International Journal of Clinical Medicine, 2015, 6, 838-844 Published Online November 2015 in SciRes. http://www.scirp.org/journal/ijcm http://dx.doi.org/10.4236/ijcm.2015.611110 How to cite this paper: Cheruku, N.K., et al. (2015) Analysis of Ankle-Brachial Index, Waist-Hip Ratio, Ejection-Fraction, Ob- esity, Smoking, Alcohol Habits, Diabetes and Hypertension as Independent Predictors of Complexity and Severity of Coro- nary Artery Disease. International Journal of Clinical Medicine, 6, 838-844. http://dx.doi.org/10.4236/ijcm.2015.611110 Analysis of Ankle-Brachial Index, Waist-Hip Ratio, Ejection-Fraction, Obesity, Smoking, Alcohol Habits, Diabetes and Hypertension as Independent Predictors of Complexity and Severity of Coronary Artery Disease Naveen Kumar Cheruku, Adikesava Naidu Otikunta, Y. V. Subba Reddy, Ravi Srinivas Department of Cardiology, Osmania General Hospital and Osmania Medical College, Hyderabad, India Received 26 October 2015; accepted 16 November 2015; published 19 November 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Background: The present study was conducted to examine the association between various coro- nary risk factors and clinical parameters, with special emphasis on ankle-brachial index, in pre- dicting the severity and complexity of coronary artery disease. Methods: Patients diagnosed with coronary artery disease at our hospital between September-2012 and December-2014 were ex- amined in this study. Selected patients were screened for cardiovascular risk factors including diabetes, hypertension, smoking, and alcohol habits as well as for clinical parameters including body-mass index, waist-hip ratio, ankle-brachial index, and ejection fraction. All patients under- went coronary angiography and were evaluated for severity of coronary artery disease (based on number of vessels involved) and complexity of coronary angiographic lesions (measured by com- puter-assisted Syntax score calculator). The collected data were analyzed to determine the role of cardiovascular risk factors and clinical parameters as predictors of complexity and severity of co- ronary artery disease. Results: A total of 211 patients (mean age: 54.64 ± 9.9 years; 81% males) with coronary artery disease were analyzed. Findings revealed that diabetes mellitus (p < 0.001), hypertension (p < 0.001), smoking habits (p = 0.036), and low ankle-brachial index (p < 0.001) were independent predictors of complex coronary artery disease as measured by Syntax score. Significant associations were also evident between severity of coronary artery disease and di- abetes mellitus (p < 0.001), hypertension (p < 0.001), and ankle-brachial index (p < 0.001). Con- versely, other cardiovascular risk factors including body-mass index, alcohol habits, wait-hip ratio, and ejection fraction did not exhibit significant associations with severity and complexity of coro- nary artery. Conclusions: The early diagnosis of coronary artery can be predicated by evaluating diabetes, hypertension, and smoking habits in patients presenting with acute coronary syndrome. In addition, ankle-brachial index can be used as an effective non-invasive bed-side tool, as an al-
N. K. Cheruku et al. ternative to Syntax score, in predicting the severity and complexity of coronary artery disease. Keywords Ankle-Brachial Index, Cardiovascular Risk Factors, Coronary Artery Disease, Peripheral Arterial Disease, Predictor, Syntax Score, Waist-Hip Ratio 1. Introduction Coronary artery disease is a leading cause of morbidity and mortality worldwide. In recent years, there has been an increasing trend in the prevalence of associated cardiovascular risk factors such as diabetes mellitus, hyper- tension, smoking, alcohol, dyslipidemia, and obesity among patients with coronary artery disease [1]. Several studies have also identified a significant association between these cardiovascular risk factors and severity and complexity of coronary artery disease [2]-[5]. In recent years, a high prevalence of peripheral arterial disease has been noted among patients with coronary artery disease [6]. Ankle -brachial index (measured <0.9) is an effec- tive non-invasive tool to detect peripheral arterial disease. Since coronary artery disease and peripheral arterial disease are manifestation of atherosclerotic process, it is considered that ankle-brachial index may also reflect coronary atherosclerotic burden [7]. Few studies have also shown a link between ankle-bra chial i ndex a nd Syn- tax score, a lesion based angiographic scoring system introduced as a tool to grade the c omplexity of coronary lesions [7] [8]. However, there are no suc h studi es from I ndia ti ll date. With this background, the pr esent st udy was conducted to study the relation between various coronary risk factors and clinical parameters, with special emphasis o n ankle-brachial index, in predic ting the severity a nd complexity of coronary artery disease. 2. Methods 2.1. Study Population Patients with coronary artery disease presenting to the Osmania General Hospital, Hyderabad b etween Septem- ber-2012 and December-2014 were analyzed in this study. Inclusion criteria included 1) age ≥18 years, 2) pres- entation of ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), or unstable angina 3) diagnosis of coronary artery disease by coronary angiography, and 4) willing ness to sign in- formed consent. Patients with chronic stable angina, dilated cardiomyopathy, ischemic cardiomyopathy and chronic kidney disease were excluded from the study. 2.2. Data Collection Selected patients were screened for diabetes and hypertension. Data on smoking and alcohol habits for each pa- tient were also recorded. Body-mass ind e x ( B MI ) was c alculat ed by di vid i ng bo d y wei g ht (kilo gr a ms) by he ig ht (meters) squared. Based on BMI data, patients were divided in normal (18.5 - 25 kg/m2), overweight (25 - 30 kg/m2), and obese (>30 kg/m2) categories. Similarly, waist-hip ratio was evaluated as the ratio of circumference of the waist to that of the hips. Subseq uently, patie nts were d ivided in to t wo groups based on the cut-off va lue fo r wai s t -hip ratio set at 1.0 for men and 0.85 for women. All patients were also evaluated for ankle-brachia l in- dex by measuring the systolic blood pressure from both brachial arteries and from both the dorsalis pedis and posterior tibial arteries using the Doppler device after the patient has been at rest in the supine position for 10 minute s. The cut -off level for ankle brachial index was <0.9. Left-ventricular ejection fraction was measured b y echocardiography, and the cut-off level was <55%. Moreover, the severity of coronary artery disease and num- ber of vessels with significant stenosis or occlusion were noted based on angiographic assessment. Syntax score of coronary angiographic lesions was calculated using computer-assisted Syntax score calculator. Based on the Syntax score data, patients were distributed in to three groups, namely mild (<22), intermediate (22 - 32) and high (>32) Syntax score. 2.3. Statistical Analysis Continuous data are presented as means and standard deviations, while categorical data are presented as fre-
N. K. Cheruku et al. quencies and percentages. Chi-square test was used to analyze the association of cardiovascular risk factors and clinical parameters in predicting the complexity and severity of coronary artery disease. A two-sided alpha level of 0.05 was used to identify statistically significant association. All data were analyzed using the Statistical Package for Social Sciences (SPSS; Chicago, IL, USA) program, version 15. 3. Results 3.1. Baseline Demographics A total of 211 patients with coronary artery disease were analyzed in this st udy. Mea n age of the study popula- tion was 54.64 ± 9.99 years (range: 26 - 80 years). Majority of patients belonged to 51 - 60 years age group . Of recruited patients, 171 (81%) were male and 40 (19 %) were female. Diabetes mellitus was present in 75 (35.5%) patients, while hypertension was present in 131 (62.1%) patients. Smoking and binge alcohol habits were re- ported in 51.2% and 37.4% of patients respectively. In addition, about one-third o f patients with coro nary arter y disease were identified to have clinical or subclinical peripheral arterial disease, as measured by abnormal an- kle-brachial index. Other baseline characteristics are described in Table 1. 3.2. Predictors of Complexity of Coronary Artery Disease Findings of the analysis of association between cardiovascular risk factors and complexity of coronary artery disease, as measured by Syntax scores, are described in Table 2. We found that diabetes mellitus (p < 0.001), hypertension (p < 0.001), smoking habits (p = 0.036), and low ankle-brachial index (p < 0.001) were indepen- dent predictors of complex coronary artery disease. On the other hand, the associations between Syntax scores and body-mass index, alcohol habits, wait -hip ratio, and left-ventr ic ula r ej ection fraction were found to be statis- tically insignificant. 3.3. Predictors of Severity of Coronary Artery Disease Findings of the analysis of association between cardiovascular risk factors and severity of coronary artery dis- ease, as measured by number of vessels involved, are described in Table 3. Significant associations were evi- dent between severity of coronary artery disease and diabetes mellitus (p < 0.001), hypertension (p < 0. 001 ) and ankle -br achia l inde x (p < 0.001). Other cardiovascular ris k factors o r clinical para meters did not exhibit signi fi- cant associations. 4. Discussion Ankle -brachial index is the ratio of the systolic blood pressure measured at the ankle to that measured at the Table 1. Baseline and clini cal characteristics of enrolled patient s . Var iab le 211 patients Age (y ears) 54.6 4 ± 9.99 Ma les 171 (81.0% ) Diabetes mellitus 75 (35.5%) Hypertension 131 (62. 1%) Smoking 108 (51.2% ) Alcohol 79 (37.4%) Body-mass index (kg/m2) 24.2 2 ± 3.60 Waist-hip ratio 1.02 ± 0.09 Ankle-brachial index 0.98 ± 0. 10 Ejection fraction (%) 53.65 ± 9.62 Data ar e expresses as mean ± S D for continuous variables and as fr equency (percentage) for ca tegorical variables.
N. K. Cheruku et al. Table 2. Analysis of association between cardi ovascular ri s k factors and Syntax sco re. Var iab le Category Syntax score* p value† Mild (n = 126; 59.7%) Moderate (n = 37; 17 . 5%) Severe (n = 48; 22 . 7%) Cardiovascular risk factors Diabetes mellit us Ye s (n = 75 ; 35.5% ) 28 (13.3%) 16 (7.6%) 31 (14.7%) <0.001 No (n = 136; 64.5%) 98 (46.4%) 21 (10.0%) 17 (8.1% ) Hypertension Yes (n = 131; 62.1%) 62 (29.9%) 29 (13.7%) 40 (19.0%) <0.001 No (n = 80; 37.9%) 63 (29.9%) 9 (4.3%) 8 (3.8%) Smoking Yes (n = 108; 51.2%) 73 (34.6%) 13 (6.2%) 22 (10.4%) 0.036 No (n = 103; 48.8%) 53 (25.1%) 24 (11.4%) 26 (12.3%) Alcohol Yes (n = 79 ; 3 7.4%) 53 (25.1%) 11 (5.2%) 1 5 (7.1%) 0.237 No (n = 132; 62.6%) 73 (34.6%) 26 (12.3%) 33 (15.6%) Clinical parameters Body-mass ind ex Normal (n = 127; 60.2%) 82 (38.9%) 21 (10.0%) 24 (11.4%) 0.378 Overweig ht (n = 72; 24.1%) 39 (18.5%) 13 (6.2%) 20 ( 9.5%) Obe s e (n = 1 2; 5.7% ) 5 (2.4%) 3 (1.4%) 4 (1.9%) Waist-hip ratio >1 for m en; >0.85 for women (n = 58; 27.5%) 40 (19.0%) 7 (3.3%) 11 (5.2%) 0.222 <1 for m en; <0.85 for women (n = 153; 72.5%) 86 (40.8%) 30 (14.2%) 37 (17.5%) Ankle-brachial ind ex <0.9 ( n = 70 ; 33.2 %) 18 (8.5%) 17 (8.1%) 35 (16.6%) <0.001 ≥0.9 (n = 141; 66.8%) 108 (51.2%) 20 (9.5%) 13 (6.2%) Ejection fraction <55% (n = 111; 52.6%) 63 (29.9%) 22 (10.4%) 26 (12.3%) 0.581 ≥55% (n = 100; 47.4%) 63 (29.9%) 15 (7.1%) 22 (10.4%) *Data are expresses as frequency (percentage); †Chi-square test; ABI: Ankle-brachial index; BMI: Body-mass index. brachial artery. Initially, this index was proposed as an easy, reliable, and noninvasive tool to determine the presence and severity of lower-extremity peripheral arterial disease [9]. In current practice, the ankle-brachial index can also be used as an indicator of coronary atherosclerosis even in the absence of symptoms of peripheral arterial disease [10]. In addition, the inverse relationship between ankle-brachial index and cardiovascular risk factors as well as its association with cardiovascular events (myocardial infarction, stroke and death) has been well established [11] [12]. Howe ver , Ind ia n st udi e s d et er mini ng t he r o le of a nkle-brachial index as a predictor of severity of coronary artery disease are limited. In this regard, we examined the association between ankle- brachial index and coronary artery disease severity. We found that ankle-brachial index could be a useful me- thod in assessing both the atherosclerotic risk factors and the degree of coronary involvement in suspected pa- tients. In the present study, most of the patients belonged to age group of 51 - 60 years. It is known that ageing leads to decreased ankle-brachial index as a result of arterial stiffening [13]. Further, a significant male predominance is commonly observed in majority of studies on coronary artery disease and peripheral arterial disease. In similar lines, about 80% of patients with coronary artery disease were male in the present study. Such gender-related differences in peripheral artery disease may be attributable to smaller baseline calf muscle area in women [14]. Metabolic syndrome is also a significant risk factor for coronary artery disease as well as peripheral arterial disease. In a study of 3041 adults aged ≥40 years, the age-adjusted prevalence of peripheral arterial disease among participants with and without high glucose was 5.9% and 3.6% respectively (p = 0.075), and with and without abdominal obesity was 4.2% and 3.7% respectively (p = 0.337) [15]. In additio n, another trial suggested that diabetes is associated with increased atherogenicity and complexity of the disease, measured by Syntax
N. K. Cheruku et al. Table 3. Analysis of association between cardi ovascular ri s k factors and severity of coronar y artery disease. Var iab le Category Coronary vessels involved* p value† Recanalized (n = 12; 5.7% ) SVD (n = 79; 37.4%) DVD (n = 59; 28.0%) TVD (n = 58; 27.5%) TVD + LMCA (n = 3; 1. 4%) Cardiovascular risk factors Diabetes mellit us Yes (n = 75 ; 35. 5%) 2 (0.9%) 15 (7 .1% ) 27 (12.8%) 30 (14.2%) 1 (0.5%) <0.001 No (n = 136; 64.5%) 10 (4 .7% ) 64 (30.3%) 32 (15.2%) 28 (13.3%) 2 (0.9%) Hypertension Yes (n = 131; 62.1%) 7 (3.3%) 35 (16.6%) 43 (20.4%) 43 (20.4%) 3 (1.4%) <0.001 No (n = 80; 37.9%) 5 (2.4%) 44 (20.9%) 16 (7.6%) 15 ( 7.1%) 0 (0.0%) Smoking Yes (n = 108; 51.2%) 7 (3.3%) 48 (22.7%) 25 (11.8%) 27 (12.8%) 1 (0.5%) 0.210 No (n = 103; 48.8%) 5 (2.4%) 31 (14.7%) 34 (16.1%) 31 (14.7%) 2 (0.9% ) Alcohol Yes ( n = 79; 3 7.4%) 3 (1.4%) 36 (17.1%) 21 (10.0%) 19 (9 .0% ) 0 ( 0.0%) 0.244 No (n = 132; 62.6%) 9 (4.3%) 43 (20.4%) 38 (18.0%) 39 (18.5%) 3 (1.4% ) Clinical parameters Body-mass ind ex Normal (n = 127; 60.2%) 7 (3.3%) 50 (23.7%) 35 (16.6%) 32 (15.2%) 3 (1.4%) 0.725 Overweigh t (n = 72; 24.1%) 5 (2.4%) 24 (11.4%) 22 (10.4%) 21 (10.0%) 0 (0.0%) Obes e (n = 12; 5.7%) 0 (0.0%) 5 (2.4%) 2 (0. 9%) 5 (2.4%) 0 (0.0%) Ankle-brachial ind ex <0.9 (n = 70; 33.2%) 1 (0.5% ) 8 ( 3.8%) 24 (11.4%) 36 (17.1%) 1 (0.5%) <0.001 ≥0.9 (n = 141; 66.8%) 11 (5.2%) 71 (33.6%) 35 (16.6%) 22 (10.4%) 2 (0.9%) Waist-hip ratio >1 for m en; >0.85 for women (n = 58; 27.5% ) 4 (1.9%) 27 (12.8%) 13 (6.2% ) 13 (6.2%) 1 (0.5%) 0.454 <1 for m en; <0.85 for women (n = 153; 72.5%) 8 (3.8%) 52 (24.6%) 46 (21.8%) 45 (21.3%) 2 (0.9%) Ejection fraction <55% (n = 111; 52.6%) 7 (3.3%) 43 (20.4%) 28 (13.3%) 31 (14.7%) 2 (0.9%) 0.887 ≥55% (n = 100; 47.4%) 5 (2.4% ) 36 (17.1%) 31 (14.7%) 27 (12.8%) 1 (0.5%) *Data are expresses as frequency (percentage); †Chi-square test ; SVD: Sin gle vessel dis eas e; DVD: Double vess e l disease; TVD: Tr iple vess e l disease ; LMCA: left main coronary artery. score [3]. However, the correlation of Syntax score with disease severity or number of vessels involved has been reported rarely. In the present study, we found a significant association between diabetes status and severity and complexity of coronary artery disease. We also observed that increase in BMI was associated increase in severi- ty of coronar y artery disease, but the associatio n was statisti cally insig nificant. This may be due to the influence of other cardiovascular risk factors. Waist-hip ratio is another major component of the metabolic syndrome, and indicates atherosclerotic burden of the patients. About 27.5% of patients displayed abnormal waist-hip ratio in present study. However, no association was observed between waist-hip ratio and severity/complexity of coro- nary artery disease. Similarly, left-ventricular dysfunction showed no association with severity and complexity of coronary artery disease. It is known that hypertension is one of the most established risk factor of coronary artery disease. In the present study, we identified that the complexity of coronary artery lesions was higher among hypertensive patients. It was also observed that incidence of multi-vessel coronary artery disease were higher among hypertensive patients than among non -hypertensive patients. In the present study, a strong association was also observed between severity of coronary artery disease and presence of coexisting peripheral arterial disease, as measured by ankle-brachial index. Further, an abnormal ankle -brachial index was associated with increased risk of multi-vessel coronary artery disease. A recent study by Ikeda et al has also shown an association between ankle-brachial index and the complexity of the coronary lesion with Syntax score [16] . Papamichael et al. have reported similar findings regarding the use of ankle- brachial index as the main variables for predicting the extent and severity of coronar y disease, especially in male
N. K. Cheruku et al. patients and those with diabetes [17]. These findings highlight the potential role of ankle-brachial index as a non-invasive tool to predict coronary artery disease severity and complexity of coronary artery disease in pa- tients with suspected coronary artery disease. The potential mechanisms that might underlie this association in- clude the progressive occlusion of arteries and vascular disease that leads to occlusion of the cerebral arteries, cerebral tissue loss a nd hence vascular dementia. There is also an evidence of a strong p ositive a ssociatio n be- tween peripheral arterial disease and inflammation, which has been inferred in the pathogenesis of atherosclero- sis. However, making more accurate decisions for using this method in t he prevention, diagnosis, prediction of severity and complexity, and pro gnostication of coronary artery disease needs further studies with large sample sizes. Study Limitations The pr esent study has several li mitations. I t is a singl e -cente r stud y; hence, scop e o f the finding might b e li mited. Further, the findings of the study were substantiated from comparatively smaller sample-size data. Duration of risk factors was not ta ken into consid eratio n for ana lyzing t he data , which c an also be considered as another li- mitation of the present study. Data on coronary risk factors treatment condition are also lacking. Therefore, we recommend fellow researchers to conduct a multi-center study involving large-sample size for f urther investi ga- tion and confirmation of present findings. 5. Conclusion We examined the role of ankle-brachial index, waist-hip ratio, ejection-fraction, obesity, smoki ng/alco hol habits, diabetes and hypertension as independent predictors of complexity and severity of coronary artery disease. About one-third of study participants with coronary artery disease were reported to have clinical or subclinical peripheral arterial disease. We found that early diagnosis of coronary artery can be predicated by evaluating di- abetics, hypertension, and smoking habits in patients presenting with acute coronary syndrome. In addition, an- kle-brachial index can be used as an effective non-invasive bed-side tool, as an alternative to Syntax score, in predicting the severity and complexity of coronary artery disease. References [1] Sekhri, T., Kanwar, R.S., Wilfred, R., Chugh, P., Chhillar, M., Aggarwal, R., Sharma, Y.K., Sethi, J., Sundriyal, J., Bhadra, K., Singh, S., Rautela, N., Chand, T., Singh, M. and Singh, S.K. (2014) Prevalence of Risk Factors for Coro- nary Arter y Disease in an Urban Indian Pop ulat ion . BMJ Open, 4, e005346. http://dx.doi.org/10.1136/bmjopen-2014-0053 46 [2] Bhatt, H., Turkistani, A., Sanghani, D., Julliard, K. and Fernaine, G. (2015) Do Cardiovascular Risk Factors and Co- ronar y SYNT AX Score Predict Cont rast Volume Use During Cardi ac C atheterizat ion? Angiology, 66, 933-940. http://dx.doi.org/10.1177/0003319715573909 [3] Kim, J.J., Hwang, B.H., Choi, I.J., Choo, E.H., Lim, S., Kim, J.K., Koh, Y.S., Kim, D.B., Jang, S.W., Cho, E.J., Lee, J.M., Kim, P.J., Cho, J.H., Jung, J.I., Seung, K.B., Min, J.K. and Chang, K. (2015) Impact of Diabetes Duration on the Extent and Severity of Coronary Atheroma Burden and Long-Term Clinical Outcome in Asymptomatic Type 2 Di- abetic Patients: Evaluation by Coronary CT Angiography. European Heart Journal—Cardiovascular Imaging, 16, 1065-1073. http://dx.doi.org/10.1093/ehjci/jev106 [4] Korkmaz, L., Adar, A., Korkmaz, A.A., Erkan, H., Agac, M.T., Acar, Z., Kurt, I.H., Akyuz, A.R. and Celik, S. (2012) Atherosclerosis Burden and Coronary Artery Lesion Complexity in Acute Coronary Syndrome Patients. Cardiology Journal, 19, 295-300. http://dx.doi.org/10.5603/CJ.2012.0052 [5] Yano, M., Miura, S.I., Shiga, Y., Miyase, Y., Suematsu, Y., Norimatsu, K., Nakamura, A., Adachi, S., Nishikawa, H. and Saku, K. (2015) Association between Smoking Habits and Severity of Coronary Stenosis as Assessed by Coronary Computed Tomography Angiography. Heart Vessels, 1-8. http://dx.doi.org/10.1007/s00380-015-07 16-7 [6] Cassar, A., Poldermans, D., Rihal, C.S. and Gersh, B.J. (2010) The Management of Combined Coronary Artery Dis- ease and Peripheral Vascular Disease. European Heart Journal , 31, 1565-1572. http://dx.doi.org/10.1093/eurheartj/ehq186 [7] Falcao, F.J., Rodrigues Alves, C.M., Caixeta, A., de Freitas Guimaraes, L., de Sousa Filho, J.T., Soares, J.A., Helber, I. and Car valho, A.C. (2013) Relation between the Ankle -Brach ial Index and the Complexity of Coronar y Artery Diseas e in Older Patients. Clinical Interventions in Aging, 8, 16 11-1616. http://dx.doi.org/10.2147/CIA.S52778 [8] Seb astianski, M., Narasimhan, S., Graham, M.M. , Toleva, O., Sh avadia, J., Abu alnaja, S., Tsuyuki, R.T. and McMur-
N. K. Cheruku et al. try, M.S. (2014) Usefulness of the Ankle-Brachial Index to Predict High Coronary SYNTAX Scores, Myocardium at Risk, and Incomplete Coro nary Revascularization . American Journal of Cardiology, 114, 1745-1749. http://dx.doi.org/10.1016/j.amjcard.2014.09.010 [9] Ko, S.H. and Bandyk, D.F. (2013) Interpretation and Significance of An kle-Br ach ial S ystoli c P ressur e Ind ex. Seminars in Vasc ular Surge r y , 26, 86-94 . http://dx.doi.org/10.1053/j.semvascsurg.2014.01.002 [10] Khan, T.H., Farooqui, F.A. and Niazi, K. (2008 ) Critical Review of the Ankle Brachial Index. Current Cardiology Re- views, 4, 101-106. http://dx.doi.org/10.2174/157340308784245810 [11] Lamina, C., Meisinger, C., Heid, I.M., Lowel, H., Rantner, B., Koenig, W. and Kronenberg, F. (2006) Association of Ank l e -Br ach ial In dex and Plaques in the Carotid and Femoral Arte ries with C ardi ovascu lar Event s and Total Mortality in a Population-Based Study with 13 Years of Follow-Up. European Heart Journa l, 27, 2580-2587 . http://dx.doi.org/10.1093/eurheartj/ehl228 [12] Resnick, H.E., Lindsay, R.S., McDermott, M.M., Devereux, R.B., Jones, K.L., Fabsitz, R.R. and Howard, B.V. (2004) Relationship of High and Low Ankle Brachial Index to All-Cause and Cardiovascular Disease Mortality: The Strong Heart Study. Circulation, 109, 733-739. http://dx.doi.org/10.1161/01.CIR.0000112642.63927.54 [13] Papa, E.D., Helber, I., Ehrlichmann, M.R., Alves, C.M., Makdisse, M., Matos, L.N., Borges, J.L., Lopes, R.D., Stefa- nini, E. and Carvalho, A.C. (2013) Ankle-Brachial Ind ex as a Predictor of Coro n ary Disease E vents in Elderly Patients Submitted to Coronary Angiography. Clinics (Sao Paulo), 68, 1481-1487 . http://dx.doi.org/10.6061/clinics/2013(12)02 [14] McDermott, M.M., Ferrucci, L., Liu, K., Guralnik, J.M., Tian, L., Kibbe, M., Liao, Y., Tao, H. and Criqui, M.H. (2011) Women with Perip heral Arterial Disease Exp erience Fast er Functio nal Decline than Men with Peripheral Arterial Dis- ease. Journal of the American College of Cardiology, 57, 707-714. http://dx.doi.org/10.1016/j.jacc.2010.09.042 [15] (2005) Abstracts of the 45th Annual Conference on Cardiovascular Disease Epidemiology and Prevention in Associa- tion with the Council on Nutrition, Physicial Activity and Metabolism. Washington DC, 29 April-2 May 2006, e184- e296. [16] Ikeda, N., Kogame, N., Iijima, R., Nakamura, M. and Sugi, K. (2013) Impact of Carotid Artery Ultrasound and An kle- Brachial Index on Prediction of Severity of SYNTAX Score. Circulation Journa l, 77, 712-716. http://dx.doi.org/10.1253/circj.CJ-12-1087 [17] Papamichael, C.M., Lekakis, J.P., Stamatelopoulos, K.S., Papaioannou, T.G., Alevizaki, M.K., Cimponeriu, A.T., Ka- nakakis, J.E., Papapanagiotou, A., Kalofoutis, A.T. and Stamatelopoulos, S.F. (2000) Ankle-Brachial Index as a Pre- dictor of the Extent of Co ronary Atherosclerosis and Car diovascular Even ts in Patients with Coronary Art ery Disease. American Journal of Cardiology, 86, 615-618. http://dx.doi.org/10.1016/S0002-9149(00)01038-9
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