Abstract
The aim of this study was to develop a new scoring system using ambulatory blood pressure monitoring (ABPM) to assist in the evaluation of coronary artery disease (CAD). One hundred twenty-five subjects (53.1 ± 9.6 years of age) were included. Pearson’s tests were first performed to identify the parameters that correlated with Duke Treadmill Score (DTS). Blood test parameters and blood pressure variability (BPV) measures that were extracted from the ABPM were included. Next, a multiple linear regression analysis was performed to train a new scoring system in the 84 patients from the 125 patients. Then, a correlation analysis was conducted to validate the correlation between the new scoring system and DTS in the remaining 41 subjects. A further correlation analysis was used to verify the clinical value of the new scoring system using ultrasonic cardiogram (UCG) and brachial-ankle pulse wave velocity (baPWV). Our new scoring system, which had a 24.096 − 0.083 × residual standard deviation of night systolic blood pressure (SBP) − 0.130 × age − 0.206 × average real variability of night SBP, was correlated with DTS (r = 0.312, P = 0.047). Moreover, our new scoring system was also correlated with various markers of cardiac function (r = −0.290, P = 0.001; r = −0.262, P = 0.004; r = −0.303, P = 0.001; r = −0.306, P = 0.001, respectively) measured by UCG and with baPWV (r = 0.529, P = 0.001). Furthermore, the r-values for the BPV Score versus the markers were closer to −1 than the corresponding r-values for the Duke Score vs the same parameters. And the differences in r-values between Duke Score and BPV Score were statistically significant (P = 0.022). In conclusion, the new scoring system based on ABPM has potential as a non-invasive tool for evaluating CAD.
Similar content being viewed by others
References
Boyle C, Partington S, Ahmed N, Myers J, Froelicher V (2005) Recent advances in exercise testing. Curr Cardiol Rev 1(2):153–164
Sharma K, Kohli P, Gulati M (2012) An update on exercise stress testing. Curr Probl Cardiol 37(5):177–202
Acar Z, Korkmaz L, Agac MT et al (2012) Relationship between Duke Treadmill Score and coronary artery lesion complexity. Clin Invest Med 35(6):365–369
Kwok JM, Miller TD, Christian TF, Hodge DO, Gibbons RJ (1999) Prognostic value of a treadmill exercise score in symptomatic patients with nonspecific ST-T abnormalities on resting ECG. J Am Med Assoc 282(11):1047–1053
Shaw LJ, Peterson ED, Shaw LK et al (1998) Use of a prognostic treadmill score in identifying diagnostic coronary disease subgroups. Circulation 98(16):1622–1630
Abbase AH, Al-Hamdany MHA (2011) Value of Duke’s Treadmill Score and other parameters to predict coronary arterial disease. Med J Babylon 8(2):230–242
Mark DB, Shaw L, Harrell FJ et al (1991) Prognostic value of a treadmill exercise score in outpatients with suspected coronary artery disease. N Engl J Med 325(12):849–853
Mark DB, Hlatky MA, Frank E, Lee KL, Califf RM, Pryor DB (1987) Exercise treadmill score for predicting prognosis in coronary artery disease. Ann Intern Med 106(6):793
Smith R (1981) Indications and contraindications for exercise testing. J Am Med Assoc 246(9):1015–1018
Berent R, Auer J, von Duvillard SP, Sinzinger H, Schmid P (2010) Komplikationen bei der Ergometrie. Herz 35(4):267–272
Pottle K (1988) Cardiovascular screening before exercise. Can Fam Physician Médecin de famille canadien 34:119–121
H cht C (2013) Blood pressure variability: prognostic value and therapeutic implications. ISRN Hypertens 2013:1–16
Lin W, Liu Y, Zhou B et al (2014) The relationship between the blood pressure variability and the severity of coronary artery lesions. J Am College Cardiol. 64(No.16 Suppl):C168
Okada H, Fukui M, Tanaka M et al (2013) Visit-to-visit variability in systolic blood pressure is a novel risk factor for the progression of coronary artery calcification. Hypertens Res 36(11):996–999
Parati G, Ochoa JE, Salvi P, Lombardi C, Bilo G (2013) Prognostic value of blood pressure variability and average blood pressure levels in patients with hypertension and diabetes. Diabetes Care 36(Supplement_2):S312–S324
Yamaguchi Y, Wada M, Sato H et al (2014) Impact of ambulatory blood pressure variability on cerebral small vessel disease progression and cognitive decline in community-based elderly Japanese. Am J Hypertens 27(10):1257–1267
Schwartz GL, Turner ST, Moore JH, Sing CF (2000) Effect of time of day on intraindividual variability in ambulatory blood pressure. Am J Hypertens 13(11):1203–1209
Sueta D, Koibuchi N, Hasegawa Y et al (2014) Telmisartan exerts sustained blood pressure control and reduces blood pressure variability in metabolic syndrome by inhibiting sympathetic activity. Am J Hypertens 27(12):1464–1471
Parati G, Ochoa JE, Lombardi C, Bilo G (2013) Assessment and management of blood-pressure variability. Nat Rev Cardiol 10(3):143–155
Tai C, Sun Y, Dai N et al (2015) Prognostic significance of visit-to-visit systolic blood pressure variability: a meta-analysis of 77,299 patients. J Clin Hypertens 17(2):107–115
Rothwell PM, Howard SC, Dolan E et al (2010) Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet 375(9718):895–905
Levitan EB, Kaciroti N, Oparil S, Julius S, Muntner P (2013) Relationships between metrics of visit-to-visit variability of blood pressure. J Hum Hypertens 27(10):589–593
O’Brien E (2011) Twenty-four-hour ambulatory blood pressure measurement in clinical practice and research: a critical review of a technique in need of implementation. J Intern Med 269(5):478–495
Imai Y, Abe K, Sasaki S et al (1990) Determination of clinical accuracy and nocturnal blood pressure pattern by new portable device for monitoring indirect ambulatory blood pressure. Am J Hypertens 3(4):293–301
Xiong H, Wu D, Tian X et al (2014) The relationship between the 24 h blood pressure variability and carotid intima-media thickness: a compared study. Comput Math Methods Med 2014:1–9
Garc a-Garc a N, Garc a-Ortiz L, Recio-Rodr guez JI et al (2013) Relationship of 24-h blood pressure variability with vascular structure and function in hypertensive patients. Blood Press Monit 18(2):101–106
Giantin V, Perissinotto E, Franchin A et al (2013) Ambulatory blood pressure monitoring in elderly patients with chronic atrial fibrillation: is it absolutely contraindicated or a useful tool in clinical practice and research? Hypertens Res 36(10):889–894
Fearon WF, Gauri AJ, Myers J, Raxwal VK, Atwood JE, Froelicher VF (2002) A comparison of treadmill scores to diagnose coronary artery disease. Clin Cardiol 25(3):117–122
Alvarez TJ, Martin-Ambrosio ES, Tarin ER, Fernandez MM, De la Tassa CM (2008) Significance of the treadmill scores and high-risk criteria for exercise testing in non-high-risk patients with unstable angina and an intermediate Duke Treadmill Score. Acta Cardiol 63(5):557–564
Johnson GG, Decker WW, Lobl JK et al (2008) Risk stratification of patients in an emergency department chest pain unit: prognostic value of exercise treadmill testing using the Duke Score. Int J Emerg Med 1(2):91–95
Mena LJ, Maestre GE, Hansen TW et al (2014) How many measurements are needed to estimate blood pressure variability without loss of prognostic information? Am J Hypertens 27(1):46–55
Mena L, Pintos S, Queipo NV, Aizpurua JA, Maestre G, Sulbaran T (2005) A reliable index for the prognostic significance of blood pressure variability. J Hypertens 23(3):505–511
Kuss H (2003) Calibration is based on least squares regression analysis. It can be used under the assumption of homogeneous variances. Using chromatographic methods variances are often heteroskedastic. Weighting of variances is a valuable and simple tool to get realistic prediction intervals (y deviations) and uncertainties of the result (x deviations). LC· GC Eur 2
Rice WR, Gaines SD (1989) One-way analysis of variance with unequal variances. Proc Natl Acad Sci USA 86(21):8183–8184
He YM, Yang XJ, Hui J et al (2006) Low serum albumin levels in patients with paroxysmal atrial fibrillation: what does it mean? Acta Cardiol 61(3):333–337
Hilborn ED, Catanzaro DG, Jackson LE (2012) Repeated holdout cross-validation of model to estimate risk of Lyme disease by landscape characteristics. Int J Environ Health Res 22(1):1–11
Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310
Ludbrook J (2010) Confidence in Altman–Bland plots: a critical review of the method of differences. Clin Exp Pharmacol Physiol 37(2):143–149
Lin W, Zhang H, Zhang Y (2013) Investigation on cardiovascular risk prediction using physiological parameters. Comput Math Methods Med 2013:1–21
Dolan E, Stanton A, Thijs L et al (2005) Superiority of ambulatory over clinic blood pressure measurement in predicting mortality: the Dublin outcome study. Hypertension 46(1):156–161
Anantharam B, Janardhanan R, Hayat S, Senior R (2013) Ischaemic burden determined by myocardial contrast echocardiography predicts mortality in patients with new-onset shortness of breath, suspected heart failure and no previous coronary artery disease. Int J Cardiol 168(2):1670–1671
Liu S, Wassef AW, Moussa M, Jassal DS, Hussain F (2013) The utility of diastolic dysfunction on echocardiography for predicting coronary artery disease burden as defined by the syntax score. Can J Cardiol 29(No. 10 Suppl):S304–S305
Chatzizisis YS, Murthy VL, Solomon SD (2013) Echocardiographic evaluation of coronary artery disease. Coron Artery Dis 24(7):613–623
Yamazaki T, Myers J, Froelicher VF (2004) Effect of age and end point on the prognostic value of the exercise test. Chest 125(5):1920–1928
Sadrzadeh Rafie AH, Dewey FE, Sungar GW et al (2008) Age and double product (systolic blood pressure × heart rate) reserve-adjusted modification of the Duke Treadmill Score nomogram in men. Am J Cardiol 102(10):1407–1412
Sadrzadeh Rafie AH, Dewey FE, Myers J, Froelicher VF (2008) Age-adjusted modification of the Duke Treadmill Score nomogram. Am Heart J 155(6):1033–1038
O’Brien E (2008) Ambulatory blood pressure measurement: the case for implementation in primary care. Hypertension 51(6):1435–1441
Acknowledgements
This work was supported in part by the Science and the Technology Planning Project of Guangdong Province (2013A022100036 and 2014A020212257).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
We declare that there are no potential conflicts of interest with respect to this paper.
Ethical approval
This study was approved by the Institutional Ethics Committee of the General Hospital of Guangzhou Military Command of PLA.
Additional information
Wei Zhu and Jian Qiu have contributed equally to this work.
Rights and permissions
About this article
Cite this article
Zhu, W., Qiu, J., Ma, L. et al. A new scoring system for evaluating coronary artery disease by using blood pressure variability. Australas Phys Eng Sci Med 40, 751–758 (2017). https://doi.org/10.1007/s13246-017-0563-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-017-0563-1