Evaluating the predictive value of aortic propagation index on the incidence of cardiovascular events in asymptomatic patients with ASCVD risk score greater than ten

Evaluating the predictive value of aortic propagation index on the incidence of cardiovascular events in asymptomatic patients with ASCVD risk score greater than ten | 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 Article Evaluating the predictive value of aortic propagation index on the incidence of cardiovascular events in asymptomatic patients with ASCVD risk score greater than ten Leila Bigdelu, Mostafa Ahmadi, Bahram Shahri, Mohammad Ali Yaghoubi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7589300/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: Prediction of cardiovascular diseases (CVD) is a challenge that cardiologists has faced all the times. Aortic propagation velocity (APV) is an echocardiography index that can be helpful with this regard; however, few studies have addressed the condition. Methods: This longitudinal study was conducted on cases with ASCVD score of >10. All the cases underwent echocardiography and APV was measured for them. The cases were classified into two groups including normal APV (>56 cm/s) and abnormal APV (≤56 cm/s). They were followed for a duration of one year and the rate of CVD was compared between the two groups. Moreover, ROC curve analysis was used for determining a cut-off. Results: Totally 60 cases including 37 (61.7%) males and 23 (38.3%) were entered in the study. At the end of one-year follow up, 7 cases developed CVD that 6 were in abnormal APV group and only 1 was in normal APV group (p=0.039). Logistic regression analysis showed that there was a significant relationship between APV and CVD development (OR=9.143; 95%CI= (3.932-1.116); p=0.047). ROC curve analysis proposed the cut off of 49.75 cm.s with a sensitivity of 85.7% and specificity of 60% (AUC=0.710; 95%CI= (0.835-0.585); p=0.072). Conclusion: APV can be used as a prognostic marker for CVD. Clinical trial number: not applicable. Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Aortic propagation velocity (APV) ASCVD risk score echocardiography Figures Figure 1 Introduction Cardiovascular diseases (CVD) remain a leading cause of mortality worldwide, accounting for more than 19 million deaths globally in 2021 [ 1 ]. In the United States, CVD was responsible for approximately 697,000 deaths in 2020, representing one in every five deaths [ 2 ]. Similarly, CVDs constitute the foremost cause of death in Iran, accounting for about 45% of all deaths [ 3 , 4 ]. In 2021, CVDs accounted for nearly 30% of total deaths and contributed to 15% of disability-adjusted life years (DALYs) in Iran, highlighting a significant health and economic burden [ 4 ]. CVDs are responsible for 161000 deaths in Iran which constitutes 45.45% of all deaths. In fact, it is the first leading cause of mortality in Iran [ 3 ]. It is also reported that CVDs poses an economic burden of 6700 billion IRR in Iran. part of this high burden is due to the premature death and loss of years of productivity [ 5 ]. This burden is compounded by risk factors such as hypertension, diabetes, obesity, and unhealthy lifestyle behaviors prevalent in different regions [ 6 ]. Risk stratification tools such as the Atherosclerotic Cardiovascular Disease (ASCVD) risk score are valuable in identifying individuals at elevated risk for cardiovascular events. However, the ASCVD risk score has limitations, including possible overestimation of risk in certain populations, prompting ongoing research into novel predictive markers to enhance risk assessment [ 7 – 9 ]. Echocardiography, a safe and non-invasive modality, offers potential for such advances by assessing cardiac structure and function [ 10 , 11 ]. Echocardiography is a non-invasive method with no harmful radiation that can assess the structure and dynamic of the heart [ 12 ]. With this regard, this tool can be used as a diagnostic, prognostic, and even screening method. Aortic propagation velocity (APV) is an echocardiography index investigated for this reason [ 13 ]. Aortic propagation velocity (APV), measured via color M-mode echocardiography, represents a promising index indicative of arterial stiffness and vascular health [ 14 , 15 ]. APV is calculated by dividing the distance propagated along the aorta during systole by the corresponding time interval, expressed in cm/s [ 15 ]. Although various prognostic cut-offs for APV have been proposed, consensus is lacking, warranting further investigation [ 16 , 17 ]. Our study aims to evaluate the predictive value of APV for cardiovascular events in asymptomatic patients with an ASCVD risk score greater than 10 over a one-year follow-up. Material and methods Study population and design This prospective cohort study was conducted in outpatient clinic of Imam Reza hospital. The included cases should have a ASCVD score of > 10 and should consent for a follow-up time of one years. Volunteers with valvular stenosis or insufficiency, cardiomyopathies, atrial fibrillation, atrial flutter, and other arrythmias, those with history of myocardial infarction, cases with congenital heart diseases, those with bundle branch block in electrocardiography, aortic dilation of > 40 mm, serum creatinine of ≥ 2 mg/dl or those needed dialysis, and cases with inappropriate echocardiography images were excluded. Data gathering Demographic data including age and gender were extracted. Moreover, underlying diseases including diabetes, hypertension, and obesity were recorded. Also, social history (smoking), statin usage, and aspirin usage were assessed. Furthermore, systolic and diastolic pressures along with laboratory data including LDL, HDL, TG, and total cholesterol were measured. Echocardiography All the patients underwent routine standard echocardiography by an expert echocardiography fellowship, during electrocardiography and blood pressure tracking. Moreover, the systolic and diastolic diameters of descending aorta were measured 3 cm above the aortic valve, using M-mode. The systolic diameter was measured, when the valve was open, while the diastolic diameter was measured according to the QRS peak. The data were measured during 3 cardiac cycles and the final data were the mean of the recorded measurements. Moreover, M-Mode color doppler echocardiography from proximal part of descending aorta at suprasternal window was performed. While the doppler curser was parallel with the main flow of descending aorta, Nyquist limit was reduced 30 to 50 cm/s and Aortic propagation flow was demonstrated by switching to M-Mode and the slope was measured by Alizing at the Sweep rate of 200 mm/s. The value of APV was calculated by dividing the distance by the time during flow emission by measuring the velocity gradient by an echocardiography machine, and the final value was the average of at least three consecutive calculations. The included cases were grouped into two groups according to the APV value. Those with APV ≤ 56 cm/s were considered as abnormal APV value and the other group was considered as the normal APV value [ 18 ]. The volunteers were followed for a duration of one year and were asked to contact the researcher, when they had a CVD. Moreover, the researcher contacted the cases to assess the CVD occurrence at the end of follow up. The two groups were compared regarding the occurrence of CVD. Ethics Those who were volunteer were asked for written informed consent. Moreover, the cases were free to discontinue the study when they want to do so. All the steps of the study were in accordance with Helsinki’s declaration and was approved by ethics committee of Mashhad University of Medical Sciences. Results Totally 60 cases with a mean age of 55.7 ± 2.57 years old were enrolled in the study. Among these 37 (61.7%) were male and the remainder were female. Table 1 shows the comparison of the gender, underlying diseases, used medication, and development of CVD after one year between the two study groups. At the end of one-year follow up, 7 cases developed CVD that 6 were in abnormal APV group and only 1 was in normal APV group. This difference was statistically significant (p = 0.039). Table 1 comparison of gender, underlying diseases, used medication, and development of CVD after one year between the two study groups Feature Group p value Normal APV N (%) Abnormal APV N (%) Gender (Male) 20 (60.6) 17 (63.0) > 0.999 Diabetes 20 (60.6) 19 (70.4) 0.587 Hypertension 28 (84.8) 22 (81.5) 0.742 Obesity 29 (87.9) 23 (85.2) > 0.999 Smoking 21 (63.6) 14 (51.9) 0.434 Aspirin usage 9 (27.3) 9 (33.3) 0.778 Statin usage 13 (39.4) 12 (44.4) 0.794 CVD after 1 year follow up 1 (3.0) 6 (22.2) 0.039 Table 2 shows the comparison of different quantitative values between normal and abnormal APV groups. The mean APV value in normal APV group was 70.85 ± 5.04 cm.s and in abnormal APV was 38.42 ± 9.09 cm.s (p < 0.001). Table 2 comparison of different quantitative values between normal and abnormal APV groups Feature Group p value Normal APV Abnormal APV N Age (years; median (IQR)) 56 (58 − 54) 55 (57 − 53) 0.344 SBP (mmHg; median (IQR)) 135 (140 − 132) 135 (140 − 135) 0.679 DBP (mmHg; median (IQR)) 85 (85 − 80) 85 (85 − 80) 0.585 LDL (mg/dl; median (IQR)) 142 (170 − 130) 142 (176 − 130) 0.982 HDL (mg/dl; median (IQR)) 44 (46 − 42) 45 (45 − 42) 0.780 Triglyceride (mg/dl; median (IQR)) 284 (297-258.5) 289 (300 − 254) 0.422 Total cholesterol (mg/dl; median (IQR)) 248 (268 − 211) 250 (278 − 211) 0.351 ASCVD (mean ± SD) 19.78 ± 7.50 19.90 ± 8.56 0.955 APV (cm.s; mean ± SD) 70.85 ± 5.04 38.42 ± 9.09 < 0.001 Ejection fraction (percent; median (IQR)) 57 (59.5–56) 57 (59 − 55) 0.290 Systolic aortic diameter (cm; median (IQR)) 3.2 (3.38-3) 3.2 (3.28–2.9) 0.319 Diastolic aortic diameter (cm; median (IQR)) 2.9 (3.04–2.75) 2.9 (3-2.7) 0.393 Posterior wall thickness (mm; mean ± SD) 7.80 ± 0.60 8.00 ± 0.74 0.245 Septal thickness (mm; mean ± SD) 7.83 ± 0.54 8.05 ± 0.73 0.189 Logistic regression analysis showed that there was a significant relationship between APV and CVD development (OR = 9.143; 95%CI= (3.932–1.116); p = 0.047). Table 3 shows the details of the results. Table 3 Logistic regression analysis in order to assess the relationship of APV with CVD development Feature Odd ratio 95% CI p value APV 9.143 (1.116–3.932) 0.047 Figure 1 shows the ROC curve. Accordingly, the best proposed prognostic cut off was 49.75 cm.s with a sensitivity of 85.7% and specificity of 60% (AUC = 0.710; 95%CI= (0.835 − 0.585); p = 0.072). Discussion The matter with cardiovascular diseases is to risk stratify in order to assess the prognosis of the at-risk individuals [ 19 ]. Atherosclerotic cardiovascular disease (ASCVD) risk scoring is an easy method that can predict the ten-year risk of CVD development. According to this risk assessment method, the cases are grouped to the low risk (ASCVD < 5%), borderline risk (5 ≤ ASCVD < 7.5%), intermediate risk (7.5 ≤ ASCVD < 20%), and high risk (ASCVD ≥ 20%) [ 20 ]. The low-risk group only advised to modify their life style, while the high-risk group received medical treatment. However, cases in borderline and intermediate groups should undergo life style changes along with further follow up and risk stratification [ 21 ]. However, the challenges in case of accuracy of ASCVD score have remained, as it is believed that the risk score may overestimate the CVD risk [ 22 , 23 ]. With this regard, the development of other prognostic factors can help the assessment of CVD prognosis. Echocardiography can be a useful method, as has no adverse radiation and can provide structural and functional data about the heart [ 24 ]. AVP is an index that has been investigated in case of having prognostic value for CVD [ 25 ]. We focused on the utility of the APV in case of predicting the risk of CVD in asymptomatic patients with ASCVD score of more than 10. Our results demonstrated that although the ASCVD score showed no significant difference between normal and abnormal APV groups, the occurrence of CVD in abnormal APV group was significantly higher. Moreover, logistic regression showed that there was a significant relationship between APV and CVD occurrence. The best propose cut-off regarding predicting CVD was 49.75 cm/s with a sensitivity of 85.7% and specificity of 60%. There are several similar studies that proposed different cut-off with this regard. Güneş et al. [ 26 ] first proposed the prognostic role of APV. They developed the cut off of 41 cm/s with 82.4% sensitivity and 97.2% specificity for CAD predicting. In a recent study Ghaderi et al. [ 18 ] proposed that there is an inverse relationship between APV and CVD development. They reported the cut-off of 56 cm/s with a sensitivity of 96.9% and specificity of 78.9%. Chetty et al. [ 15 ] studied the difference of APV between those with significant CAD and those without this condition, according to the angiography. They reported that patients with significant CAD had notably lower APV values. The cut off of 47.5 cm/s with a sensitivity of 76% and specificity of 72% was calculated in their study. However, Arı et al. [ 27 ] proposed controversial results. They reported that APV has no correlation with classical aortic stiffness echocardiography parameters and they concluded that it can not be a marker of atherosclerosis. Still, making a conclusion based on only one contrary study is not reasonable. In fact, it seems that APV can be an easy and useful marker of CVD. However, more studies are needed to reach a consensus regarding the proposed cut-off. It seems that along the vascular aging and atherosclerosis progression, the vascular elasticity lowers. This arterial stiffness has relationship with risk factors like cigarette smoking, hypertension, obesity, diabetes, lipid profile changes, and age [ 28 , 29 ]. Arterial affect the structure and functionality of vascular system and therefore, APV as a marker of vascular function and structure can be helpful [ 30 ]. One of the strengths of our study was the fact that we studied all CVD cases and did not focus on only CAD. The other positive point of our investigation was the study of asymptomatic cases with ASCVD score more than ten. Other studies in the literature mainly focused on the patients that needed angiography. Still, our study was limited to a follow-up time of only one year and higher follow up time may yield more concise results. Conclusion We found APV as a useful prognostic echocardiography marker in risk assessment of CVD development. The proposed cut-off in our study was 49.75 cm/s with a good sensitivity and specificity. However, the studies in the literature reported different cut-offs. With this regard, we advise the researcher of this field to conduct other similar studies with higher follow up time to make a conclusion in this case. Declarations Ethics approval and consent to participate This study was approved by the ethical committee of the medical faculty of Mashhad University of Medical Sciences. All patients filled a written informed consent for participation in the study. Consent to publish Not applicable. Availability of data and material Data are available from the authors upon reasonable request and with permission of the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This study is funded by Mashhad University of Medical Sciences. Contribution L. B., MA. Y., M. A., B. Sh. and Mo. M. analyzed and interpreted the patient data regarding the cardiovascular disease and managed patients. Mo.M., M.K. and F.K. helped in management of patient, diagnosis and was a major contributor in writing the manuscript. All authors read and approved the final manuscript. Acknowledgements None. 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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-7589300","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525643612,"identity":"8c2c24c0-0916-4155-ac2c-11df5ae1a16a","order_by":0,"name":"Leila Bigdelu","email":"","orcid":"","institution":"Mashhad University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Leila","middleName":"","lastName":"Bigdelu","suffix":""},{"id":525643613,"identity":"0219a5ed-2409-4a97-990e-242dc2551800","order_by":1,"name":"Mostafa Ahmadi","email":"","orcid":"","institution":"Mashhad University of Medical 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07:15:48","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77013,"visible":true,"origin":"","legend":"","description":"","filename":"0dbc2178eec24ceca99af00162ab15bc1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7589300/v1/99bedb29d8d385c7ae381043.xml"},{"id":93013015,"identity":"14848dfc-bd08-4685-8c0d-903ba3782795","added_by":"auto","created_at":"2025-10-08 07:23:48","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87511,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7589300/v1/42f4ca04b5be40310622d93d.html"},{"id":93010971,"identity":"920f77ad-9e17-4120-a733-e1412ea198a7","added_by":"auto","created_at":"2025-10-08 07:15:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7589300/v1/8b6f8e6dcf4434214898dbea.png"},{"id":93013879,"identity":"90d997d6-2382-42aa-981a-695cc58e6514","added_by":"auto","created_at":"2025-10-08 07:31:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2127542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7589300/v1/bdee3633-03ba-44f6-aa72-57bbeefb8279.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the predictive value of aortic propagation index on the incidence of cardiovascular events in asymptomatic patients with ASCVD risk score greater than ten","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVD) remain a leading cause of mortality worldwide, accounting for more than 19\u0026nbsp;million deaths globally in 2021 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States, CVD was responsible for approximately 697,000 deaths in 2020, representing one in every five deaths [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Similarly, CVDs constitute the foremost cause of death in Iran, accounting for about 45% of all deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2021, CVDs accounted for nearly 30% of total deaths and contributed to 15% of disability-adjusted life years (DALYs) in Iran, highlighting a significant health and economic burden [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. CVDs are responsible for 161000 deaths in Iran which constitutes 45.45% of all deaths. In fact, it is the first leading cause of mortality in Iran [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is also reported that CVDs poses an economic burden of 6700\u0026nbsp;billion IRR in Iran. part of this high burden is due to the premature death and loss of years of productivity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This burden is compounded by risk factors such as hypertension, diabetes, obesity, and unhealthy lifestyle behaviors prevalent in different regions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRisk stratification tools such as the Atherosclerotic Cardiovascular Disease (ASCVD) risk score are valuable in identifying individuals at elevated risk for cardiovascular events. However, the ASCVD risk score has limitations, including possible overestimation of risk in certain populations, prompting ongoing research into novel predictive markers to enhance risk assessment [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Echocardiography, a safe and non-invasive modality, offers potential for such advances by assessing cardiac structure and function [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEchocardiography is a non-invasive method with no harmful radiation that can assess the structure and dynamic of the heart [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. With this regard, this tool can be used as a diagnostic, prognostic, and even screening method. Aortic propagation velocity (APV) is an echocardiography index investigated for this reason [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Aortic propagation velocity (APV), measured via color M-mode echocardiography, represents a promising index indicative of arterial stiffness and vascular health [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. APV is calculated by dividing the distance propagated along the aorta during systole by the corresponding time interval, expressed in cm/s [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although various prognostic cut-offs for APV have been proposed, consensus is lacking, warranting further investigation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our study aims to evaluate the predictive value of APV for cardiovascular events in asymptomatic patients with an ASCVD risk score greater than 10 over a one-year follow-up.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population and design\u003c/h2\u003e\u003cp\u003eThis prospective cohort study was conducted in outpatient clinic of Imam Reza hospital. The included cases should have a ASCVD score of \u0026gt;\u0026thinsp;10 and should consent for a follow-up time of one years. Volunteers with valvular stenosis or insufficiency, cardiomyopathies, atrial fibrillation, atrial flutter, and other arrythmias, those with history of myocardial infarction, cases with congenital heart diseases, those with bundle branch block in electrocardiography, aortic dilation of \u0026gt;\u0026thinsp;40 mm, serum creatinine of \u0026ge;\u0026thinsp;2 mg/dl or those needed dialysis, and cases with inappropriate echocardiography images were excluded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData gathering\u003c/h3\u003e\n\u003cp\u003eDemographic data including age and gender were extracted. Moreover, underlying diseases including diabetes, hypertension, and obesity were recorded. Also, social history (smoking), statin usage, and aspirin usage were assessed. Furthermore, systolic and diastolic pressures along with laboratory data including LDL, HDL, TG, and total cholesterol were measured.\u003c/p\u003e\n\u003ch3\u003eEchocardiography\u003c/h3\u003e\n\u003cp\u003eAll the patients underwent routine standard echocardiography by an expert echocardiography fellowship, during electrocardiography and blood pressure tracking. Moreover, the systolic and diastolic diameters of descending aorta were measured 3 cm above the aortic valve, using M-mode. The systolic diameter was measured, when the valve was open, while the diastolic diameter was measured according to the QRS peak. The data were measured during 3 cardiac cycles and the final data were the mean of the recorded measurements. Moreover, M-Mode color doppler echocardiography from proximal part of descending aorta at suprasternal window was performed. While the doppler curser was parallel with the main flow of descending aorta, Nyquist limit was reduced 30 to 50 cm/s and Aortic propagation flow was demonstrated by switching to M-Mode and the slope was measured by Alizing at the Sweep rate of 200 mm/s. The value of APV was calculated by dividing the distance by the time during flow emission by measuring the velocity gradient by an echocardiography machine, and the final value was the average of at least three consecutive calculations.\u003c/p\u003e\u003cp\u003eThe included cases were grouped into two groups according to the APV value. Those with APV\u0026thinsp;\u0026le;\u0026thinsp;56 cm/s were considered as abnormal APV value and the other group was considered as the normal APV value [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The volunteers were followed for a duration of one year and were asked to contact the researcher, when they had a CVD. Moreover, the researcher contacted the cases to assess the CVD occurrence at the end of follow up. The two groups were compared regarding the occurrence of CVD.\u003c/p\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003e Those who were volunteer were asked for written informed consent. Moreover, the cases were free to discontinue the study when they want to do so. All the steps of the study were in accordance with Helsinki\u0026rsquo;s declaration and was approved by ethics committee of Mashhad University of Medical Sciences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTotally 60 cases with a mean age of 55.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57 years old were enrolled in the study. Among these 37 (61.7%) were male and the remainder were female. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the comparison of the gender, underlying diseases, used medication, and development of CVD after one year between the two study groups. At the end of one-year follow up, 7 cases developed CVD that 6 were in abnormal APV group and only 1 was in normal APV group. This difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.039).\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\u003ecomparison of gender, underlying diseases, used medication, and development of CVD after one year between the two study groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal APV N (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbnormal APV N (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (70.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22 (81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.742\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29 (87.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (85.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatin usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD after 1 year follow up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\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=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the comparison of different quantitative values between normal and abnormal APV groups. The mean APV value in normal APV group was 70.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04 cm.s and in abnormal APV was 38.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09 cm.s (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003ecomparison of different quantitative values between normal and abnormal APV groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal APV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAbnormal APV N\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (58\u0026thinsp;\u0026minus;\u0026thinsp;54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (57\u0026thinsp;\u0026minus;\u0026thinsp;53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (140\u0026thinsp;\u0026minus;\u0026thinsp;132)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (140\u0026thinsp;\u0026minus;\u0026thinsp;135)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (85\u0026thinsp;\u0026minus;\u0026thinsp;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (85\u0026thinsp;\u0026minus;\u0026thinsp;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL (mg/dl; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (170\u0026thinsp;\u0026minus;\u0026thinsp;130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (176\u0026thinsp;\u0026minus;\u0026thinsp;130)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mg/dl; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (46\u0026thinsp;\u0026minus;\u0026thinsp;42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45 (45\u0026thinsp;\u0026minus;\u0026thinsp;42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride (mg/dl; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e284 (297-258.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e289 (300\u0026thinsp;\u0026minus;\u0026thinsp;254)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dl; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248 (268\u0026thinsp;\u0026minus;\u0026thinsp;211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250 (278\u0026thinsp;\u0026minus;\u0026thinsp;211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASCVD (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.78\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.90\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPV (cm.s; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEjection fraction (percent; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (59.5\u0026ndash;56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (59\u0026thinsp;\u0026minus;\u0026thinsp;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic aortic diameter (cm; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.2 (3.38-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2 (3.28\u0026ndash;2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.319\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic aortic diameter (cm; median (IQR))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.9 (3.04\u0026ndash;2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9 (3-2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePosterior wall thickness (mm; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptal thickness (mm; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.189\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\u003eLogistic regression analysis showed that there was a significant relationship between APV and CVD development (OR\u0026thinsp;=\u0026thinsp;9.143; 95%CI= (3.932\u0026ndash;1.116); p\u0026thinsp;=\u0026thinsp;0.047). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the details of the results.\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\u003eLogistic regression analysis in order to assess the relationship of APV with CVD development\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdd ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003eAPV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.116\u0026ndash;3.932)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.047\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\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the ROC curve. Accordingly, the best proposed prognostic cut off was 49.75 cm.s with a sensitivity of 85.7% and specificity of 60% (AUC\u0026thinsp;=\u0026thinsp;0.710; 95%CI= (0.835\u0026thinsp;\u0026minus;\u0026thinsp;0.585); p\u0026thinsp;=\u0026thinsp;0.072).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe matter with cardiovascular diseases is to risk stratify in order to assess the prognosis of the at-risk individuals [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Atherosclerotic cardiovascular disease (ASCVD) risk scoring is an easy method that can predict the ten-year risk of CVD development. According to this risk assessment method, the cases are grouped to the low risk (ASCVD\u0026thinsp;\u0026lt;\u0026thinsp;5%), borderline risk (5\u0026thinsp;\u0026le;\u0026thinsp;ASCVD\u0026thinsp;\u0026lt;\u0026thinsp;7.5%), intermediate risk (7.5\u0026thinsp;\u0026le;\u0026thinsp;ASCVD\u0026thinsp;\u0026lt;\u0026thinsp;20%), and high risk (ASCVD\u0026thinsp;\u0026ge;\u0026thinsp;20%) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The low-risk group only advised to modify their life style, while the high-risk group received medical treatment. However, cases in borderline and intermediate groups should undergo life style changes along with further follow up and risk stratification [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the challenges in case of accuracy of ASCVD score have remained, as it is believed that the risk score may overestimate the CVD risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. With this regard, the development of other prognostic factors can help the assessment of CVD prognosis.\u003c/p\u003e\u003cp\u003eEchocardiography can be a useful method, as has no adverse radiation and can provide structural and functional data about the heart [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. AVP is an index that has been investigated in case of having prognostic value for CVD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We focused on the utility of the APV in case of predicting the risk of CVD in asymptomatic patients with ASCVD score of more than 10. Our results demonstrated that although the ASCVD score showed no significant difference between normal and abnormal APV groups, the occurrence of CVD in abnormal APV group was significantly higher. Moreover, logistic regression showed that there was a significant relationship between APV and CVD occurrence. The best propose cut-off regarding predicting CVD was 49.75 cm/s with a sensitivity of 85.7% and specificity of 60%.\u003c/p\u003e\u003cp\u003eThere are several similar studies that proposed different cut-off with this regard. G\u0026uuml;neş et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] first proposed the prognostic role of APV. They developed the cut off of 41 cm/s with 82.4% sensitivity and 97.2% specificity for CAD predicting. In a recent study Ghaderi et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] proposed that there is an inverse relationship between APV and CVD development. They reported the cut-off of 56 cm/s with a sensitivity of 96.9% and specificity of 78.9%. Chetty et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] studied the difference of APV between those with significant CAD and those without this condition, according to the angiography. They reported that patients with significant CAD had notably lower APV values. The cut off of 47.5 cm/s with a sensitivity of 76% and specificity of 72% was calculated in their study. However, Arı et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] proposed controversial results. They reported that APV has no correlation with classical aortic stiffness echocardiography parameters and they concluded that it can not be a marker of atherosclerosis. Still, making a conclusion based on only one contrary study is not reasonable.\u003c/p\u003e\u003cp\u003eIn fact, it seems that APV can be an easy and useful marker of CVD. However, more studies are needed to reach a consensus regarding the proposed cut-off. It seems that along the vascular aging and atherosclerosis progression, the vascular elasticity lowers. This arterial stiffness has relationship with risk factors like cigarette smoking, hypertension, obesity, diabetes, lipid profile changes, and age [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Arterial affect the structure and functionality of vascular system and therefore, APV as a marker of vascular function and structure can be helpful [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the strengths of our study was the fact that we studied all CVD cases and did not focus on only CAD. The other positive point of our investigation was the study of asymptomatic cases with ASCVD score more than ten. Other studies in the literature mainly focused on the patients that needed angiography. Still, our study was limited to a follow-up time of only one year and higher follow up time may yield more concise results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found APV as a useful prognostic echocardiography marker in risk assessment of CVD development. The proposed cut-off in our study was 49.75 cm/s with a good sensitivity and specificity. However, the studies in the literature reported different cut-offs. With this regard, we advise the researcher of this field to conduct other similar studies with higher follow up time to make a conclusion in this case.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethical committee of the medical faculty of Mashhad University of Medical Sciences. All patients filled a written informed consent for participation in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the authors upon reasonable request and with permission of the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is funded by Mashhad University of Medical Sciences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL. B., MA. Y., M. A., B. Sh. and Mo. M. analyzed and interpreted the patient data regarding the cardiovascular disease and managed patients. Mo.M., M.K. and F.K. helped in management of patient, diagnosis and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMensah GA, Fuster V, Murray CJ, Roth GA, Diseases GBoC, Collaborators R: \u003cstrong\u003eGlobal burden of cardiovascular diseases and risks, 1990-2022\u003c/strong\u003e. \u003cem\u003eJournal of the American College of Cardiology \u003c/em\u003e2023, \u003cstrong\u003e82\u003c/strong\u003e(25):2350-2473.\u003c/li\u003e\n\u003cli\u003eVirani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN: \u003cstrong\u003eHeart disease and stroke statistics\u0026mdash;2020 update: a report from the American Heart Association\u003c/strong\u003e. \u003cem\u003eCirculation \u003c/em\u003e2020, \u003cstrong\u003e141\u003c/strong\u003e(9):e139-e596.\u003c/li\u003e\n\u003cli\u003eSarrafzadegan N, Mohammmadifard N: \u003cstrong\u003eCardiovascular Disease in Iran in the Last 40 Years: Prevalence, Mortality, Morbidity, Challenges and Strategies for Cardiovascular Prevention\u003c/strong\u003e. \u003cem\u003eArch Iran Med \u003c/em\u003e2019, \u003cstrong\u003e22\u003c/strong\u003e(4):204-210.\u003c/li\u003e\n\u003cli\u003eZhu X, Chen L, Yang X, Du Y, Zhao Y, Hu T, Sun N, Sun Q, Liang W, Wei X: \u003cstrong\u003eGlobal, regional, and national trends in tobacco-induced cardiovascular disease burden for 1990\u0026ndash;2021 with projections to 2045: A comprehensive analysis based on the Global Burden of Disease Study 2021\u003c/strong\u003e. \u003cem\u003eTobacco Induced Diseases \u003c/em\u003e2025, \u003cstrong\u003e23\u003c/strong\u003e:10.18332/tid/204008.\u003c/li\u003e\n\u003cli\u003eAlipour V, Zandian H, Yazdi-Feyzabadi V, Avesta L, Moghadam TZ: \u003cstrong\u003eEconomic burden of cardiovascular diseases before and after Iran\u0026rsquo;s health transformation plan: evidence from a referral hospital of Iran\u003c/strong\u003e. \u003cem\u003eCost Effectiveness and Resource Allocation \u003c/em\u003e2021, \u003cstrong\u003e19\u003c/strong\u003e(1):1.\u003c/li\u003e\n\u003cli\u003eOrganization WH: \u003cstrong\u003eAssessing national capacity for the prevention and control of noncommunicable diseases: report of the 2021 global survey\u003c/strong\u003e: World Health Organization; 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Aortic propagation velocity (APV) is an echocardiography index that can be helpful with this regard; however, few studies have addressed the condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This longitudinal study was conducted on cases with ASCVD score of \u0026gt;10. All the cases underwent echocardiography and APV was measured for them. The cases were classified into two groups including normal APV (\u0026gt;56 cm/s) and abnormal APV (≤56 cm/s). They were followed for a duration of one year and the rate of CVD was compared between the two groups. Moreover, ROC curve analysis was used for determining a cut-off.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTotally 60 cases including 37 (61.7%) males and 23 (38.3%) were entered in the study. At the end of one-year follow up, 7 cases developed CVD that 6 were in abnormal APV group and only 1 was in normal APV group (p=0.039). Logistic regression analysis showed that there was a significant relationship between APV and CVD development (OR=9.143; 95%CI= (3.932-1.116); p=0.047). ROC curve analysis proposed the cut off of 49.75 cm.s with a sensitivity of 85.7% and specificity of 60% (AUC=0.710; 95%CI= (0.835-0.585); p=0.072).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAPV can be used as a prognostic marker for CVD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Evaluating the predictive value of aortic propagation index on the incidence of cardiovascular events in asymptomatic patients with ASCVD risk score greater than ten","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:15:44","doi":"10.21203/rs.3.rs-7589300/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-11T05:11:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T14:17:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135613445329666821928951857717219903313","date":"2025-11-02T19:53:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T19:46:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75387243797807121243456422288037666893","date":"2025-09-24T12:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T11:23:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-22T10:09:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-19T16:55:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-19T05:19:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-11T07:55:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b874504c-df6b-4241-b901-8835d088bc8f","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55863885,"name":"Health sciences/Cardiology"},{"id":55863886,"name":"Health sciences/Diseases"},{"id":55863887,"name":"Health sciences/Medical research"},{"id":55863888,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-22T16:24:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 07:15:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7589300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7589300","identity":"rs-7589300","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}