A Comparison of the Prognosis of SARS-CoV-2 Viral Infection in Patients with and Without Underlying Heart Disease


avatar Hamid Khederlou ORCID 1 , avatar Vida Ebrahimi 2 , avatar Ahmadreza Rasouli 3 , 4 , avatar Maryam Mehrpooya ORCID 5 , *

Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
Department of Pharmaceutical Biotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Department of Nutrition, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran
Student Research Committee, School of Health, Qazvin University of Medical Sciences, Qazvin, Iran
Department of Interventional Cardiology, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran

how to cite: Khederlou H, Ebrahimi V, Rasouli A, Mehrpooya M. A Comparison of the Prognosis of SARS-CoV-2 Viral Infection in Patients with and Without Underlying Heart Disease. Int J Cardiovasc Pract. 2021;6(2):e132258. https://doi.org/10.5812/intjcardiovascpract-132258.



Since December 2019, a pneumonia outbreak has spread through a viral infection. Originally, the virus pathogen was termed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). SARS-CoV-2 can cause severe and life-threatening problems.


The purpose of this study was to describe the prognostic comparison of SARS-CoV-2 viral infection in patients with and without underlying heart disease in our center.


A total of 400 Iranian adult patients with COVID-19 enrolled in this descriptive-analytical research. The study population was categorized into 4 groups: (1) no underlying disorder, (2) only cardiovascular complication, (3) only non-cardiovascular disease (CVD), and (4) cardiovascular and non-cardiovascular complications. The prognosis was adjudicated based on underlying diseases. Anthropometric details were also gathered. Finally, the data were analyzed using SPSS version 25.


The mortality rate was almost 4.17 times more in patients suffering from both cardiovascular and non-cardiovascular complications compared with the first group having no underlying disorders. This rate was 3.98 times more than the baseline in patients complicated with CVD alone. Based on a logistic regression model, COVID-19 prognosis was associated with age, underlying disorder type, length of hospitalization, the requirement for the intensive care unit (ICU), length of stay in ICU, the need for intubation, reservoir bag-mask requirement, and some of the life-threatening complications such as pneumonia and sepsis.


Cardiovascular underlying disorders (such as myocarditis and deep vein thrombosis) are likely to be linked to the prognosis of COVID-19.

1. Background

The global outbreak of SARS-CoV-2 viral infection (COVID-19 pandemic) had devastating health, social, and economic effects (1-3). The first case was reported on 8 December 2019 in Wuhan City, Hubei Province, China. On 11 March 2020, it was considered a pandemic by the World Health Organization (WHO).

With the increasing spread all over the world, a worse fate awaited people with various underlying diseases. Two-thirds of patients with other comorbid conditions needed intensive care unit (ICU) admission or died (4). Meanwhile, patients with cardiovascular disease (CVD) have a worse prognosis (5). It appears that there is sophisticated interaction between COVID-19 and cardiovascular system. In fact, cardiovascular risk factors (such as diabetes mellitus, systemic hypertension, and CVD) were preexisting findings in patients with COVID-19 infection (6-9). In hospitalized patients, lines of evidence of myocardial injury have been reported in 20 - 40% of cases (6, 10). In addition, acute coronary syndrome, venous thromboembolism, and arrhythmias may occur in victims (11). Regarding the prognosis of these patients, myocardial damage is associated with a remarkably worse prognosis (12). Proper and timely assessment of the viral-induced heart damage by biomarkers of high-sensitivity cardiac troponin I (cTnI) and NT-proBNP is mandatory for early identification of this complication, which may predict the prognosis of affected patients (5).

2. Objectives

The purpose of this study was to describe the prognostic comparison of SARS-CoV-2 viral infection in patients with and without underlying heart disease in our center.

3. Methods

This descriptive-analytical study was conducted from April to June 2020 at Imam Khomeini Hospital of Tehran University of Medical Science with the ethical code of IR.TUMS.VCR.REC.1399.355. In this study, 549 patients hospitalized with the initial diagnosis of SARS-CoV-2 were examined. After obtaining written consent, the patients with relevant symptoms, lung computed tomography (CT) scan, and positive polymerase chain reaction (PCR) test consistent with SARS-CoV-2 infection were enrolled in the study. Patients who did not consent to participate in the study or were discharged during hospitalization with personal consent were excluded. Finally, 400 diagnosed patients with SARS-CoV-2 infection were enrolled in the study and divided into 4 groups: (1) without any underlying disease (n = 100), (2) with a history of CVD alone without other underlying diseases (n = 100), (3) a history of underlying diseases, except CVD (n = 100), (4) with both previous CVD and other underlying non-CVDs (n = 100).

The checklist (including demographic and anthropometric information of patients) was completed. Patient prognosis as the length of hospitalization (in the ward or ICU), appropriate response to treatment based on the percentage of blood oxygen saturation (Spo2), the requirement for supplemental oxygen (with reserve bag mask), transferred or not transferred to ICU intubation, life-threatening complication (acute kidney injury [acute kidney injury (AKI), pulmonary thromboembolism (PTE), acute myocardial infarction (AMI)) and its type, and death or discharge were registered and recorded in the checklist.

Finally, the obtained data were assessed using SPSS version 25 (SPSS Inc, Chicago, Ill, USA), and the relationship between SARS-CoV-2 viral infection in patients with and without underlying heart disease was analyzed. P-values less than 0.05 were considered statistically significant.

4. Results

A total of 400 SARS-CoV-2 patients agreed to participate in the study. Patients were divided into 4 groups as without the underlying disease (n = 100), with a previous CVD (n = 100), non-CVD (n = 100), with both previous CVD and non-CVD (n = 100). The frequency of socio-demographic and anthropometric characteristics of patients is displayed in Table 1 by groups.

Table 1. The Frequency (Percentage) of Socio-demographic and Anthropometric Characteristics of Patients
VariablesThe Type of Underlying DiseaseTotal
Group 1 a (n = 100)Group 2 b (n = 100)Group 3 c (n = 100)Group 4 d (n = 100)
Female27483335143 (35.8)
Male73526765257 (64.2)
Hospitalization in the ICU
No94807572321 (80.3)
Yes620252879 (19.7)
Reservoir bag mask
No72513641200 (50)
Yes28496459200 (50)
No97909086363 (90.8)
Yes310101437 (9.2)
Type of life-threatening complication
Pneumonia713181856 (14)
ARDS167418 (4.5)
Sepsis0351220 (5)
AKI3951431 (7.8)
Myocarditis11057 (1.8)
DVT424515 (3.8)
Seizure00202 (0.5)
QT01337 (1.8)
Myocardial infarction10102 (0.5)
Heart failure00494 (1)
Pyelonephritis00101 (0.25)
No85736064282 (70.5)

Further, 64.2% (257) of patients were male, and 19.7% (79) of patients required ICU admission. Half of the patients needed the reservoir bag mask. In 9.2% of patients, intubation was performed. During hospitalization, the patients developed pneumonia (14%), AKI (7.8%), sepsis (5%), acute respiratory distress syndrome (ARDS; 4.5%), deep vein thrombosis (DVT; 3.8%), myocarditis (1.8%), QT prolongation (1.8%), heart failure (1%), seizure (0.5%), myocardial infarction (0.5%), and pyelonephritis (0.25%).

The means and SDs of age, length of hospitalization, and length of hospitalization in ICU variables are presented in Table 2 separately for each group.

Table 2. The Means and SDs of Age, Length of Hospitalization, and Length of Hospitalization in the Intensive Care Unit
VariablesThe Type of Underlying DiseaseTotalP-Value
Group 1 (n = 100)Group 2 (n = 100)Group 3 (n = 100)Group 4 (n = 100)
Age a50.92 ± 15.6953.87 ± 14.7663.46 ± 13.7963.80 ± 12.8758.01 ± 15.36< 0.001
Length of hospitalization b4.17 ± 3.255.53 ± 4.856.12 ± 4.237.69 ± 7.965.88 ± 5.50< 0.001
Length of hospitalization in the ICU44.83 ± 3.977.05 ± 4.574.88 ± 3.0710.89 ± 9.767.56 ± 6.990.027

The total mean age was 58.01 ± 15.36 years. On average, each patient was hospitalized for 5.88 days. In patients who transferred to the ICU, the mean length of hospitalization was 7.56 days. The mean differences in age, length of hospitalization, and length of hospitalization in the ICU variables were statistically significant between the study groups. Multiple comparisons of these variables are displayed in Table 3. The highest mean difference in the age variable is between group 1 and group 4 (P < 0.001), and the lowest mean difference was between group 3 and group 4. In the length of hospitalization variable, the mean difference between group 1 and groups 3 and 4 was statistically significant. The mean length of hospitalization in the ICU in group 4 was 6 days longer than group 3, and this difference was statistically significant.

Table 3. Multiple Comparisons of Age, Length of Hospitalization, and Length of Hospitalization in the Intensive Care Unit Variables
VariablesIJMean Difference (I-J)SEP-Value
Age aGroup 4Group 112.882.025< 0.001
Group 29.932.025< 0.001
Group 30.342.0250.998
Group 3Group 112.542.025< 0.001
Group 29.592.025< 0.001
Group 2Group 12.952.0250.465
Length of hospitalization Group 4Group 13.520.760< 0.001
Group 22.160.7600.193
Group 31.570.760> 0.999
Group 3Group 11.950.7600.001
Group 20.590.7600.344
Group 2Group 11.360.7600.359
Length of hospitalization in the ICU2 bGroup 4Group 16.062.9740.068
Group 23.841.9350.309
Group 36.011.8190.005
Group 3Group 10.0473.005> 0.999
Group 2-2.171.9830.114
Group 2Group 12.213.077> 0.999

Figure 1 shows the number of deaths and discharges in each group.

The association of final outcomes and socio-demographic and anthropometric features was analyzed using a logistic regression model (Table 4), with a univariable model as the crude odds ratio (OR) and a forward selection model as the adjusted OR.

Table 4. Factors Associated with COVID-19 Patients’ Final Outcomes
VariablesCrude OR (95% CI)P-ValueAdjusted OR (95% CI)P-Value
Age1.038 (1.017 - 1.060)< 0.0011.048 (1.008 - 1.09)0.019
Length of hospitalization1.112 (1.062 - .166)< 0.0010.812 (0.726 - 0.907)< 0.001
Length of hospitalization in the ICU1.287 (1.192 - 1.391)< 0.001--
The type of underlying disease
Group 1Base---
Group 22.348 (0.785 - 7.026)0.127--
Group 33.982 (1.376 - 11.007)0.010--
Group 44.171 (1.483 - 11.728)0.007--
Male0.928 (0.505 - 1.706)0.810--
Hospitalization in the ICU
Yes46.733(20.38 - 107.14)< 0.00119.33 (5.26 - 71.01)< 0.001
Reservoir bag mask
Yes20.737 (6.33 - 67.85)< 0.001--
Yes88.60 (33.13 - 236.94)< 0.00172.43 (12.66 - 414.22)< 0.001
Type of life-threatening complication
Pneumonia9.86 (5.09 - 19.11)< 0.00113.29 (3.61 - 48.91)< 0.001
ARDS8.09 (3.04 - 21.52)< 0.001--
Sepsis13.11 (5.05 - 34.04)< 0.001--
AKI2.15 (0.87 - 5.29)0.094--
Myocarditis9.81 (2.13 - 45.22)0.00394.68 (6.74 - 1329.04)< 0.001
DVT6.78 (2.34 - 19.61)< 0.001--
Seizure6.96 (0.43 - 113.04)0.173--
QT9.81 (2.13 - 45.22)0.003104.48 (13.12 - 832.15)< 0.001
Myocardial infarction0NA--
Heart failure2.3 (0.235 - 22.60)0.473--

The risk of death was higher in elderly patients than in younger patients (crude OR = 1.038 and adjusted OR = 1.048). Mortality was lower in patients with long hospitalization (adjusted OR = 0.812). Mortality in the underlying non-CVD and CVD group was 4.17 (crude OR) times higher than the group without the disease. This rate was 3.98 (crude OR) times higher in the underlying CVD group than in the baseline. The adjusted OR of patients transferred to the ICU was 19.33. Also, the adjusted OR for patients who were intubated was 72.43. The mortality rate for patients who got pneumonia during hospitalization was 13.29 times higher. The mortality rate for patients who got pneumonia, myocarditis, and QT during hospitalization was 13.29, 94.68, and 104.48 times higher than the baseline, respectively.

5. Discussion

The present study is a descriptive-analytical investigation of patients with COVID-19, reviewing the prognosis in patients with and without underlying CVDs in Imam Khomeini Hospital, Tehran, Iran.

Regarding COVID-19, a truly relevant illness is probably any form of heart disease. This is because first, the prevalence of cardio-cerebrovascular disorders is much greater in patients with COVID-19 compared to the general population (13), and second, patients with hypertension, cardio-cerebrovascular disorders, or diabetes are more expected to stay in the ICU or expire because of SARS-CoV-2 (12-14). The overall rates of hypertension, cardio-cerebrovascular complications, and diabetes were approximately 2-fold, 3-fold, and 2-fold, respectively, higher in ICU cases than in non-ICU inpatients (13). Recently, the Chinese Center for Disease Control and Prevention reported the results of a study with the largest sample size to date in the field of COVID-19 in China, indicating that the overall mortality rate was 2.3%, and in comparison, the number of deaths was 10.5% in cases with underlying CVD (8).

In the present study, the number of deaths in COVID-19 cases with underlying non-CVD and CVD was 4.17 times higher than that of COVID-19 patients without any underlying disorders. The mortality rate was 3.98 times higher in patients with underlying CVD than the baseline.

On the other hand, as a minimum, 8.0% of COVID-19 cases suffered any type of acute heart injury, but additional investigations revealed that the occurrence of cardiac injury is about 13-fold greater for patients requiring ICU than non-ICU inpatients (13, 15, 16).

A recent investigation indicated that during the COVID-19 pandemic, patients with underlying CVD, coronary heart disease, hypertension, viral illness, cardiomyopathy, or other comorbid disorders are more likely to suffer myocardial damage (17). Myocardial cell injury occurs through several mechanisms, including destabilized coronary plaque, systemic inflammatory reactions, virus-related damage, and serious hypoxia (6, 17, 18).

5.1. Conclusions

Underlying cardiovascular complications are possibly associated with COVID-19 prognosis. Predicting the effect of common complications on COVID-19 prognosis support health professionals and authorities to consider appropriate facilities and top approaches to managing COVID-19 in hospitals. Further investigations with longer durations and greater sample populations are highly recommended.


  • 1.

    Khederlou H, Rasouli A, Anari R, Moeini F, Parsa S, Sadeghi N, et al. Evaluation of the Relationship between Serum Vitamin D and Morbidity and Mortality in Patients with COVID-19. J Thor Dis Cardio Surg. 2021;2(3).

  • 2.

    OECD Interim Economic Assessment. Coronavirus: The world economy at risk. France: Organisation for Economic Co-operation and Development; 2020.

  • 3.

    Kandel N, Chungong S, Omaar A, Xing J. Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries. Lancet. 2020;395(10229):1047-53. [PubMed ID: 32199075]. [PubMed Central ID: PMC7271261]. https://doi.org/10.1016/S0140-6736(20)30553-5.

  • 4.

    Epidemiology Working Group for Ncip Epidemic Response: Chinese Center for Disease Control Prevention. [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2020;41(2):145-51. Chinese. [PubMed ID: 32064853]. https://doi.org/10.3760/cma.j.issn.0254-6450.2020.02.003.

  • 5.

    Guzik TJ, Mohiddin SA, Dimarco A, Patel V, Savvatis K, Marelli-Berg FM, et al. COVID-19 and the cardiovascular system: implications for risk assessment, diagnosis, and treatment options. Cardiovasc Res. 2020;116(10):1666-87. [PubMed ID: 32352535]. [PubMed Central ID: PMC7197627]. https://doi.org/10.1093/cvr/cvaa106.

  • 6.

    Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. [PubMed ID: 31986264]. [PubMed Central ID: PMC7159299]. https://doi.org/10.1016/S0140-6736(20)30183-5.

  • 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):1061-9. [PubMed ID: 32031570]. [PubMed Central ID: PMC7042881]. https://doi.org/10.1001/jama.2020.1585.

  • 8.

    Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239-42. [PubMed ID: 32091533]. https://doi.org/10.1001/jama.2020.2648.

  • 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.

    Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir Med. 2020;8(4):420-2. [PubMed ID: 32085846]. [PubMed Central ID: PMC7164771]. https://doi.org/10.1016/S2213-2600(20)30076-X.

  • 11.

    Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat Rev Cardiol. 2020;17(9):1-16. [PubMed ID: 32690910]. [PubMed Central ID: PMC7370876]. https://doi.org/10.1038/s41569-020-0413-9.

  • 12.

    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.

  • 13.

    Li B, Yang J, Zhao F, Zhi L, Wang X, Liu L, et al. Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China. Clin Res Cardiol. 2020;109(5):531-8. [PubMed ID: 32161990]. [PubMed Central ID: PMC7087935]. https://doi.org/10.1007/s00392-020-01626-9.

  • 14.

    Madjid M, Solomon S, Vardeny O, Mullen B. Cardiac implications of novel coronavirus (COVID19). ACC Clin Bull. 2020.

  • 15.

    Zheng YY, Ma YT, Zhang JY, Xie X. COVID-19 and the cardiovascular system. Nat Rev Cardiol. 2020;17(5):259-60. [PubMed ID: 32139904]. [PubMed Central ID: PMC7095524]. https://doi.org/10.1038/s41569-020-0360-5.

  • 16.

    Nunez-Gil IJ, Fernandez-Ortiz A, Maroud Eid C, Huang J, Romero R, Becerra-Munoz VM, et al. Underlying heart diseases and acute COVID-19 outcomes. Cardiol J. 2021;28(2):202-14. [PubMed ID: 33346365]. [PubMed Central ID: PMC8078951]. https://doi.org/10.5603/CJ.a2020.0183.

  • 17.

    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.

  • 18.

    Labarrere CA, Woods JR, Hardin JW, Campana GL, Ortiz MA, Jaeger BR, et al. Early prediction of cardiac allograft vasculopathy and heart transplant failure. Am J Transplant. 2011;11(3):528-35. [PubMed ID: 21219580]. https://doi.org/10.1111/j.1600-6143.2010.03401.x.

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