Abstract
Fluid overload is associated with adverse outcomes in hemodialysis (HD) patients. Two bedside methods are increasingly utilized to evaluate objectively fluid status—bioimpedance and lung ultrasonography, but there is no available direct, head-to-head comparison of their prognostic significance. Importantly, their predictive abilities have never been tested in a HD population, alongside those of a classic model that also incorporates established echocardiographic parameters of increased mortality risk. Between 26 May 2011 and 26 October 2012, we included in the study 173 patients undergoing chronic HD treatment for at least 3 months in a single dialysis unit. Relative fluid overload (RFO) and B-lines score (BLS) were used as candidate predictors. From Cox survival analysis we evaluated the increase in the predictive abilities for all-cause mortality of adding continuous RFO or BLS to a model including conventional predictors . 31 patients (17.9 %) died during a median follow-up of 21.3 (interquartile range 19.9–30.3) months. All Cox models showed good calibration. The C statistic for the all-cause mortality prediction increased significantly when the RFO was included into the baseline model (ΔC statistics 0.058 95 %CI = 0.003–0.114), but not when the BLS was included into the baseline model. Only the model that incorporated RFO showed significantly better risk reclassification abilities than the baseline model (IDI = 3.6 % and continuous NRI = 24.8 %). Fluid overload, as assessed by bioimpedance, and not by lung ultrasonography, improves risk prediction for death, beyond classical and echocardiographic-based risk prediction scores/parameters.
Similar content being viewed by others
References
Ortiz A, Covic A, Fliser D et al (2014) Epidemiology, contributors to, and clinical trials of mortality risk in chronic kidney failure. Lancet 383(9931):1831–1843
Bradbury BD, Fissell RB, Albert JM et al (2007) Predictors of early mortality among incident US hemodialysis patients in the Dialysis Outcomes and Practice Patterns Study (DOPPS). Clin J Am Soc Nephrol 2(1):89–99
Tonelli M, Wiebe N, Culleton B et al (2006) Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol 17(7):2034–2047
Wizemann V, Wabel P, Chamney P et al (2009) The mortality risk of overhydration in haemodialysis patients. Nephrol Dial Transplant 24(5):1574–1579
Hur E, Usta M, Toz H et al (2013) Effect of fluid management guided by bioimpedance spectroscopy on cardiovascular parameters in hemodialysis patients: a randomized controlled trial. Am J Kidney Dis 61(6):957–965
Onofriescu M, Hogas S, Voroneanu L et al (2014) Bioimpedance-guided fluid management in maintenance hemodialysis: a pilot randomized controlled trial. Am J Kidney Dis 64(1):111–118
Gargani L, Frassi F, Soldati G, Tesorio P, Gheorghiade M, Picano E (2008) Ultrasound lung comets for the differential diagnosis of acute cardiogenic dyspnoea: a comparison with natriuretic peptides. Eur J Heart Fail 10(1):70–77
Frassi F, Gargani L, Gligorova S, Ciampi Q, Mottola G, Picano E (2007) Clinical and echocardiographic determinants of ultrasound lung comets. Eur J Echocardiogr 8:474–479
Zoccali C, Torino C, Tripepi R et al (2013) Pulmonary congestion predicts cardiac events and mortality in ESRD. J Am Soc Nephrol 24(4):639–646
Ortiz A, Massy ZA, Fliser D et al (2011) Clinical usefulness of novel prognostic biomarkers in patients on hemodialysis. Nat Rev Nephrol 8(3):141–150
Jambrik Z, Monti S, Coppola V et al (2004) Usefulness of ultrasound lung comets as a nonradiologic sign of extravascular lung water. Am J Cardiol 93(10):1265–1270
Picano E, Frassi F, Agricola E, Gligorova S, Gargani L, Mottola G (2006) Ultrasound lung comets: a clinically useful sign of extravascular lung water. J Am Soc Echocardiogr 19(3):356–363
Moissl UM, Wabel P, Chamney PW et al (2006) Body fluid volume determination via body composition spectroscopy in health and disease. Physiol Meas 27(9):921–933
Lang RM, Bierig M, Devereux RB et al (2005) Recommendations for chamber quantification: a report from the American Society of Echocardiography’s Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardiology. J Am Soc Echocardiogr 18:1440–1463
Contal C, O’Quigley J (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30(3):253–270
Pencina MJ, D’Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 23(13):2109–2123
Pencina MJ, D’Agostino RB Sr, Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30(1):11–21
Machek P, Jirka T, Moissl U, Chamney P, Wabel P (2010) Guided optimization of fluid status in haemodialysis patients. Nephrol Dial Transplant 25(2):538–544
Ferrario M, Moissl U, Garzotto F et al (2014) Effects of fluid overload on heart rate variability in chronic kidney disease patients on hemodialysis. BMC Nephrol 15:26
Moissl U, Arias-Guillén M, Wabel P et al (2013) Bioimpedance-guided fluid management in hemodialysis patients. Clin J Am Soc Nephrol 8(9):1575–1582
Onofriescu M, Siriopol D, Voroneanu L et al (2015) Overhydration, cardiac function and survival in hemodialysis patients. PLoS One 10(8):e0135691
Liu S, Zhang DL, Guo W, Cui WY, Liu WH (2012) Left ventricular mass index and aortic arch calcification score are independent mortality predictors of maintenance hemodialysis patients. Hemodial Int 16(4):504–511
Wald R, Goldstein MB, Wald RM et al (2014) Correlates of left ventricular mass in chronic hemodialysis recipients. Int J Cardiovasc Imaging 30(2):349–356
Mallamaci F, Benedetto FA, Tripepi R et al (2010) Detection of pulmonary congestion by chest ultrasound in dialysis patients. JACC Cardiovasc Imaging 3(6):586–594
Siriopol D, Hogas S, Voroneanu L et al (2013) Predicting mortality in haemodialysis patients: a comparison between lung ultrasonography, bioimpedance data and echocardiography parameters. Nephrol Dial Transplant 28(11):2851–2859
Paudel K, Kausik T, Visser A, Ramballi C, Fan SL (2015) Comparing lung ultrasound with bioimpedance spectroscopy for evaluating hydration in peritoneal dialysis patients. Nephrology (Carlton) 20(1):1–5
Gargani L (2011) Lung ultrasound: a new tool for the cardiologist. Cardiovasc Ultrasound 9:6
Wabel P, Moissl U, Chamney P et al (2008) Towards improved cardiovascular management: the necessity of combining blood pressure and fluid overload. Nephrol Dial Transplant 23(9):2965–2971
Kramer A, Stel VS, Caskey FJ et al (2012) Exploring the association between macroeconomic indicators and dialysis mortality. Clin J Am Soc Nephrol 7(10):1655–1663
Acknowledgments
This study was partially funded by the University of Medicine and Pharmacy Iasi, Grant number IDEI—PCE 2011, PN-II-ID-PCE-2011-3-0637.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Prof. Dr. Adrian Covic is an honorary speaker for Fresenius Medical Care. Fresenius Medical Care is the manufacturer of the BCM® device and was not involved in any way in the study. The other authors have nothing to declare.
Ethical standard
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Figure 1
Images from the ultrasound scanning in two areas from one patient from our study, showing 0 (A) and 2 B-lines (B) (tiff 214 kb)
Rights and permissions
About this article
Cite this article
Siriopol, D., Voroneanu, L., Hogas, S. et al. Bioimpedance analysis versus lung ultrasonography for optimal risk prediction in hemodialysis patients. Int J Cardiovasc Imaging 32, 263–270 (2016). https://doi.org/10.1007/s10554-015-0768-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10554-015-0768-x