Comparing Myocardial Perfusion Scan Findings in Patients With and Without Covid-19

Comparing Myocardial Perfusion Scan Findings in Patients With and Without Covid-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparing Myocardial Perfusion Scan Findings in Patients With and Without Covid-19 Bahar Moasses-Ghafari, Sahar Choupani, Sajed Jahanbin, Reza Lotfi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4509262/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 12 You are reading this latest preprint version Abstract Background and aim: Covid-19 is a pandemic viral disease involving multi organ systems including cardiovascular system, directly or indirectly. SPECT GATED MPI is a non-invasive nuclear medicine imaging modality to evaluate the myocardial perfusion and function. The aim of this study was to assess the effect of Covid-19 pandemic on myocardial perfusion imaging for ischemic heart disease. Methods This was a cross-sectional (descriptive-analytical) study conducted on 750 patients needing myocardial perfusion imaging (MPI) who referred to the nuclear medicine center of Kowsar hospital, a teaching hospital in Sanandaj, the center for Kurdistan province located in northwest of Iran. Data collection was done during the Covid-19 pandemic, between 6 April 2020 and 21 March 2021. Data analyses were conducted in SPSS using independent sample T test and Chi-square. Results A total of 750 patients, including 328 (43.7%) Covid-19 positive and 422 (56.3%) Covid-19 negative, were entered in the analysis. Although Covid-19 infected Obese patients had significantly higher rate of abnormal MPI (P< 0.0001), no significant difference was observed between the two groups regarding abnormal MPI (p=0.551). Conclusion The absence of a significant discrepancy in abnormal MPI occurrence between the two groups suggests that there may be Covid-19 patients with potentially abnormal MPI who have gone undetected. Additionally, Covid-19 patients with pleural chest pain, myalgia, or dyspnea could have been misdiagnosed with chest pain secondary to heart disease. Covid-19 SARS-COV-2 MPI Ischemic heart disease Introduction The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome (SARS) virus, has been the prominent cause of morbidity and mortality worldwide in the last three years ( 1 ). In contrast to other upper respiratory tract viruses, COVID-19 instigates multiple systemic manifestations in the body by activating the inflammatory cascade and thrombotic process. It has become increasingly evident that certain populations are at higher risk of severe illness and death ( 2 ). Among these groups are individuals with underlying health conditions such as ischemic heart disease. A growing body of literature posits that COVID-19 infection may heighten the risk of cardiovascular events, including but not limited to acute coronary syndrome, myocarditis, arrhythmia, and sudden death ( 3 – 5 ). COVID-19 may affect the cardiovascular system by directly damaging the heart and endothelium, or the damage may be secondary to oxygen supply/demand imbalance, abnormal generalized inflammatory response, atherosclerotic plaque rupture and hypercoagulopathy state ( 1 , 5 ). Coronary microvascular dysfunction (CMD) is defined as the clinical syndrome of angina and electrocardiographic ischemic changes in the absence of obstructive CAD. CMD during COVID-19 could be due to various causes, including damaged endothelium, microthrombi and abnormal renin-angiotensin system function ( 6 ). Recovered Patients from COVID-19 may experience prolonged chest pain, tachycardia, shortness of breath and decreased activity capacity; these symptoms are linked to CMD using stress myocardial perfusion imaging (MPI)( 7 ). Due to insufficient data on MPI's role in the evaluation of CMD caused by COVID-19, we conducted a study to investigate the COVID-19 impact on myocardial ischemia detected myocardial perfusion imaging. Material and Method Study protocol This was a retrospective study conducted on patients referred to the nuclear medicine center of Kowsar hospital in Sanandaj, the center for Kurdistan province in northwest of Iran, by cardiologists for myocardial perfusion imaging. The study was conducted between 6 April 2020 and 21 March 2021. Medical records of the study patients including demographic and clinical data and the results of myocardial perfusion were investigated. The proposal of this study was evaluated and approved by ethical committee of Kurdistan University of Medical Science (Ethic code: IR.MUK.REC.1399.331). Written informed consent was obtained during the admission for each patient. Clinical definition Baseline demographic information and cardiovascular risk factors of the participated patients were collected using the hospital documentations, including age, gender, hypertension, diabetes mellitus, hyperlipidemia, obesity, smoking, cancer, diagnosed coronary artery disease (CAD), familial history of CAD. Patients were considered having diabetes mellitus based on the fact that they were receiving oral hypoglycemic medications or insulin. BMI ≥ 30 was considered obese, hypertension was defined as blood pressure ≥ 140/90 or receiving anti-hypertensive medication, hyperlipidemia was defined total cholesterol ≥ 6.2 mmol/L or receiving cholesterol lowering medication, familial history of CAD was considered positive in case that a first degree relative was diagnosed before the age of 55. Smoking was defined as previous or current use of tobacco. A patient was considered to known CAD based on the provided history of prior atherosclerotic coronary disease, myocardial infarction, chest pain or equivalent symptoms, abnormal ECG or positive cardiac biomarkers, previous Percutaneous Coronary Intervention (PCI) and coronary artery bypass graft (CABG). The lung-to-heart ratio (LHR) was determined by calculating the ratio between the average counts obtained from corresponding regions of interest positioned over the left lung and the heart, respectively. TID (Time-Intensity Dynamics) refers to the temporal changes in signal intensity or contrast enhancement observed within a specific region of interest in medical imaging. Myocardial perfusion imaging MPI was done according to the standard 2-day stress-rest protocol of the hospital; all patients were prohibited from using beta blockers, calcium channel blockers, methylxanthines, dipyridamole, tea or any Caffeine-containing product 24 hours prior to the stress phase of the scan. To induce pharmaceutical stress, 0.56mg/kg of dipyridamole was infused for 3 minutes, followed by 11 Mbq/kg of Tc-99 MIBI bolus injection at the third minute of infusion. In case of facing any side effects, the injection would be discontinued, and aminophylline would be infused; as for the resting imaging, the Tc-99 MIBI bolus injection was administered on the next day. All patients were treated with high-fat-containing foods like milk and cream, imaging was performed 75–90 minutes afterwards. Gated myocardial perfusion was performed using SIEMENS SYMBIA EVO EXCEL gamma camera. 64 projection images were acquired at a 3-degree angle to have anterior orbit from a 180-degree 64 in 64 matrices by scanning the area between 45-degree right anterior oblique and 45-degree left posterior oblique projections in L-mode with low energy in supine position 75–90 minutes after injection for stress images and 75–90 minutes for rest images. “Myovation Evolution” software (OSEM with 12 iterations, 10 subsets) was used to process the images. Images were automatically recorrected by filtered back projection and Butterworth filter (0.4 order 10) after the reconstruction of images by manual overlapping. The images were assessed by an experienced nuclear medicine specialist using QGS/QPS (Xeleris, Cedras-Sinai Medical Centre, Los Angeles, California, USA) and 4DM software. Imaging interpretation The myocardial perfusion was evaluated using a visual semiquantitative method known as the 20-segment myocardial modeling. A 5-point system was employed to assess perfusion, with scores assigned as follows: normal = 0, mildly reduced = 1, moderately reduced = 2, severely reduced = 3, and no uptake = 4. Following the scoring of all segments, three parameters were calculated: the summed stress score (SSS), which represents the sum of scores from all 20 segments during the stress phase; a score below 3 indicates normality. The summed rest score (SRS) and the summed difference score (SDS), which is derived by subtracting SSS from SRS, were also computed. Both quantitative analysis and qualitative observations from nuclear medicine specialists were incorporated to evaluate MPI abnormality. Statistical analysis Histogram and Shapiro-Wilks tests were used to verify the normality of data. Continuous data were expressed as mean ± standard deviation, and categorical data were demonstrated as percentages. The student’s t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively. A P-value < 0.05 was considered statistically significant. Statistical analysis was performed with IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, N.Y., USA). Results A total of 785 were included in this study, 363 of whom had a history of COVID-19, 35 patients were excluded due to inappropriate diagnostic methods leading to confusion on whether they were infected or not, eventually 328 were COVID-19 positive and the rest of 422 patients with negative PCR results were used as controls. The mean (SD) age of the patients was 56.12 (11.38), and 446 (59.5%) were female. Baseline demographic and clinical features of the study patients are summarized in Table 1 . Table 1 baseline demographic and clinical characteristics of the study patients All (n = 750) Covid-19 POS. (n = 328) Covid-19 NEG. (n = 422) P value Age mean (SD) 56.12 (11.38) 56.59 (10.53) 55.76 (12.00) 0.322 Gender, n(%) Male 304 (40.5) 128 (42.10) 176(57.90) 0.458 Female 446 (59.5) 200(44.84) 246 (55.16) HTN, n(%) 398 (53.1) 187 (46.98) 211 (53.02) 0.056 Diabetes, n(%) 200 (26.7) 98 (49) 102 (51) 0.080 Obesity, n(%) 136 (18.13) 94 (69.11) 42 (30.89) < 0.0001 Hyperlipidemia, n(%) 142 (18.9) 52 (36.61) 90 (63.39) 0.058 Cancer, n(%) 8 (1.1) 3 (37.5) 5 (62.5) 0.721 Known CAD, n(%) 297 (39.6) 101 (34.01) 196 (65.99) < 0.0001 Familial history, n(%) 267 (35.5) 106 (39.70) 161 (60.30) 0.098 Smoking, n(%) 167 (22.3) 72 (43.11) 95 (56.89) 0.855 Hospitalization, n(%) 72(9.6) 72(9.6) 0(0.0) < 0.0001 As shown in Table 1 , the COVID positive group with medical history of DM, HTN, CAD and obesity exhibited a higher rate of MPI referral compared to the non-COVID group. This difference may stem from the possibility of more severe post-COVID syndrome and complications in patients with these pre-existing conditions. Additionally, medical practitioners may exercise greater caution when evaluating patients with these risk factors. Patients with COVID had higher rate of HTN, hyperlipidemia and DM. There was no significant difference between the two groups regarding age, gender, HTN, diabetes, dyslipidemia, or cancer. However, obesity was significantly higher in COVID-19 patients. In addition, COVID-19 patients had a significantly (P < 0.001) higher known CAD rate than non-COVID-19 patients (Table 3 ). Chest pain was the most frequent cause of referral and screening and dyspnea were the second and third reasons respectively (Table 2 ). Table 2 frequency of causes of referral (MPI indication) variable COVID-19 n(%) Total, n(%) Pos. Neg. MPI indication Chest pain 182(55.5) 229(54.3) 411(54.8) Stenting and post-CABG follow up 8(2.4) 15(3.6) 23(3.1) Dyspnea 66(20.1) 47(11.1) 113(15.1) Evaluation for stenting 2(0.6) 0(0.0) 2(0.3) Other surgeries 12(3.7) 33(7.8) 45( 6 ) Screening 58(17.7) 98(23.2) 156(20.8) Regarding medication use, COVID-19 patients had significantly higher CCB and diuretics use (P = 0.02 and P = 0.006 respectively) non-COIVD-19 patients had more beta blocker and Aspirin use (P = 0.001 and P = 0.01) (Table 3 ). Table 3 Frequency of drugs use among patients All (n = 750) COVID-19 POS. (n = 328) COVID-19 NEG. (n = 422) P value Medication use n(%) 572 (76.26) 252 (76.82) 320 (75.82) 0.75 Oral hypoglycemic 197 (25.86) 95 (28.96) 102 (24.17) 0.13 Anticoagulant 42 (0.05) 18 (5.48) 24 (5.68) 0.90 Beta blockers 249 (33.2) 88 (26.82) 161 (38.15) 0.001 CCB 99 (13.2) 54 (16.46) 45 (10.66) 0.02 Nitroglycerin 91 (12.13) 37 (11.28) 54 (12.79) 0.528 ACE-I/ARB 352 (43.33) 159 (48.47) 193 (45.73) 0.456 Statin 223 (29.73) 86 (29.73) 137 (32.46) 0.063 Aspirin 248 (33.06) 92 (28.04) 156 (36.96) 0.01 Diuretic 69 (9.2) 41 (12.5) 28 (6.63) 0.006 According to specialist observation, 44.2 had ischemic MPI. Although the incidence of severe ischemia and myocardial necrosis was similar between the two groups, the COVID positive group exhibited a higher frequency of mild and moderate ischemia, although there was no statistically significant relation (Table 4 ). Additionally, obese COVID-19 patients had a significantly higher rate of abnormal MPI (P < 0.0001) compared to non-obese COVID-19 patients (Table 5 ). Table 4 Association between Covid-19 infection and Ischemia Ischemia No Ischemia Mild Moderate Severe Necrotic p-value Covid-19 0.831 Positive 183 (55.8) 108 (32.9) 19 (5.8) 8 (2.4) 10 (3.0) Negative 245(58.1) 133(31.5) 18(4.3) 11(2.6) 15(3.6) Table 5 Association between obesity and SSS in positive and negative Covid groups COVID-19 SSS P value Normal Mildly abnormal Moderately abnormal Severely abnormal Positive Obese < 0.001 Yes 87 (92.5) 5 (5.3) 1 (1.06) 1 (1.06) No 203 (86.7) 16 (6.8) 7 (2.9) 8 (3.4) Negative Obese 0.172 Yes 36 (85.7) 4 ( 10 ) 0 2 ( 5 ) No 331 (87.1) 24 (6.3) 9 (2.4) 16 (4.2) Table 6 presents the measured variables of MPI (Myocardial Perfusion Imaging). No statistically significant correlation was found between the two groups in terms of SSS (Summed Stress Score), abnormal MPI, myocardial thickening, EF (Ejection Fraction), LHR (Left Heart Ratio), and TID. Although a higher occurrence of hypokinesia was noted in COVID patients, statistical significance was not reached. Additionally, the visibility of the right ventricle was evaluated to explore pulmonary hypertension or right ventricular hypertrophy resulting from left ventricular failure. Although a higher prevalence of right ventricular visualization was observed in COVID-19 patients, the difference was not statistically significant (P = 0.192). Table 6 Difference between the two study groups in measured variables of MPI MPI findings All (n = 750) COVID-19 POS. (n = 328) COVID-19 NEG. (n = 422) P value End diastolic volume, mean (SD) 75.55 (20.28) 73.15 (31.17) 73.00 (31.70) 0.947 End systolic volume, mean (SD) 25.67 (23.4) 28.04 (24.47) 27.51 (24.04) 0.769 SSS, mean (SD) 2.02 (4.85) 1.79 (4.19) 2.19 (5.30) 0.268 SRS, mean (SD) 0.92 (3.52) 0.72 (2.69) 1.07 (4.04) 0.176 SDS, mean (SD) 1.08 (2.08) 1.08 (2.27) 1.08 (1.93) 0.984 EF, mean (SD) 60.22 (3.89) 59.43 (7.35) 58.87 (6.74) 0.281 EF (%) > 50%, mean (SD) 60 ( 8 ) 24 (7.3) 36 (8.5) 0.543 Visualized TID, mean (SD) 47 (6.3) 24 (7.4) 23 (5.5) 0.318 Abnormal LHR > 0.4, mean (SD) 56 (7.5) 23 (7.0) 33 (7.8) 0.781 Table 7 Comparison of Gated Findings of MPI and RV visualization between the two study groups COVID-19 POS. (n = 328) COVID-19 NEG. (n = 422) P value Abnormal cardiac wall motion 0.331 Yes 116 (35.4) 135 (32.0) No 212 (64.6) 287 (68.0) Abnormal cardiac wall thickness 0.430 Yes 30 (9.1) 46 (10.9) No 298 (90.9) 376 (89.1) right ventricular visibility 0.192 Yes 189 (57.6) 223 (52.8) No 139 (42.4) 199 (47.2) RV visualization 0.192 Yes 189 (57.6) 223 (52.8) No 139 (42.4) 199 (47.2) As indicated in Table 7 , no significant difference was observed between the two groups regarding Gated findings. Although not significant, right ventricular (RV) visualization in Covid-19 patients was higher than patients without Covid-19. Interestingly, our data demonstrated that Covid-19 positive patients who exhibited RV visualization tended to have a higher hospitalization rate compared to those without RV visualization (57.6% vs 42.4%) however it was not significant (p = 0.192) (Table 7 ). Discussion Despite the nearly two-year span since the emergence of the COVID-19 pandemic and the widespread use of vaccines globally, COVID-19 remains a significant contributor to mortality. Cardiovascular complications, including acute myocardial infarction (AMI), myocardial injury, arrhythmias, and thrombotic events, may also contribute to the morbidity and mortality of affected patients ( 8 ). Myocardial perfusion imaging (MPI) is a non-invasive imaging modality employed for the diagnosis and assessment of coronary artery disease ( 9 ). However, the COVID-19 pandemic has raised concerns regarding the potential implications of COVID-19 on the outcomes of MPI assessments. To our knowledge, this is the first study evaluating the effect of COVID-19 on myocardial perfusion imaging in Iran. This research involved the referral of 750 patients for myocardial perfusion scans by cardiologists. A total of 328 patients (47.73%) tested positive for COVID-19. There was a higher rate of referrals among patients who had comorbid conditions such as hypertension, diabetes, and obesity. Specifically, obese COVID-19-positive patients were found to have a significantly higher rate of referral and abnormal MPI. In a study by Chan-Young Jung et al. (2021), higher BMI levels were associated with a graded susceptibility to severe COVID-19 infections ( 10 ). Additionally, Bolukcu et al. found that the mean BMI level of deceased COVID-19-positive patients was 31 kg/m2 and that there was no significant difference (P = 0.09) compared to COVID-19-negative patients However, the BMI of patients requiring intensive care was significantly higher (P = 0.04) in the COVID-19 group ( 11 ). Peters et al (2021) demonstrated that a higher BMI, waist circumference, waist-to‐hip ratio and waist‐to‐height ratio were each associated with an increased risk of death from COVID‐19, influenza/pneumonia and coronary heart disease ( 12 ). In addition to mild to severe complications during the acute phase, COVID-19 symptoms may persist even after complete recovery, a condition known as "long COVID-19 syndrome". This phenomenon is believed to be the result of immune system dysregulation, autoimmune reactions, and viral persistence. The most commonly reported symptoms of long COVID-19 include fatigue and dyspnea, which can last for months after the initial COVID-19 infection. Other symptoms may include cognitive and mental impairments, chest and joint pains, palpitations, myalgia, anosmia and ageusia, cough, headache, and gastrointestinal and cardiac issues. In a retrospective cohort study conducted by Wong et al (2022), COVID-19 survivors had a higher risk of cerebrovascular and cardiac complications including stroke, arrhythmia, myocarditis, ischemic heart disease, heart failure, and thromboembolic disorders. Additionally, the probability of survival in COVID-19 survivors decreased significantly in all cardiovascular outcomes ( 13 ). In the present study, chest pain and dyspnea were found to be the primary reasons for referral, while other indications such as screening and evaluation of surgical candidates due to their non-emergent nature were the least common causes of referral. The persistence of long COVID-19 symptoms underscores the need for continued monitoring and management of patients after the acute phase of the illness has passed. Further research is needed to better understand the underlying mechanisms of this condition and to develop effective treatments. The findings of our study demonstrate a lack of statistically significant association between abnormal perfusion imaging and COVID-19. It is plausible that this result is attributable to the exclusion of a substantial number of patients who may have exhibited abnormal myocardial perfusion imaging. There has been considerable apprehension regarding seeking medical assistance in healthcare facilities since the onset of the COVID-19 pandemic, leading to delayed detection and treatment of medical conditions. Nappi et al (2020) investigated the impact of COVID-19 on SPECT-MPI in a single center study in italy, similar to our experience they also found no significant difference between infected and non-infected patient, claiming that 68% of patients were missed based on the fact that the MPI rate has declined in comparison to the prior years( 14 ) In our center, the number of patients referred for MPI scans did not exhibit a significant decrease. However, there was a change in patient referral indications, with a shift from non-emergency cases to those presenting with chest pain and dyspnea secondary to COVID-19. Our findings are supported by Hasnie et al.'s (2020) validation that COVID-19 does not adversely affect MPI, but the study also reported a high rate of missed abnormal patients at 81% during the study period (15). While most diagnostic parameters of the MPI scan did not exhibit significant differences between the two groups, the visualization of right ventricle was observed more frequently in COVID-19 positive patients. This finding may be attributed to the possibility of pulmonary hypertension secondary to COVID-19 pathology. Furthermore, the COVID-positive group exhibited significantly higher rates of obesity and familial history of heart disease, which may be due to physician caution and/or increased prevalence of post-COVID syndrome. In terms of MPI abnormalities, there was only a slight increase in mild and moderate ischemia in COVID-positive patients, while severe ischemia and myocardial necrosis did not differ significantly between the two groups. However, the presence of more hypokinesia could be indicative of endothelial dysfunction.One of the significant limitations of our study pertains to its single-center, retrospective design and relatively small sample size, which restricts the generalizability of our findings. Additionally, the inclusion of patients in the study using varied diagnostic methods, notwithstanding PCR being the standard diagnostic approach, introduces a potential for misdiagnosis of COVID-19 negative patients. The heterogeneity in the severity of COVID-19 among diagnosed patients and the interval (less than six month) between COVID diagnosis and myocardial perfusion imaging further challenges the interpretability of our results. Attempts were made to contact enrolled patients to address this issue; however, a vast majority of patients expressed apprehension, resulting in neglect of this matter. Due to feasibility, the control group utilized in the study was of a historical nature rather than being concurrent without having any restrictions imposed on the use of MPI. Conclusion Obese COIVD-19 patients had significantly higher rate of abnormal myocardial perfusion scan. RV visualization and hypokinesia were observed to be more frequent in COVID-positive patients in our study. This finding could be suggestive of the possible occurrence of pulmonary hypertension and endothelial dysfunction as secondary pathologies of COVID-19. The RV visualization was not statistically significant, but it could be a significant clinical finding for further research. The occurrence of hypokinesia could be an indication of endothelial dysfunction in COVID-19 patients. Further research is warranted to confirm these findings and explore the underlying mechanisms. The lack of difference in the incidence of abnormal MPI between two groups was due to the limitations may imply a markedly elevated rate of undetected patients. Declarations Availability of supporting data Please contact the corresponding author for data requests. Acknowledgements All authors would like to thank the study patients for collaboration in this project. Funding Not applicable - Authors’ contributions The concept was designed by authors BM and KR; authors SJ, SC, BM and RL were involved in data collection; SC, SJ and KR were involved in documentation for the study; the initial manuscript was drafted by KR and BM; KR and BM coordinated, supervised and critically reviewed the manuscript for intellectual content. All authors approved the final manuscript and agree upon being accountable for all aspects of the work. Ethical considerations The proposal of this study was assessed and approved by the ethics committee of Kurdistan University of Medical Sciences (Ethic code: IR.MUK.REC.1402.271). In addition, written informed consent was obtained from each patient before the data gathering. Consent for publication Written informed consent was obtained from each patient for publication of this research. Competing interests The authors reported no conflict of interest and no funding was received on this work. References Cau R, Bassareo PP, Mannelli L, Suri JS, Saba L. Imaging in COVID-19-related myocardial injury. Int J Cardiovasc Imaging. 2021;37(4):1349–60. Assante R, D'Antonio A, Mannarino T, Gaudieri V, Zampella E, Mainolfi CG, et al. Impact of COVID-19 infection on short-term outcome in patients referred to stress myocardial perfusion imaging. Eur J Nucl Med Mol Imaging. 2022;49(5):1544–52. Williams MC, Shaw L, Hirschfeld CB, Maurovich-Horvat P, Norgaard BL, Pontone G et al. Impact of COVID-19 on the imaging diagnosis of cardiac disease in Europe. Open Heart. 2021;8(2). Araz M, Soydal C, Sutcu G, Demir B, Ozkan E. Myocardial perfusion SPECT findings in postCOVID period. Eur J Nucl Med Mol Imaging. 2022;49(3):889–94. Verma A, Ramayya T, Upadhyaya A, Valenta I, Lyons M, Marschall J, et al. Post COVID-19 syndrome with impairment of flow-mediated epicardial vasodilation and flow reserve. Eur J Clin Invest. 2022;52(12):e13871. Cap M, Bilge O, Gundogan C, Tatli I, Ozturk C, Tastan E, et al. SPECT myocardial perfusion imaging identifies myocardial ischemia in patients with a history of COVID-19 without coronary artery disease. Int J Cardiovasc Imaging. 2022;38(2):447–56. Vallejo N, Teis A, Mateu L, Bayés-Genís A. Persistent chest pain after recovery of COVID-19: microvascular disease-related angina? Eur heart J Case Rep. 2021;5(3):ytab105. Hasnie UA, Bhambhvani P, Iskandrian AE, Hage FG. Prevalence of abnormal SPECT myocardial perfusion imaging during the COVID-19 pandemic. Eur J Nucl Med Mol Imaging. 2021;48:2447–54. Thornton GD, Shetye A, Knight DS, Knott K, Artico J, Kurdi H, et al. Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction. Front Cardiovasc Med. 2021;8:764599. Jung CY, Park H, Kim DW, Lim H, Chang JH, Choi YJ, et al. Association between Body Mass Index and Risk of Coronavirus Disease 2019 (COVID-19): A Nationwide Case-control Study in South Korea. Clin Infect diseases: official publication Infect Dis Soc Am. 2021;73(7):e1855–62. Bolukcu S, ÖZmen ME, EkŞÍ Ç, Okay G, SÜMbÜL B, Kacmaz AB et al. Investigation the Relationship Between Body Mass Index and Mortality in COVID-19 Patients. 2021. Peters SAE, MacMahon S, Woodward M. Obesity as a risk factor for COVID-19 mortality in women and men in the UK biobank: Comparisons with influenza/pneumonia and coronary heart disease. Diabetes Obes Metab. 2021;23(1):258–62. Wang W, Wang C-Y, Wang S-I, Wei JC-C. Long-term cardiovascular outcomes in COVID-19 survivors among non-vaccinated population: a retrospective cohort study from the TriNetX US collaborative networks. EClinicalMedicine. 2022;53:101619. Nappi C, Megna R, Acampa W, Assante R, Zampella E, Gaudieri V, et al. Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease. Eur J Nucl Med Mol Imaging. 2021;48:421–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 15 Oct, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers agreed at journal 27 Aug, 2024 Reviews received at journal 26 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers invited by journal 16 Aug, 2024 Editor invited by journal 28 Jun, 2024 Editor assigned by journal 25 Jun, 2024 Submission checks completed at journal 25 Jun, 2024 First submitted to journal 31 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4509262","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325943448,"identity":"cc241814-218b-49f8-bffd-b169537fbd57","order_by":0,"name":"Bahar Moasses-Ghafari","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bahar","middleName":"","lastName":"Moasses-Ghafari","suffix":""},{"id":325943449,"identity":"490ee99a-e590-4ea2-a13a-8403eaab07fe","order_by":1,"name":"Sahar Choupani","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Choupani","suffix":""},{"id":325943451,"identity":"87e6e43c-aeb5-48bd-bc0e-a8d7f2ea5045","order_by":2,"name":"Sajed Jahanbin","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sajed","middleName":"","lastName":"Jahanbin","suffix":""},{"id":325943456,"identity":"4a60bae7-e425-4379-97c5-6ab493bd01a9","order_by":3,"name":"Reza Lotfi","email":"","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Lotfi","suffix":""},{"id":325943458,"identity":"8cece945-2421-4e69-9b03-6d9214f97437","order_by":4,"name":"Khaled Rahmani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACgxsMjAcY2IAMEC/BQIKHnwHExQMMZzAwIGkpsJCRbCCgxVgCWQvDhwobgwMEtJhJNz848KHsnry52OFnDx4AHWZ8I/nZgw8VDPL8YgewarGROWZwcMa5YsOds9PMDUB+MbuRZm444wyD4czZCdi1SCQYHOZtS2DccDvBTAKiJcFMmrcNGBS3sWsxk0j/cPhvW4L9htvp38BajGekf8OrxVgix+AwY1tC4obbORBbDCRy8NtiOCOn4GDPuYRkoJYysBaJM2/KJGeckcDpF4Mb6Rsf/ChLsAU6bJvkjz919vzt6dskgIEtzy+NXQsWIABWKUGschDgP0CK6lEwCkbBKBgBAADNvGG0k8AqfwAAAABJRU5ErkJggg==","orcid":"","institution":"Kurdistan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Rahmani","suffix":""}],"badges":[],"createdAt":"2024-05-31 13:21:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4509262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4509262/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-04458-x","type":"published","date":"2025-01-23T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74858304,"identity":"8ca6dabe-08b4-4127-9f1c-44bdc99d0398","added_by":"auto","created_at":"2025-01-27 16:07:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":740704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4509262/v1/1a5d11c8-5e80-4bad-b881-e56313d5bc10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparing Myocardial Perfusion Scan Findings in Patients With and Without Covid-19","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome (SARS) virus, has been the prominent cause of morbidity and mortality worldwide in the last three years (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In contrast to other upper respiratory tract viruses, COVID-19 instigates multiple systemic manifestations in the body by activating the inflammatory cascade and thrombotic process. It has become increasingly evident that certain populations are at higher risk of severe illness and death (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Among these groups are individuals with underlying health conditions such as ischemic heart disease. A growing body of literature posits that COVID-19 infection may heighten the risk of cardiovascular events, including but not limited to acute coronary syndrome, myocarditis, arrhythmia, and sudden death (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCOVID-19 may affect the cardiovascular system by directly damaging the heart and endothelium, or the damage may be secondary to oxygen supply/demand imbalance, abnormal generalized inflammatory response, atherosclerotic plaque rupture and hypercoagulopathy state (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Coronary microvascular dysfunction (CMD) is defined as the clinical syndrome of angina and electrocardiographic ischemic changes in the absence of obstructive CAD. CMD during COVID-19 could be due to various causes, including damaged endothelium, microthrombi and abnormal renin-angiotensin system function (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Recovered Patients from COVID-19 may experience prolonged chest pain, tachycardia, shortness of breath and decreased activity capacity; these symptoms are linked to CMD using stress myocardial perfusion imaging (MPI)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Due to insufficient data on MPI's role in the evaluation of CMD caused by COVID-19, we conducted a study to investigate the COVID-19 impact on myocardial ischemia detected myocardial perfusion imaging.\u003c/p\u003e"},{"header":"Material and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy protocol\u003c/h2\u003e \u003cp\u003eThis was a retrospective study conducted on patients referred to the nuclear medicine center of Kowsar hospital in Sanandaj, the center for Kurdistan province in northwest of Iran, by cardiologists for myocardial perfusion imaging. The study was conducted between 6 April 2020 and 21 March 2021. Medical records of the study patients including demographic and clinical data and the results of myocardial perfusion were investigated.\u003c/p\u003e \u003cp\u003e The proposal of this study was evaluated and approved by ethical committee of Kurdistan University of Medical Science (Ethic code: IR.MUK.REC.1399.331). Written informed consent was obtained during the admission for each patient.\u003c/p\u003e \u003cp\u003eClinical definition\u003c/p\u003e \u003cp\u003eBaseline demographic information and cardiovascular risk factors of the participated patients were collected using the hospital documentations, including age, gender, hypertension, diabetes mellitus, hyperlipidemia, obesity, smoking, cancer, diagnosed coronary artery disease (CAD), familial history of CAD. Patients were considered having diabetes mellitus based on the fact that they were receiving oral hypoglycemic medications or insulin. BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 was considered obese, hypertension was defined as blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 or receiving anti-hypertensive medication, hyperlipidemia was defined total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L or receiving cholesterol lowering medication, familial history of CAD was considered positive in case that a first degree relative was diagnosed before the age of 55. Smoking was defined as previous or current use of tobacco. A patient was considered to known CAD based on the provided history of prior atherosclerotic coronary disease, myocardial infarction, chest pain or equivalent symptoms, abnormal ECG or positive cardiac biomarkers, previous Percutaneous Coronary Intervention (PCI) and coronary artery bypass graft (CABG). The lung-to-heart ratio (LHR) was determined by calculating the ratio between the average counts obtained from corresponding regions of interest positioned over the left lung and the heart, respectively. TID (Time-Intensity Dynamics) refers to the temporal changes in signal intensity or contrast enhancement observed within a specific region of interest in medical imaging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMyocardial perfusion imaging\u003c/h2\u003e \u003cp\u003eMPI was done according to the standard 2-day stress-rest protocol of the hospital; all patients were prohibited from using beta blockers, calcium channel blockers, methylxanthines, dipyridamole, tea or any Caffeine-containing product 24 hours prior to the stress phase of the scan. To induce pharmaceutical stress, 0.56mg/kg of dipyridamole was infused for 3 minutes, followed by 11 Mbq/kg of Tc-99 MIBI bolus injection at the third minute of infusion. In case of facing any side effects, the injection would be discontinued, and aminophylline would be infused; as for the resting imaging, the Tc-99 MIBI bolus injection was administered on the next day. All patients were treated with high-fat-containing foods like milk and cream, imaging was performed 75\u0026ndash;90 minutes afterwards. Gated myocardial perfusion was performed using SIEMENS SYMBIA EVO EXCEL gamma camera. 64 projection images were acquired at a 3-degree angle to have anterior orbit from a 180-degree 64 in 64 matrices by scanning the area between 45-degree right anterior oblique and 45-degree left posterior oblique projections in L-mode with low energy in supine position 75\u0026ndash;90 minutes after injection for stress images and 75\u0026ndash;90 minutes for rest images.\u003c/p\u003e \u003cp\u003e\u0026ldquo;Myovation Evolution\u0026rdquo; software (OSEM with 12 iterations, 10 subsets) was used to process the images. Images were automatically recorrected by filtered back projection and Butterworth filter (0.4 order 10) after the reconstruction of images by manual overlapping. The images were assessed by an experienced nuclear medicine specialist using QGS/QPS (Xeleris, Cedras-Sinai Medical Centre, Los Angeles, California, USA) and 4DM software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImaging interpretation\u003c/h2\u003e \u003cp\u003eThe myocardial perfusion was evaluated using a visual semiquantitative method known as the 20-segment myocardial modeling. A 5-point system was employed to assess perfusion, with scores assigned as follows: normal\u0026thinsp;=\u0026thinsp;0, mildly reduced\u0026thinsp;=\u0026thinsp;1, moderately reduced\u0026thinsp;=\u0026thinsp;2, severely reduced\u0026thinsp;=\u0026thinsp;3, and no uptake\u0026thinsp;=\u0026thinsp;4. Following the scoring of all segments, three parameters were calculated: the summed stress score (SSS), which represents the sum of scores from all 20 segments during the stress phase; a score below 3 indicates normality. The summed rest score (SRS) and the summed difference score (SDS), which is derived by subtracting SSS from SRS, were also computed. Both quantitative analysis and qualitative observations from nuclear medicine specialists were incorporated to evaluate MPI abnormality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eHistogram and Shapiro-Wilks tests were used to verify the normality of data. Continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical data were demonstrated as percentages. The student\u0026rsquo;s t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analysis was performed with IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, N.Y., USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 785 were included in this study, 363 of whom had a history of COVID-19, 35 patients were excluded due to inappropriate diagnostic methods leading to confusion on whether they were infected or not, eventually 328 were COVID-19 positive and the rest of 422 patients with negative PCR results were used as controls. The mean (SD) age of the patients was 56.12 (11.38), and 446 (59.5%) were female. Baseline demographic and clinical features of the study patients are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ebaseline demographic and clinical characteristics of the study patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCovid-19 POS. (n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCovid-19 NEG. (n\u0026thinsp;=\u0026thinsp;422)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.12 (11.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.59 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.76 (12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (42.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176(57.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200(44.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246 (55.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHTN, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (46.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211 (53.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiabetes, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eObesity, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (18.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (69.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (30.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (36.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (63.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCancer, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKnown CAD, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (34.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196 (65.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFamilial history, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (39.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161 (60.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (43.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95 (56.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHospitalization, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the COVID positive group with medical history of DM, HTN, CAD and obesity exhibited a higher rate of MPI referral compared to the non-COVID group. This difference may stem from the possibility of more severe post-COVID syndrome and complications in patients with these pre-existing conditions. Additionally, medical practitioners may exercise greater caution when evaluating patients with these risk factors. Patients with COVID had higher rate of HTN, hyperlipidemia and DM. There was no significant difference between the two groups regarding age, gender, HTN, diabetes, dyslipidemia, or cancer. However, obesity was significantly higher in COVID-19 patients. In addition, COVID-19 patients had a significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) higher known CAD rate than non-COVID-19 patients (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Chest pain was the most frequent cause of referral and screening and dyspnea were the second and third reasons respectively (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003efrequency of causes of referral (MPI indication)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCOVID-19 n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal, n(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePos.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeg.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMPI indication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182(55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229(54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e411(54.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStenting and post-CABG follow up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(15.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvaluation for stenting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScreening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156(20.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding medication use, COVID-19 patients had significantly higher CCB and diuretics use (P\u0026thinsp;=\u0026thinsp;0.02 and P\u0026thinsp;=\u0026thinsp;0.006 respectively) non-COIVD-19 patients had more beta blocker and Aspirin use (P\u0026thinsp;=\u0026thinsp;0.001 and P\u0026thinsp;=\u0026thinsp;0.01) (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of drugs use among patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 POS. (n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOVID-19 NEG. (n\u0026thinsp;=\u0026thinsp;422)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication use n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572 (76.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252 (76.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320 (75.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral hypoglycemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (25.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (28.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (24.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (5.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta blockers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (26.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161 (38.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (16.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (10.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitroglycerin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (12.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE-I/ARB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e352 (43.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (48.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (45.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (29.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (29.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 (32.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248 (33.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (28.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (36.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to specialist observation, 44.2 had ischemic MPI. Although the incidence of severe ischemia and myocardial necrosis was similar between the two groups, the COVID positive group exhibited a higher frequency of mild and moderate ischemia, although there was no statistically significant relation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, obese COVID-19 patients had a significantly higher rate of abnormal MPI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to non-obese COVID-19 patients (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Covid-19 infection and Ischemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIschemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Ischemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNecrotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovid-19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245(58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133(31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between obesity and SSS in positive and negative Covid groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eSSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMildly abnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerately abnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSeverely abnormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eObese\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eObese\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the measured variables of MPI (Myocardial Perfusion Imaging). No statistically significant correlation was found between the two groups in terms of SSS (Summed Stress Score), abnormal MPI, myocardial thickening, EF (Ejection Fraction), LHR (Left Heart Ratio), and TID. Although a higher occurrence of hypokinesia was noted in COVID patients, statistical significance was not reached. Additionally, the visibility of the right ventricle was evaluated to explore pulmonary hypertension or right ventricular hypertrophy resulting from left ventricular failure. Although a higher prevalence of right ventricular visualization was observed in COVID-19 patients, the difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.192).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference between the two study groups in measured variables of MPI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 POS. (n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOVID-19 NEG. (n\u0026thinsp;=\u0026thinsp;422)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd diastolic volume, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.55 (20.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.15 (31.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.00 (31.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd systolic volume, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.67 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.04 (24.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.51 (24.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02 (4.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79 (4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19 (5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRS, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.22 (3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.43 (7.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.87 (6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF (%)\u0026thinsp;\u0026gt;\u0026thinsp;50%, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisualized TID, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal LHR\u0026thinsp;\u0026gt;\u0026thinsp;0.4, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Gated Findings of MPI and RV visualization between the two study groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 POS. (n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOVID-19 NEG. (n\u0026thinsp;=\u0026thinsp;422)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal cardiac wall motion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal cardiac wall thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e376 (89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eright ventricular visibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRV visualization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e223 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (47.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs indicated in Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, no significant difference was observed between the two groups regarding Gated findings. Although not significant, right ventricular (RV) visualization in Covid-19 patients was higher than patients without Covid-19. Interestingly, our data demonstrated that Covid-19 positive patients who exhibited RV visualization tended to have a higher hospitalization rate compared to those without RV visualization (57.6% vs 42.4%) however it was not significant (p\u0026thinsp;=\u0026thinsp;0.192) (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the nearly two-year span since the emergence of the COVID-19 pandemic and the widespread use of vaccines globally, COVID-19 remains a significant contributor to mortality. Cardiovascular complications, including acute myocardial infarction (AMI), myocardial injury, arrhythmias, and thrombotic events, may also contribute to the morbidity and mortality of affected patients (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Myocardial perfusion imaging (MPI) is a non-invasive imaging modality employed for the diagnosis and assessment of coronary artery disease (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, the COVID-19 pandemic has raised concerns regarding the potential implications of COVID-19 on the outcomes of MPI assessments. To our knowledge, this is the first study evaluating the effect of COVID-19 on myocardial perfusion imaging in Iran.\u003c/p\u003e \u003cp\u003eThis research involved the referral of 750 patients for myocardial perfusion scans by cardiologists. A total of 328 patients (47.73%) tested positive for COVID-19. There was a higher rate of referrals among patients who had comorbid conditions such as hypertension, diabetes, and obesity. Specifically, obese COVID-19-positive patients were found to have a significantly higher rate of referral and abnormal MPI. In a study by Chan-Young Jung et al. (2021), higher BMI levels were associated with a graded susceptibility to severe COVID-19 infections (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, Bolukcu et al. found that the mean BMI level of deceased COVID-19-positive patients was 31 kg/m2 and that there was no significant difference (P\u0026thinsp;=\u0026thinsp;0.09) compared to COVID-19-negative patients However, the BMI of patients requiring intensive care was significantly higher (P\u0026thinsp;=\u0026thinsp;0.04) in the COVID-19 group (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Peters et al (2021) demonstrated that a higher BMI, waist circumference, waist-to‐hip ratio and waist‐to‐height ratio were each associated with an increased risk of death from COVID‐19, influenza/pneumonia and coronary heart disease (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to mild to severe complications during the acute phase, COVID-19 symptoms may persist even after complete recovery, a condition known as \"long COVID-19 syndrome\". This phenomenon is believed to be the result of immune system dysregulation, autoimmune reactions, and viral persistence. The most commonly reported symptoms of long COVID-19 include fatigue and dyspnea, which can last for months after the initial COVID-19 infection. Other symptoms may include cognitive and mental impairments, chest and joint pains, palpitations, myalgia, anosmia and ageusia, cough, headache, and gastrointestinal and cardiac issues. In a retrospective cohort study conducted by Wong et al (2022), COVID-19 survivors had a higher risk of cerebrovascular and cardiac complications including stroke, arrhythmia, myocarditis, ischemic heart disease, heart failure, and thromboembolic disorders. Additionally, the probability of survival in COVID-19 survivors decreased significantly in all cardiovascular outcomes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In the present study, chest pain and dyspnea were found to be the primary reasons for referral, while other indications such as screening and evaluation of surgical candidates due to their non-emergent nature were the least common causes of referral. The persistence of long COVID-19 symptoms underscores the need for continued monitoring and management of patients after the acute phase of the illness has passed. Further research is needed to better understand the underlying mechanisms of this condition and to develop effective treatments.\u003c/p\u003e \u003cp\u003eThe findings of our study demonstrate a lack of statistically significant association between abnormal perfusion imaging and COVID-19. It is plausible that this result is attributable to the exclusion of a substantial number of patients who may have exhibited abnormal myocardial perfusion imaging. There has been considerable apprehension regarding seeking medical assistance in healthcare facilities since the onset of the COVID-19 pandemic, leading to delayed detection and treatment of medical conditions. Nappi et al (2020) investigated the impact of COVID-19 on SPECT-MPI in a single center study in italy, similar to our experience they also found no significant difference between infected and non-infected patient, claiming that 68% of patients were missed based on the fact that the MPI rate has declined in comparison to the prior years(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) In our center, the number of patients referred for MPI scans did not exhibit a significant decrease. However, there was a change in patient referral indications, with a shift from non-emergency cases to those presenting with chest pain and dyspnea secondary to COVID-19. Our findings are supported by Hasnie et al.'s (2020) validation that COVID-19 does not adversely affect MPI, but the study also reported a high rate of missed abnormal patients at 81% during the study period (15).\u003c/p\u003e \u003cp\u003eWhile most diagnostic parameters of the MPI scan did not exhibit significant differences between the two groups, the visualization of right ventricle was observed more frequently in COVID-19 positive patients. This finding may be attributed to the possibility of pulmonary hypertension secondary to COVID-19 pathology. Furthermore, the COVID-positive group exhibited significantly higher rates of obesity and familial history of heart disease, which may be due to physician caution and/or increased prevalence of post-COVID syndrome.\u003c/p\u003e \u003cp\u003eIn terms of MPI abnormalities, there was only a slight increase in mild and moderate ischemia in COVID-positive patients, while severe ischemia and myocardial necrosis did not differ significantly between the two groups. However, the presence of more hypokinesia could be indicative of endothelial dysfunction.One of the significant limitations of our study pertains to its single-center, retrospective design and relatively small sample size, which restricts the generalizability of our findings. Additionally, the inclusion of patients in the study using varied diagnostic methods, notwithstanding PCR being the standard diagnostic approach, introduces a potential for misdiagnosis of COVID-19 negative patients. The heterogeneity in the severity of COVID-19 among diagnosed patients and the interval (less than six month) between COVID diagnosis and myocardial perfusion imaging further challenges the interpretability of our results. Attempts were made to contact enrolled patients to address this issue; however, a vast majority of patients expressed apprehension, resulting in neglect of this matter. Due to feasibility, the control group utilized in the study was of a historical nature rather than being concurrent without having any restrictions imposed on the use of MPI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eObese COIVD-19 patients had significantly higher rate of abnormal myocardial perfusion scan. RV visualization and hypokinesia were observed to be more frequent in COVID-positive patients in our study. This finding could be suggestive of the possible occurrence of pulmonary hypertension and endothelial dysfunction as secondary pathologies of COVID-19. The RV visualization was not statistically significant, but it could be a significant clinical finding for further research. The occurrence of hypokinesia could be an indication of endothelial dysfunction in COVID-19 patients. Further research is warranted to confirm these findings and explore the underlying mechanisms. The lack of difference in the incidence of abnormal MPI between two groups was due to the limitations may imply a markedly elevated rate of undetected patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAvailability of supporting data\u003c/h3\u003e\n\u003cp\u003ePlease contact the corresponding author for data requests.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eAll authors would like to thank the study patients for collaboration in this project.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concept was designed by authors BM and KR; authors SJ, SC, BM and RL were involved in data collection; SC, SJ and KR were involved in documentation for the study; the initial manuscript was drafted by KR and BM; KR and BM coordinated, supervised and critically reviewed the manuscript for intellectual content. All authors approved the final manuscript and agree upon being accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The proposal of this study was assessed and approved by the ethics committee of Kurdistan University of Medical Sciences (Ethic code: IR.MUK.REC.1402.271). In addition, written informed consent was obtained from each patient before the data gathering.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from each patient for publication of this research.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors reported no conflict of interest and no funding was received on this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCau R, Bassareo PP, Mannelli L, Suri JS, Saba L. Imaging in COVID-19-related myocardial injury. Int J Cardiovasc Imaging. 2021;37(4):1349\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssante R, D'Antonio A, Mannarino T, Gaudieri V, Zampella E, Mainolfi CG, et al. Impact of COVID-19 infection on short-term outcome in patients referred to stress myocardial perfusion imaging. Eur J Nucl Med Mol Imaging. 2022;49(5):1544\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams MC, Shaw L, Hirschfeld CB, Maurovich-Horvat P, Norgaard BL, Pontone G et al. Impact of COVID-19 on the imaging diagnosis of cardiac disease in Europe. Open Heart. 2021;8(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraz M, Soydal C, Sutcu G, Demir B, Ozkan E. Myocardial perfusion SPECT findings in postCOVID period. Eur J Nucl Med Mol Imaging. 2022;49(3):889\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma A, Ramayya T, Upadhyaya A, Valenta I, Lyons M, Marschall J, et al. Post COVID-19 syndrome with impairment of flow-mediated epicardial vasodilation and flow reserve. Eur J Clin Invest. 2022;52(12):e13871.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCap M, Bilge O, Gundogan C, Tatli I, Ozturk C, Tastan E, et al. SPECT myocardial perfusion imaging identifies myocardial ischemia in patients with a history of COVID-19 without coronary artery disease. Int J Cardiovasc Imaging. 2022;38(2):447\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVallejo N, Teis A, Mateu L, Bay\u0026eacute;s-Gen\u0026iacute;s A. Persistent chest pain after recovery of COVID-19: microvascular disease-related angina? Eur heart J Case Rep. 2021;5(3):ytab105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasnie UA, Bhambhvani P, Iskandrian AE, Hage FG. Prevalence of abnormal SPECT myocardial perfusion imaging during the COVID-19 pandemic. Eur J Nucl Med Mol Imaging. 2021;48:2447\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornton GD, Shetye A, Knight DS, Knott K, Artico J, Kurdi H, et al. Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction. Front Cardiovasc Med. 2021;8:764599.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung CY, Park H, Kim DW, Lim H, Chang JH, Choi YJ, et al. Association between Body Mass Index and Risk of Coronavirus Disease 2019 (COVID-19): A Nationwide Case-control Study in South Korea. Clin Infect diseases: official publication Infect Dis Soc Am. 2021;73(7):e1855\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolukcu S, \u0026Ouml;Zmen ME, EkŞ\u0026Iacute; \u0026Ccedil;, Okay G, S\u0026Uuml;Mb\u0026Uuml;L B, Kacmaz AB et al. Investigation the Relationship Between Body Mass Index and Mortality in COVID-19 Patients. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters SAE, MacMahon S, Woodward M. Obesity as a risk factor for COVID-19 mortality in women and men in the UK biobank: Comparisons with influenza/pneumonia and coronary heart disease. Diabetes Obes Metab. 2021;23(1):258\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Wang C-Y, Wang S-I, Wei JC-C. Long-term cardiovascular outcomes in COVID-19 survivors among non-vaccinated population: a retrospective cohort study from the TriNetX US collaborative networks. EClinicalMedicine. 2022;53:101619.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNappi C, Megna R, Acampa W, Assante R, Zampella E, Gaudieri V, et al. Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease. Eur J Nucl Med Mol Imaging. 2021;48:421\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Covid-19, SARS-COV-2, MPI, Ischemic heart disease","lastPublishedDoi":"10.21203/rs.3.rs-4509262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4509262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aim:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCovid-19 is a pandemic viral disease involving multi organ systems including cardiovascular system, directly or indirectly. SPECT GATED MPI is a non-invasive nuclear medicine imaging modality to evaluate the myocardial perfusion and function. The aim of this study was to assess the effect of Covid-19 pandemic on myocardial perfusion imaging for ischemic heart disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e This was a cross-sectional (descriptive-analytical) study conducted on 750 patients needing myocardial perfusion imaging (MPI) who referred to the nuclear medicine center of Kowsar hospital, a teaching hospital in Sanandaj, the center for Kurdistan province located in northwest of Iran. Data collection was done during the Covid-19 pandemic, between 6 April 2020 and 21 March 2021. Data analyses were conducted in SPSS using independent sample T test and Chi-square.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e A total of 750 patients, including 328 (43.7%) Covid-19 positive and 422 (56.3%) Covid-19 negative, were entered in the analysis. Although Covid-19 infected Obese patients had significantly higher rate of abnormal MPI (P\u0026lt; 0.0001), no significant difference was observed between the two groups regarding abnormal MPI (p=0.551).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The absence of a significant discrepancy in abnormal MPI occurrence between the two groups suggests that there may be Covid-19 patients with potentially abnormal MPI who have gone undetected. Additionally, Covid-19 patients with pleural chest pain, myalgia, or dyspnea could have been misdiagnosed with chest pain secondary to heart disease.\u003c/p\u003e","manuscriptTitle":"Comparing Myocardial Perfusion Scan Findings in Patients With and Without Covid-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 07:40:55","doi":"10.21203/rs.3.rs-4509262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-15T11:57:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-10-07T08:43:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296455643717451683898829195113876473736","date":"2024-08-27T06:01:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-26T19:47:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T00:29:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210107725682903022299846068882162896000","date":"2024-08-18T15:03:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238888441872115939050285189076476153580","date":"2024-08-16T13:03:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-16T10:57:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-28T12:08:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-25T05:27:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-25T05:26:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-05-31T13:20:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4e69bcd-c248-4694-97fd-dfa2415a05d5","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T15:59:42+00:00","versionOfRecord":{"articleIdentity":"rs-4509262","link":"https://doi.org/10.1186/s12872-024-04458-x","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-01-23 15:57:05","publishedOnDateReadable":"January 23rd, 2025"},"versionCreatedAt":"2024-07-16 07:40:55","video":"","vorDoi":"10.1186/s12872-024-04458-x","vorDoiUrl":"https://doi.org/10.1186/s12872-024-04458-x","workflowStages":[]},"version":"v1","identity":"rs-4509262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4509262","identity":"rs-4509262","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}