Archives of Academic Emergency Medicine. 2021; 9(1): e65 OR I G I N A L RE S E A RC H Effect of Underlying Cardiovascular Disease on the Prog- nosis of COVID-19 Patients; a Sex and Age-Dependent Analysis Mohammad Haji Aghajani1, Ziba Asadpoordezaki2,3, Mehrdad Haghighi4, Asma Pourhoseingoli1, Niloufar Taherpour1, Amirmohammad Toloui5, Mohammad Sistanizad1,6∗ 1. Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2. Department of Psychology, Maynooth University, Kildare, Ireland. 3. Kathleen Lonsdale Institute for Human Health Research, Maynooth University, Kildare, Ireland. 4. Infectious Diseases and Tropical Medicine Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5. Physiology Research Center, Iran University of Medical Sciences, School of Medicine, Tehran, Iran. 6. Department of Clinical Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Received: July 2021; Accepted: August 2021; Published online: 30 September 2021 Abstract: Introduction: Adults with underlying medical disorders are at increased risk for severe illness from the virus that causes COVID-19. This study aimed to compare the effect of underlying diseases on the mortality of male and female patients as a primary objective. We also evaluated the effect of drugs previously used by COVID-19 patients on their outcome. Methods: This retrospective cohort study was carried out on confirmed cases of COVID-19 who were admitted to a teaching hospital in Tehran, Iran. Data was gathered from patients’ files. Log binomial model was used for investigating the association of underlying diseases and in-hospital mortality of these patients. Results: A total of 991 patients (mean age 61.62±17.02; 54.9% male) were recruited. Hyperten- sion (41.1%), diabetes mellitus (30.6%), and coronary artery disease (19.6%) were the most common underlying diseases. The multivariable model showed that hypertension (RR = 1.62; 95% CI: 1.22-2.14, p = 0.001) in male patients over 55 years old and coronary artery disease (RR = 2.40; 95% CI: 1.24-4.46, p = 0.009) in female patients under 65 years old were risk factors of mortality. In females over 65 years old, the history of taking Angiotensin Converting Enzyme inhibitors (ACEi) and Angiotensin Receptor Blockers (ARB) (RR = 0.272; 95% CI: 0.17-0.41, p = 0.001) was a significant protective factor for death. Conclusion: COVID-19 patients with a history of car- diovascular diseases such as hypertension and coronary artery disease, especially those in specific age and sex groups, are high-risk patients for in-hospital mortality. Additionally, a previous history of taking ACEi and ARB medications in females over 65 tears old was a protective factor against in-hospital mortality of COVID-19 pa- tients. Keywords: COVID-19; Hypertension; Coronary Artery Disease, Prognosis Cite this article as: Haji Aghajani m, Asadpoordezaki Z, Haghighi M, Pourhosseingoli A, Taherpour N, Toloui A, Sistanizad M. Effect of Un- derlying Cardiovascular Disease on the Prognosis of COVID-19 Patients; a Sex and Age-Dependent Analysis. Arch Acad Emerg Med. 2021; 9(1): e65. https://doi.org/10.22037/aaem.v9i1.1363. ∗Corresponding Author: Mohammad Sistanizad; 3rd floor, Faculty of Phar- macy, Shahid Beheshti Medical University, Vali-e-asr Ave, Niyayesh Junction, Tehran, Iran. Postal code, 1991953381, Email: sistanizadm@sbmu.ac.ir, Tel: +98-9122784895, Fax: +98-2188200087, ORCID: http://orcid.org/0000-0002- 7836-6411. 1. Introduction Coronaviruses are a large family of viruses that are known to cause illnesses ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS)(1, 2). The severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), which causes COVID-19 was first reported in Wuhan, China, in late December 2019 (3). COVID-19 has spread This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Haji Aghajani et al. 2 worldwide leading to a global pandemic, it affects different areas of human life such as health, social, and economy, and had caused 93,805,612 confirmed cases and 2,026,093 deaths, by 18 January 2021 (4). Adults with underlying medical disorders are at increased risk for severe illness from the virus that causes COVID-19. Cardiovascular disease (CVD) is one of the most important underlying diseases, which could affect the prognosis of pa- tients with COVID-19 (5). In addition, a high rate of underly- ing CVD has been observed in patients with COVID-19, and increased mortality rates have been reported with these co- morbidities (6, 7). From the point of view of studies from different countries, age and sex are considered to be strong prognostic factors of death in patients with COVID-19. Sex difference in COVID- 19 outcome results from an interlock interaction between bi- ological, geographical, and social impacts, and past medical history including preexisting CVD. This study aimed to compare the effect of underlying disease on the mortality of male and female patients as a primary ob- jective. We also evaluated the effect of drugs previously used by COVID-19 patients, including Beta blockers, Angiotensin Converting Enzyme inhibitors (ACEi), Angiotensin Receptor Blockers (ARB), anticoagulants, and antiplatelet drugs, on their outcome. 2. Methods 2.1. Study Design and Setting The present study was a retrospective cohort study con- ducted on 991 confirmed COVID-19 patients with hospital- ization criteria in Imam Hossein Hospital, affiliated to Shahid Beheshti University of Medical Sciences, Tehran, Iran. The current study was performed based on Helsinki declarations and was approved by the reviewer’s board and ethics com- mittee of the deputy for research affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Ethics code: IR.SBMU.RETECH.REC.1399.263). 2.2. Participants Using the census method, all of the patients who were admit- ted from 29 February to 20 July 2020 with a laboratory con- firmed SARS-Cov2 infection based on Reverse Transcriptase Polymerase Chain Reaction test (RT-PCR) using throat and nose swab specimens were included in this study. Confirmed COVID-19 outpatients and the patients with the clinical di- agnosis of COVID-19 whose diagnosis was not confirmed by PCR test were excluded from the study. 2.3. Follow-up and outcome In this study, the measured outcome was in-hospital mortal- ity and follow-up time was the duration of hospitalization, which is from the date of admission to date of discharge or when the patient died during hospitalization. 2.4. Data gathering Data were collected from medical records of COVID-19 pa- tients using a researcher-made checklist. Researchers de- signed a checklist based on the aim of the study according to the opinion of medical and methodological expert team. Data extracted for each patient included demographic char- acteristics (age, sex), Body Mass Index (BMI), past medical histories such as underlying diseases and medication history, signs and symptoms on admission, duration of hospitaliza- tion, and outcome of patients such as intensive care unit (ICU) admission, and in-hospital mortality. The mentioned information was extracted from medical records of COVID- 19 patients by a trained research team that included nursing and medical personnel of cardiac care unit (CCU) and ICU. 2.5. Definitions In this study, underlying diseases were defined as chronic health conditions that patients already had before their hos- pitalization due to COVID-19 infection. Candidate underly- ing diseases were Diabetes Mellitus (DM), Central Nervous System (CNS) disorders, Hypertension (HTN), Chronic Kid- ney Diseases (CKDs), any type of cancer, Hyperlipidemia, Im- munosuppressive disorders, Respiratory diseases, Congen- ital disorders, Coronary Artery Diseases (CADs), and his- tory of coronary angioplasty or Coronary Artery Bypass Graft (CABG) surgery. The information from the mentioned dis- eases was based on the reports of physicians’ examination. Medication history referred to the patients’ use of different types of drugs due to their special health conditions, based on a physicians’ prescription, before hospitalization due to COVID-19 infection. In this study, the studied medications were Beta blockers, ACEi or ARB, ASA (Acetyl Salicylic Acid), Atorvastatin, Nitroglycerin, Warfarin, Rivaroxaban, and Met- formin. 2.6. Statistical analysis Continuous variables were described using mean ± standard deviation (SD), and categorical variables were expressed as frequency (percentage). The normality assumption was ex- amined using checking kurtosis, skewness, box plot, and Q-Q plot, due to the large amount of data. T-test and Mann–Whitney U test were used for comparisons of means in normal and non-normal variables, respectively. In addi- tion, a multivariable log binomial regression model was per- formed for investigating the association of in-hospital mor- tality with underlying diseases and other variables of the study. The final multivariable model was selected based on potential risk factors according to the backward approach with P-value < 0.2. Due to the proven role of sex and age in This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2021; 9(1): e65 the etiology of disease and its prognosis, we used subgroup analysis to consider the probable effect of these interactive biological variables. In subgroup analysis, the age cut-offs considered for males and females were 55 and 65 years, re- spectively (8). Findings were reported as Relative Risk (RR) and 95% confidence interval (95% CI). A two-sided P-value less than 0.05 was considered statistically significant. Ana- lyzing was done using the STATA 14 Package. 3. Results 3.1. Demographic characteristics and clinical findings Among the 991 patients, 544 (54.9%) were male and 257 pa- tients (25.9%) died. The mean age was 61.62±17.02 years [range 10-99]. The most common chief complaints were dys- pnea with 626 (63.2%), cough with 524 (52.9%), fever with 495 (49.9%), myalgia with 320 (32.3%), nausea/vomiting with 204 (20.6%), and diarrhea with 90 (9.1%) cases. The median dura- tion of hospitalization was 6 days with an inter quartile range (IQR) of 6. One hundred eighteen (11.9%) patients were ad- mitted to ICU. Table 1 shows the distribution of demographic and some clinical characteristics of studied cases. 3.2. Underlying diseases and past cardiovascu- lar medications Hypertension with 407(41.1%), Diabetes Mellitus with 303(30.6%), and CAD with 194(19.6%) cases were the most frequent underlying diseases in both sexes. In the first step of investigating the association of the underlying diseases with death in our whole population of COVID-19 patients, the uni- variate analysis showed that CNS disorders (17.2% vs 8.6% in dead and alive patients, respectively with p <0.001), HTN (53.7% vs 36.6% dead and alive patients, respectively with p <0.001), and CAD (25.7% vs 17.4% dead and alive patients, respectively with p <0.001) were underlying diseases associ- ated with death. Also, having a history of using ASA (25.2% vs 18.7% dead and alive patients, respectively with p = 0.026), nitroglycerin (10.5% vs 6.3% dead and alive patients, respec- tively with p = 0.027), and Warfarin or Rivaroxaban (7% with 2.9% dead and alive patients, respectively with p = 0.003) had a significant association with mortality in our whole popula- tion. Tables 2 and 3 show the association between underly- ing diseases and history of using cardiovascular medications with mortality of patients based on their sex. In the next step of designing a model, we fitted a multivari- able model, adjusting the effects of demographic factors. In this model, only demographic factors of sex and age had a significant association with death. Accordingly, we have no- ticed the strong effect of sex and age and their interactions on this model. To adjust their interaction effects precisely and to know how underlying diseases affect mortality in each sex, we have analyzed the relation between mortality, under- lying diseases and medication history in different age and sex subgroups. Our final multivariable models were fitted in four different sex and age groups. A total number of 257 Females under 65, 190 females over 65, 199 males under 55, and 345 males over 55 were our subgroups in the final model. For males over 55 years old, HTN was a significant risk fac- tor in both univariate and multivariable analyses with RR: 1.34 (95% CI: 1.03-1.74, P=0.029) and RR: 1.62(95% CI: 1.22- 2.14, P=0.001), respectively. There was no variable that sig- nificantly associated with mortality in males under 55 years old. In females under 65 years old, CAD with RR: 3.01(95% CI: 1.55-5.84, P=0.001) in univariate analysis, and RR: 2.40 (95% CI: 1.24-4.46, P=0.009) in Multivariable analysis was a re- markable risk factor of death on both analyses. Although the history of taking ACEi or ARB with RR: 2.16 (95% CI: 1.18-4.03, P=0.012), and Atorvastatin with RR: 2.45(95% CI: 1.29-4.67, P=0.006) were significant risk factors in univariate analyses, they were not significant in multivariable analysis. In females over 65 years old, history of taking ACEi or ARB was a significant protective factor against death in both univariate and Multivariable analyses with RR: 0.521 (95% CI: 0.32-0.83, P=0.007) and RR: 0.272 (95% CI: 0.17-0.41, P=0.001), respectively. The results of univariate and Multi- variable models are shown in table 4. 4. Discussion In this retrospective cohort study, we investigated the as- sociation between underlying cardiovascular diseases, pa- tients’ drug history, and COVID-19 mortality. We found that in males older than 55, HTN and in females under 65, coro- nary artery disease was strongly associated with in-hospital mortality. Additionally, a previous history of taking ACEi and ARB medications in females over 65 were protective factors against in-hospital mortality of COVID-19 patients. There are several studies on the assessment of the relation- ship of a history of HTN and severity of COVID-19 and its related mortality. Several studies showed that HTN is a risk factor for COVID-19-related mortality. For example, a cohort study showed that Hypertensive COVID-19 patients have more severe inflammatory responses to the disease and experience more severe internal organ injury. Poor out- come in hypertensive patients was more prevalent than non- hypertensive COVID-19 patients (9). Meanwhile, other stud- ies demonstrated that there was no adequate evidence sup- porting the prognostic effect of HTN for COVID-19 (10). Our results showed that HTN is a risk factor for mortality only in males older than 55 years. This finding justifies the inconsis- tency between the studies. Because hypertension seems to be a risk factor for patient mortality in a certain group of pa- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Haji Aghajani et al. 4 Table 1: Baseline characteristics based on final disease outcome Variables Total (n:991) Discharged (n:734 NDeath ((n:257 P-value Sex (%) Female 447(45.1) 347(47.3) 100(38.9) 0.020* Male 544(54.9) 387(52.7) 157(61.1) Age ( Year) Mean ± SD 61.62±17.02 58.45±16.75 70.84±14.23 <0.001* Signs and Symptoms Dyspnea 626(63.2) 458(62.4) 168(65.4) 0.395 Cough 524(52.9) 396(54) 128(49.8) 0.252 Fever 495(49.9) 374(51) 121(47.1) 0.285 Myalgia 320(32.3) 260(35.4) 60(23.3) 0.001* Nausea/ Vomiting 203(20.5) 153(20.8) 50(19.5) 0.635 Diarrhea 90(9.1) 73(9.9) 17(6.6) 0.110 ICU Admission Yes 118(11.9%) 44(6%) 74(28.8%) <0.001* No 183(71.2%) 690(94%) 873(88.1%) BMI(Kg/M2) Median (IQR) 26.17(5.31) 26.23(5.18) 25.92(5.87) 0.170 Hospital Stay (Day) Median (IQR) 6(6) 6(5) 6(7) 0.338 Data are presented as mean ± standard deviation (SD), number (%) or median (inter quartile range). * P<0.05 was statistically significant. BMI: Body Mass Index; ICU: intensive care unit; IQR: inter quartile range. Table 2: Distribution of underlying diseases and their crude association with in-hospital mortality between sex groups in COVID-19 patient Variables Male (n=544) Female (n:447) Survived Dead P Survived Dead P DM 112(28.9) 50(31.8) 0.50 106(30.5) 35(35.0) 0.39 CNS 44(11.4) 32(20.4) 0.006* 19(5.5) 12(12.1) 0.02* Hypertension 121(31.3) 76(48.4) <0.001* 148(42.7) 62(62.0) <0.001* CKD 36(9.3) 16(10.2) 0.74 34(9.8) 16(16.0) 0.08 Cancer 9(2.3) 7(4.5) 0.18 20(5.8) 6(6.0) 0.92 Hyperlipidemia 19(4.9) 12(7.6) 0.213 21(6.1) 10(10.0) 0.17 ISD 9(2.3) 7(4.5) 0.18 16(4.6) 5(5.0) 0.87 RD 27(7.0) 16(10.2) 0.20 31(8.9) 13(13.0) 0.22 CD 5(1.3) 4(2.5) 0.29 10(2.9) 2(2.0) 0.63 CAD 77(19.9) 36(22.9) 0.42 51(14.7) 30(30.0) <0.001* Coronary revisualization CABG 31(8.0) 12(7.6) 0.82 7(2.0) 7(7.1) 0.003* Angioplasty 24(6.2%) 12(7.6%) 11(3.2%) 8(8.2%) Data are presented as number (%). DM: Diabetes mellitus; CNS: Central nervous system disorders; CKD: Chronic kidney Disease; ISD: Immunosuppressive disorders; RD: Respiratory diseases; CD: Congenital Disorders; CAD: Coronary Artery Disease; CABG: Coronary Artery Bypass Graft; P<0.05 was considered statistically significant. tients, not for all COVID-19 patients. To be illustrated, our findings are in line with previous re- search, confirming the prognostic value of age in predicting COVID-19 patients’ disease severity and its pertaining mor- tality (11). Moreover, we have concluded that the male sex is independently associated with a higher risk of death in COVID-19 patients. Evidence regarding the impact of sex on in-hospital mortality of COVID-19 patients is a growing topic and so far, independent association of male sex with mortal- ity has been shown in some studies (12-14). Reasons behind this finding could be the higher levels of humoral and cellular immunity in females and possible differences in sex-based comorbidities (14-19). Underlying cardiovascular disease was a risk factor of in- hospital mortality in female patients aged less than 65 years. It has been previously shown that premenopausal females who develop coronary artery disease might have lower levels of estrogen compared to those without coronary artery dis- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2021; 9(1): e65 Table 3: Distribution of past cardiovascular medications and their crude association with in-hospital mortality between sex groups among COVID-19 patient Variables Male (n=544) Female (n:447) Survived Dead P Survived Dead P Beta Blockers 51(13.2) 20(12.7) 0.862 57(16.4) 28(28) 0.009* ACEi or ARB 82(21.2) 38(24.1) 0.473 106(30.5) 31(31) 0.931 ASA 79(20.5) 42(26.6) 0.119 58(16.7) 23(23) 0.151 Atorvastatin 54(14) 25(15.8) 0.582 66(19) 25(25) 0.191 Nitroglycerin 29(7.5) 13(8.2) 0.582 17(4.9) 14(14) 0.002* Warfarin/Rivaroxaban 12(3.1) 8(5.1) 0.271 9(2.6) 10(10.0%) 0.003* Data are presented as number (%). * P<0.05 was statistically significant. ACEi: Angiotensin Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blocker; ASA: Acetyl Salicylic Acid. Table 4: Univariate and Multivariable analysis results for the association of underlying disease and drug history with in-hospital death in patients with COVID-19 in different age and sex subgroups Variables Univariate Multivariable RR (95% CI) P RR (95% CI) P Males over 55 years old** Hypertension No 1 1 - Yes 1.34(1.03-1.74) 0.029* 1.62(1.22-2.14) 0.001* Taking Beta blocker No 1 1 Yes 0.77(0.51-1.14) 0.200 0.68(0.46-1.02) 0.063 Taking ACEi or ARB No 1 1 Yes 0.89(0.66-1.02) 0.446 0.74(0.54-1.01) 0.063 Females under 65 years old Coronary Artery Disease No 1 1 Yes 3.01(1.55-5.84) 0.001* 2.40(1.24-4.64) 0.009* Taking Atorvastatin No 1 1 Yes 2.45(1.29-4.67) 0.006* 1.74(0.90-3.37) 0.096 Taking ACEi or ARB No 1 1 Yes 2.16(1.18-4.03) 0.012* 1.72(0.92-3.20) 0.085 Females over 65 years old Hypertension No 1 1 Yes 1.10(0.72-1.70) 0.643 1.16(0.912-1.75) 0.086 Taking ACEi or ARB No 1 1 Yes 0.521(0.32-0.83) 0.007* 0.27 (0.17-0.41) <0.001* Chronic kidney Disease No 1 1 Yes 1.51(0.969-2.33) 0.068 1.34(0.945-2.39) 0.067 CI: confidence interval. *P<0.05 was considered statistically significant. **All variables were excluded in the final model of the subgroup of males under 55 years old. ACEi: Angiotensin Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blocker. ease (20-22). Besides, there is growing evidence on the pro- tective effect of estrogen hormone against COVID-19 (23-26). Considering that the most important factor responsible for higher levels of immune system activity in females is proba- bly hormonal differences, there seems to be a correlation be- tween lower estrogen levels, higher incidence of CAD, more This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Haji Aghajani et al. 6 susceptibility to developing severe disease from SARS-CoV-2 infection, and higher mortality rates. Growing evidence suggests that taking anti-hypertensive drugs (ACEI/ARB) is not associated with higher mortality rates or illness severity in COVID-19 patients and in fact, it might be beneficial for these patients. We demonstrated that the history of taking these drugs has a protective im- pact against the mortality of females more than 65 years old, in line with other studies showing a possibly lower mortal- ity rate in patients treated with these medications (6, 27-30). However, the effects of taking these medications haven’t been completely studied in different ages and sexes. Due to com- plexity regarding confounding factors of underlying diseases and biological changes, especially in females during post- menopausal period, more studies are required to assess the effects of these drugs in specific age categories. 5. Limitations This retrospective study had its limitations. Due to its na- ture, tools to evaluate patients’ data documentation were not available; Some data such as previous medication history were recorded according to patients’ self-report and, there- fore, were not totally reliable. Previous medical files of pa- tients were inaccessible due to the shortage of time and sup- plies during the pandemic. 6. Conclusion COVID-19 patients with a history of cardiovascular diseases such as hypertension and coronary artery disease, especially those in specific age and sex groups, are high-risk patients for in-hospital mortality. Additionally, a previous history of tak- ing ACEi and ARB medications in females over 65 were pro- tective factors against in-hospital mortality of COVID-19 pa- tients. 7. Declarations 7.1. Acknowledgments The authors express their appreciation to the participants and the personnel of Imam Hossien hospital for their col- laboration. We acknowledge the team of professional nurses of Shahid Beheshti University of Medical Science for their effort in data collection (Mrs. Golnoosh Mortezaee, Effat Taheri, Ghodsi Najari, Faezeh Nesaei, Faezeh Fakour, Ghaza- leh Amanabadi, and Vida Torabi) and Maedeh Sayad for ded- ication to data entry. 7.2. Funding This work was supported by the Prevention of Cardiovascular Disease Research Center, Shahid Beheshti University of Med- ical Science, Tehran, Iran. 7.3. Conflict of interest statement The authors have declared that no competing interests exist. 7.4. Author contribution All authors met the four criteria for authorship contribution based on the recommendations of the international commit- tee of medical journal editors. References 1. Swerdlow DL, Finelli L. Preparation for Possible Sus- tained Transmission of 2019 Novel Coronavirus: Lessons From Previous Epidemics. JAMA. 2020;323(12):1129-30. 2. Chowell G, Castillo-Chavez C, Fenimore PW, Kribs-Zaleta CM, Arriola L, Hyman JM. Model parameters and out- break control for SARS. Emerging infectious diseases. 2004;10(7):1258-63. 3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clin- ical features of patients infected with 2019 novel coro- navirus in Wuhan, China. Lancet (London, England). 2020;395(10223):497-506. 4. 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Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Limitations Conclusion Declarations References