Rationale and study design of the prospective, longitudinal, observational cohort study "rISk strAtification in end-stage renal disease" (ISAR) study

Christoph Schmaderer, Susanne Tholen, Anna-Lena Hasenau, Christine Hauser, Yana Suttmann, Siegfried Wassertheurer, Christopher C Mayer, Axel Bauer, Kostantinos D Rizas, Stephan Kemmner, Konstantin Kotliar, Bernhard Haller, Johannes Mann, Lutz Renders, Uwe Heemann, Marcus Baumann, Christoph Schmaderer, Susanne Tholen, Anna-Lena Hasenau, Christine Hauser, Yana Suttmann, Siegfried Wassertheurer, Christopher C Mayer, Axel Bauer, Kostantinos D Rizas, Stephan Kemmner, Konstantin Kotliar, Bernhard Haller, Johannes Mann, Lutz Renders, Uwe Heemann, Marcus Baumann

Abstract

Background: The ISAR study is a prospective, longitudinal, observational cohort study to improve the cardiovascular risk stratification in endstage renal disease (ESRD). The major goal is to characterize the cardiovascular phenotype of the study subjects, namely alterations in micro- and macrocirculation and to determine autonomic function.

Methods/design: We intend to recruit 500 prevalent dialysis patients in 17 centers in Munich and the surrounding area. Baseline examinations include: (1) biochemistry, (2) 24-h Holter Electrocardiography (ECG) recordings, (3) 24-h ambulatory blood pressure measurement (ABPM), (4) 24 h pulse wave analysis (PWA) and pulse wave velocity (PWV), (5) retinal vessel analysis (RVA) and (6) neurocognitive testing. After 24 months biochemistry and determination of single PWA, single PWV and neurocognitive testing are repeated. Patients will be followed up to 6 years for (1) hospitalizations, (2) cardiovascular and (3) non-cardiovascular events and (4) cardiovascular and (5) all-cause mortality.

Discussion/conclusion: We aim to create a complex dataset to answer questions about the insufficiently understood pathophysiology leading to excessively high cardiovascular and non-cardiovascular mortality in dialysis patients. Finally we hope to improve cardiovascular risk stratification in comparison to the use of classical and non-classical (dialysis-associated) risk factors and other models of risk stratification in ESRD patients by building a multivariable Cox-Regression model using a combination of the parameters measured in the study.

Clinical trials identifier: ClinicalTrials.gov NCT01152892 (June 28, 2010).

Keywords: Ambulatory blood pressure monitoring (ABPM); Dialysis; End stage renal disease; Hemodialysis; ISAR; Montreal Cognitive Assessment (MoCA); Pulse wave analysis (PWA); Pulse wave velocity (PWV); Risk stratification; retinal vessel analysis (RVA).

References

    1. Collins AJ, et al. United States renal data system public health surveillance of chronic kidney disease and end-stage renal disease. Kidney Int Suppl (2011) 2015;5(1):2–7. doi: 10.1038/kisup.2015.2.
    1. Liyanage T, et al. Worldwide access to treatment for end-stage kidney disease: a systematic review. Lancet. 2015;385(9981):1975–82. doi: 10.1016/S0140-6736(14)61601-9.
    1. Collins AJ, et al. Excerpts from the US renal data system 2009 annual data report. Am J Kidney Dis. 2010;55(1 Suppl 1):S1–420.
    1. Eknoyan G, et al. Effect of dialysis dose and membrane flux in maintenance hemodialysis. N Engl J Med. 2002;347(25):2010–9. doi: 10.1056/NEJMoa021583.
    1. Ghanta M, Kozicky M, Jim B. Pathophysiologic and treatment strategies for cardiovascular disease in end-stage renal disease and kidney transplantations. Cardiol Rev. 2015;23(3):109–18.
    1. Tholen S, et al. Variability of cognitive performance during hemodialysis: standardization of cognitive assessment. Dement Geriatr Cogn Disord. 2014;38(1–2):31–8. doi: 10.1159/000357803.
    1. Wassertheurer S, et al. A new oscillometric method for pulse wave analysis: comparison with a common tonometric method. J Hum Hypertens. 2010;24(8):498–504. doi: 10.1038/jhh.2010.27.
    1. Weber T, et al. Validation of a brachial cuff-based method for estimating central systolic blood pressure. Hypertension. 2011;58(5):825–32. doi: 10.1161/HYPERTENSIONAHA.111.176313.
    1. Wei W, et al. Validation of the mobil-O-Graph: 24 h-blood pressure measurement device. Blood Press Monit. 2010;15(4):225–8. doi: 10.1097/MBP.0b013e328338892f.
    1. Luzardo L, et al. 24-h ambulatory recording of aortic pulse wave velocity and central systolic augmentation: a feasibility study. Hypertens Res. 2012;35(10):980–7. doi: 10.1038/hr.2012.78.
    1. Hametner B, et al. Oscillometric estimation of aortic pulse wave velocity: comparison with intra-aortic catheter measurements. Blood Press Monit. 2013;18(3):173–6. doi: 10.1097/MBP.0b013e3283614168.
    1. Nunan D, et al. Assessment of central haemomodynamics from a brachial cuff in a community setting. BMC Cardiovasc Disord. 2012;12:48. doi: 10.1186/1471-2261-12-48.
    1. Karpetas A, et al. Ambulatory recording of wave reflections and arterial stiffness during intra- and interdialytic periods in patients treated with dialysis. Clin J Am Soc Nephrol. 2015;10(4):630–8. doi: 10.2215/CJN.08180814.
    1. Koutroumbas G, et al. Ambulatory aortic blood pressure, wave reflections and pulse wave velocity are elevated during the third in comparison to the second interdialytic day of the long interval in chronic haemodialysis patients. Nephrol Dial Transplant. 2015;30:2046–53. doi: 10.1093/ndt/gfv090.
    1. Sarafidis PA, et al. Evaluation of a novel brachial cuff-based oscillometric method for estimating central systolic pressure in hemodialysis patients. Am J Nephrol. 2014;40(3):242–50. doi: 10.1159/000367791.
    1. Franssen PM, Imholz BP. Evaluation of the Mobil-O-Graph new generation ABPM device using the ESH criteria. Blood Press Monit. 2010;15(4):229–31. doi: 10.1097/MBP.0b013e328339be38.
    1. Feistritzer HJ, et al. Comparison of an oscillometric method with cardiac magnetic resonance for the analysis of aortic pulse wave velocity. PLoS One. 2015;10(1):e0116862. doi: 10.1371/journal.pone.0116862.
    1. Baumann M, et al. Aortic pulse wave velocity predicts mortality in chronic kidney disease stages 2–4. J Hypertens. 2014;32(4):899–903.
    1. Wang JJ, et al. Hypertensive retinal vessel wall signs in a general older population: the blue mountains eye study. Hypertension. 2003;42(4):534–41. doi: 10.1161/01.HYP.0000090122.38230.41.
    1. Leung H, et al. Relationships between age, blood pressure, and retinal vessel diameters in an older population. Invest Ophthalmol Vis Sci. 2003;44(7):2900–4. doi: 10.1167/iovs.02-1114.
    1. Ikram MK, et al. Retinal vascular caliber measurements: clinical significance, current knowledge and future perspectives. Ophthalmologica. 2013;229(3):125–36. doi: 10.1159/000342158.
    1. Wong TY, et al. Retinal vessel diameters and their associations with age and blood pressure. Invest Ophthalmol Vis Sci. 2003;44(11):4644–50. doi: 10.1167/iovs.03-0079.
    1. Wong TY, et al. The prevalence and risk factors of retinal microvascular abnormalities in older persons: the cardiovascular health study. Ophthalmology. 2003;110(4):658–66. doi: 10.1016/S0161-6420(02)01931-0.
    1. Garhofer G, et al. Use of the retinal vessel analyzer in ocular blood flow research. Acta Ophthalmol. 2010;88(7):717–22. doi: 10.1111/j.1755-3768.2009.01587.x.
    1. Seifertl BU, Vilser W. Retinal Vessel Analyzer (RVA)--design and function. Biomed Tech (Berl) 2002;47(Suppl 1 Pt 2):678–81. doi: 10.1515/bmte.2002.47.s1b.678.
    1. Vilser W, Nagel E, Lanzl I. Retinal Vessel Analysis--new possibilities. Biomed Tech (Berl) 2002;47(Suppl 1 Pt 2):682–5. doi: 10.1515/bmte.2002.47.s1b.682.
    1. Bachler M, et al. Online and offline determination of QT and PR interval and QRS duration in electrocardiography. In: Zu Q, Hu B, Elçi A, editors. Pervasive computing and the networked World. Springer Berlin Heidelberg: Springer; 2013. p. 1–15.
    1. Rautaharju PM, et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part IV: the ST segment, T and U waves, and the QT interval: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation. 2009;119(10):e241–50. doi: 10.1161/CIRCULATIONAHA.108.191096.
    1. Hancock EW, et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part V: electrocardiogram changes associated with cardiac chamber hypertrophy: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation. 2009;119(10):e251–61. doi: 10.1161/CIRCULATIONAHA.108.191097.
    1. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996; 93(5): 1043–65.
    1. Rajendra Acharya U, et al. Heart rate variability: a review. Med Biol Eng Comput. 2006;44(12):1031–51. doi: 10.1007/s11517-006-0119-0.
    1. Sassi R, et al. Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace. 2015;17:1341–53. doi: 10.1093/europace/euv015.
    1. Schmidt G, et al. Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet. 1999;353(9162):1390–6. doi: 10.1016/S0140-6736(98)08428-1.
    1. Barthel P, et al. Risk stratification after acute myocardial infarction by heart rate turbulence. Circulation. 2003;108(10):1221–6. doi: 10.1161/01.CIR.0000088783.34082.89.
    1. Bauer A, et al. Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. Lancet. 2006;367(9523):1674–81. doi: 10.1016/S0140-6736(06)68735-7.
    1. Nasreddine ZS, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9. doi: 10.1111/j.1532-5415.2005.53221.x.
    1. Pereira AA, et al. Subcortical cognitive impairment in dialysis patients. Hemodial Int. 2007;11(3):309–14. doi: 10.1111/j.1542-4758.2007.00185.x.
    1. Kurella Tamura M, et al. Prevalence and correlates of cognitive impairment in hemodialysis patients: the Frequent Hemodialysis Network trials. Clin J Am Soc Nephrol. 2010;5(8):1429–38. doi: 10.2215/CJN.01090210.
    1. Hughes CP, et al. A new clinical scale for the staging of dementia. Br J Psychiatry. 1982;140:566–72. doi: 10.1192/bjp.140.6.566.
    1. Charlson ME, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. doi: 10.1016/0021-9681(87)90171-8.
    1. Liu J, et al. An improved comorbidity index for outcome analyses among dialysis patients. Kidney Int. 2010;77(2):141–51. doi: 10.1038/ki.2009.413.
    1. Hemmelgarn BR, et al. Adapting the Charlson Comorbidity Index for use in patients with ESRD. Am J Kidney Dis. 2003;42(1):125–32. doi: 10.1016/S0272-6386(03)00415-3.
    1. Lindholm B, Davies S. End-stage renal disease: a new comorbidity index for estimating mortality risk in ESRD. Nat Rev Nephrol. 2010;6(7):391–3. doi: 10.1038/nrneph.2010.81.
    1. Floege J, et al. Development and validation of a predictive mortality risk score from a European hemodialysis cohort. Kidney Int. 2015;87(5):996–1008. doi: 10.1038/ki.2014.419.
    1. Fortier C, et al. Aortic-brachial stiffness mismatch and mortality in dialysis population. Hypertension. 2015;65(2):378–84. doi: 10.1161/HYPERTENSIONAHA.114.04587.
    1. Harrell F. Regression modeling strategies. With application to linear models, logistic regression and survival. New York: Springer; 2001.
    1. Peduzzi P, et al. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–10. doi: 10.1016/0895-4356(95)00048-8.
    1. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–8. doi: 10.1093/aje/kwk052.
    1. Beyersmann J, Allignol A, Schumacher M. Competing risks and multistate models with R. New York: Springer; 2012.
    1. McManus RJ, et al. NICE hypertension guideline 2011: evidence based evolution. BMJ. 2012;344:e181. doi: 10.1136/bmj.e181.
    1. Parati G, et al. European Society of Hypertension practice guidelines for ambulatory blood pressure monitoring. J Hypertens. 2014;32(7):1359–66. doi: 10.1097/HJH.0000000000000221.
    1. Agarwal R. Blood pressure and mortality among hemodialysis patients. Hypertension. 2010;55(3):762–8. doi: 10.1161/HYPERTENSIONAHA.109.144899.
    1. Kalantar-Zadeh K, et al. Reverse epidemiology of hypertension and cardiovascular death in the hemodialysis population: the 58th annual fall conference and scientific sessions. Hypertension. 2005;45(4):811–7. doi: 10.1161/01.HYP.0000154895.18269.67.
    1. Zager PG, et al. “U” curve association of blood pressure and mortality in hemodialysis patients. Medical Directors of Dialysis Clinic, Inc. Kidney Int. 1998;54(2):561–9. doi: 10.1046/j.1523-1755.1998.00005.x.
    1. Agarwal R, Jardine A. Pro: Ambulatory blood pressure should be used in all patients on hemodialysis. Nephrol Dial Transplant. 2015;30:1432–7. doi: 10.1093/ndt/gfv243.
    1. Jardine AG, Agarwal R. Con: Ambulatory blood pressure measurement in patients receiving haemodialysis: a sore arm and a waste of time? Nephrol Dial Transplant. 2015;30:1438–41. doi: 10.1093/ndt/gfv244.
    1. Zoccali C, et al. Moderator’s view: Ambulatory blood pressure monitoring and home blood pressure for the prognosis, diagnosis and treatment of hypertension in dialysis patients. Nephrol Dial Transplant. 2015;30:1443–8. doi: 10.1093/ndt/gfv241.
    1. Blacher J, et al. Arterial calcifications, arterial stiffness, and cardiovascular risk in end-stage renal disease. Hypertension. 2001;38(4):938–42. doi: 10.1161/hy1001.096358.
    1. Goodman WG, et al. Coronary-artery calcification in young adults with end-stage renal disease who are undergoing dialysis. N Engl J Med. 2000;342(20):1478–83. doi: 10.1056/NEJM200005183422003.
    1. Blacher J, et al. Impact of aortic stiffness on survival in end-stage renal disease. Circulation. 1999;99(18):2434–9. doi: 10.1161/01.CIR.99.18.2434.
    1. O’Rourke MF, Gallagher DE. Pulse wave analysis. J Hypertens Suppl. 1996;14(5):S147–57.
    1. Papaioannou TG, et al. Non-invasive 24 hour ambulatory monitoring of aortic wave reflection and arterial stiffness by a novel oscillometric device: the first feasibility and reproducibility study. Int J Cardiol. 2013;169(1):57–61. doi: 10.1016/j.ijcard.2013.08.079.
    1. Durand MJ, et al. Acute exertion elicits a H2O2-dependent vasodilator mechanism in the microvasculature of exercise-trained but not sedentary adults. Hypertension. 2015;65(1):140–5. doi: 10.1161/HYPERTENSIONAHA.114.04540.
    1. Lim LS, et al. Structural changes in the retinal microvasculature and renal function. Invest Ophthalmol Vis Sci. 2013;54(4):2970–6. doi: 10.1167/iovs.13-11941.
    1. Ciocalteu AM, Dumitrache M. The impact of hemodialysis on retinal circulation. Oftalmologia. 2011;55(1):94–9.
    1. Winkelmayer WC, et al. The increasing prevalence of atrial fibrillation among hemodialysis patients. J Am Soc Nephrol. 2011;22(2):349–57. doi: 10.1681/ASN.2010050459.
    1. Suzuki M, et al. Nonlinear measures of heart rate variability and mortality risk in hemodialysis patients. Clin J Am Soc Nephrol. 2012;7(9):1454–60. doi: 10.2215/CJN.09430911.
    1. Drawz PE, et al. Heart rate variability is a predictor of mortality in chronic kidney disease: a report from the CRIC Study. Am J Nephrol. 2013;38(6):517–28. doi: 10.1159/000357200.
    1. Chandra P, et al. Relationship between heart rate variability and pulse wave velocity and their association with patient outcomes in chronic kidney disease. Clin Nephrol. 2014;81(1):9–19. doi: 10.5414/CN108020.
    1. Elias MF, Dore GA, Davey A. Kidney disease and cognitive function. Contrib Nephrol. 2013;179:42–57. doi: 10.1159/000346722.
    1. Tiffin-Richards FE, et al. The Montreal Cognitive Assessment (MoCA) - a sensitive screening instrument for detecting cognitive impairment in chronic hemodialysis patients. PLoS One. 2014;9(10):e106700. doi: 10.1371/journal.pone.0106700.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner