMID] [PMCID] [Google Scholar]
9. Shafi AM, Shaikh SA, Shirke MM, Iddawela S, Harky A. Cardiac manifestations in COVID‐19 patients-A systematic review. J Card Surg 2020;35(8):1988-2008. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
10. Zheng SQ, Yang L, Zhou PX, Li HB, Liu F, Zhao RS. Recommendations and guidance for providing pharmaceutical care services during COVID-19 pandemic: a China perspective. Res Social Adm Pharm 2021;17(1):1819-1824. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
11. Imam Z, Odish F, Gill I, O'Connor D, Armstrong J, Vanood A, et al. Older age and comorbidity are independent mortality predictors in a large cohort of 1305 COVID‐19 patients in Michigan, United States. J Intern Med 2020;288(4):469-476. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
12. 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):1054-1062. [view at publisher] [DOI] [Google Scholar]
13. Clark A, Jit M, Warren-Gash C, Guthrie B, Wang HHX, Mercer SW, et al. Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study. Lancet Glob Health 2020;8(8):e1003-e1017. [view at publisher] [Google Scholar]
14. Rapp JL, Lieberman-Cribbin W, Tuminello S, Taioli E. Male sex, severe obesity, older age, and chronic kidney disease are associated with COVID-19 severity and mortality in New York City. Chest 2021;159(1):112-115. [DOI] [PMID] [PMCID] [Google Scholar]
15. Niaee MS, Namdar P, Allami A, Zolghadr L, Javadi A, Karampour A, et al. Ivermectin as an adjunct treatment for hospitalized adult COVID-19 patients: A randomized multi-center clinical trial. Asian Pac J Trop Med 2021;14(6):266. [view at publisher] [DOI] [Google Scholar]
16. Abolghasemi S, Mardani M, Sali S, Honarvar N, Baziboroun M. COVID‐19 and kidney transplant recipients. Transpl Infect Dis 2020;22(6):e13413. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
17. Zylla MM, Merle U, Vey JA, Korosoglou G, Hofmann E, Müller M, et al. Predictors and prognostic implications of cardiac arrhythmias in patients hospitalized for COVID-19. J Clin Med 2021;10(1):133. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
18. Hsia BC, Greige N, Quiroz JA, Khokhar AS, Daily J, Di Biase L, et al. QT prolongation in a diverse, urban population of COVID-19 patients treated with hydroxychloroquine, chloroquine, or azithromycin. J Interv Card Electrophysiol 2020;59(2):337-345. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
19. Samuel S, Friedman RA, Sharma C, Ganigara M, Mitchell E, Schleien C, et al. Incidence of arrhythmias and electrocardiographic abnormalities in symptomatic pediatric patients with PCR-positive SARS-CoV-2 infection, including drug-induced changes in the corrected QT interval. Heart Rhythm 2020;17(11):1960-1966. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
20. Alvarez-Garcia J, Lee S, Gupta A, Cagliostro M, Joshi AA, Rivas-Lasarte M, et al. Prognostic impact of prior heart failure in patients hospitalized with COVID-19. J Am Coll Cardiol 2020;76(20):2334-2348. [DOI] [PMID] [PMCID] [Google Scholar]
21. Lala A, Johnson KW, Januzzi JL, Russak AJ, Paranjpe I, Richter F, et al. Prevalence and impact of myocardial injury in patients hospitalized with COVID-19 infection. J Am Coll Cardiol 2020;76(5):533-546. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
22. Emami A, Javanmardi F, Pirbonyeh N, Akbari A. Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis. Arch Acad Emerg Med 2020;8(1):e35-e. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
23. Ghazy RM, Almaghraby A, Shaaban R, Kamal A, Beshir H, Moursi A, et al. A systematic review and meta-analysis on chloroquine and hydroxychloroquine as monotherapy or combined with azithromycin in COVID-19 treatment. Sci Rep 2020;10(1):1-18. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
24. Catteau L, Dauby N, Montourcy M, Bottieau E, Hautekiet J, Goetghebeur E, et al. Low-dose hydroxychloroquine therapy and mortality in hospitalised patients with COVID-19: a nationwide observational study of 8075 participants. Int J Antimicrob Agents 2020;56(4):106144. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
25. Arshad S, Kilgore P, Chaudhry ZS, Jacobsen G, Wang DD, Huitsing K, et al. Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19. Int J Infect Dis 2020;97:396-403. [view at publisher] [DOI] [PMID] [PMCID] [Google Scholar]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
