Prevalence and risk factors of hypotension associated with preload-dependence during intermittent hemodialysis in critically ill patients

Laurent Bitker, Frédérique Bayle, Hodane Yonis, Florent Gobert, Véronique Leray, Romain Taponnier, Sophie Debord, Alina Stoian-Cividjian, Claude Guérin, Jean-Christophe Richard, Laurent Bitker, Frédérique Bayle, Hodane Yonis, Florent Gobert, Véronique Leray, Romain Taponnier, Sophie Debord, Alina Stoian-Cividjian, Claude Guérin, Jean-Christophe Richard

Abstract

Background: Hypotension is a frequent complication of intermittent hemodialysis (IHD) performed in intensive care units (ICUs). Passive leg raising (PLR) combined with continuous measurement of cardiac output is highly reliable to identify preload dependence, and may provide new insights into the mechanisms involved in IHD-related hypotension. The aim of this study was to assess prevalence and risk factors of preload dependence-related hypotension during IHD in the ICU.

Methods: A single-center prospective observational study performed on ICU patients undergoing IHD for acute kidney injury and monitored with a PiCCO® device. Primary end points were the prevalence of hypotension (defined as a mean arterial pressure below 65 mm Hg) and hypotension associated with preload dependence. Preload dependence was assessed by the passive leg raising test, and considered present if the systolic ejection volume increased by at least 10% during the test, as assessed continuously by the PiCCO® device.

Results: Forty-seven patients totaling 107 IHD sessions were included. Hypotension was observed in 61 IHD sessions (57%, CI95%: 47-66%) and was independently associated with inotrope administration, higher SOFA score, lower time lag between ICU admission and IHD session, and lower MAP at IHD session onset. Hypotension associated with preload dependence was observed in 19% (CI95%: 10-31%) of sessions with hypotension, and was associated with mechanical ventilation, lower SAPS II, higher pulmonary vascular permeability index (PVPI) and dialysate sodium concentration at IHD session onset. ROC curve analysis identified PVPI and mechanical ventilation as the only variables with significant diagnostic performance to predict hypotension associated with preload dependence (respective AUC: 0.68 (CI95%: 0.53-0.83) and 0.69 (CI95%: 0.54-0.85). A PVPI ≥ 1.6 at IHD session onset predicted occurrence of hypotension associated with preload dependence during IHD with a sensitivity of 91% (CI95%: 59-100%), and a specificity of 53% (CI95%: 42-63%).

Conclusions: The majority of hypotensive episodes occurring during intermittent hemodialysis are unrelated to preload dependence and should not necessarily lead to reduction of fluid removal by hemodialysis. However, high PVPI at IHD session onset and mechanical ventilation are risk factors of preload dependence-related hypotension, and should prompt reduction of planned fluid removal during the session, and/or an increase in session duration.

Figures

Fig. 1
Fig. 1
Prevalence of hypotension during intermittent hemodialysis sessions
Fig. 2
Fig. 2
Variation of CCI from baseline value during hypotension as a function of preload dependence status. Black circles are individual values. CCI continuous cardiac index

References

    1. Kidney Disease Improving Global Outcomes (KDIGO) Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012; 2:1–138.
    1. Schortgen F, Soubrier N, Delclaux C, Thuong M, Girou E, Brun-Buisson C, et al. Hemodynamic tolerance of intermittent hemodialysis in critically ill patients: usefulness of practice guidelines. Am J Respir Crit Care Med. 2000;162:197–202. doi: 10.1164/ajrccm.162.1.9907098.
    1. du Cheyron D, Terzi N, Seguin A, Valette X, Prevost F, Ramakers M, et al. Use of online blood volume and blood temperature monitoring during haemodialysis in critically ill patients with acute kidney injury: a single-centre randomized controlled trial. Nephrol Dial Transplant. 2013;28:430–7. doi: 10.1093/ndt/gfs124.
    1. Vinsonneau C, Camus C, Combes A, Costa de Beauregard MA, Klouche K, Boulain T, et al. Continuous venovenous haemodiafiltration versus intermittent haemodialysis for acute renal failure in patients with multiple-organ dysfunction syndrome: a multicentre randomised trial. Lancet. 2006;368:379–85. doi: 10.1016/S0140-6736(06)69111-3.
    1. Tonelli M, Astephen P, Andreou P, Beed S, Lundrigan P, Jindal K. Blood volume monitoring in intermittent hemodialysis for acute renal failure. Kidney Int. 2002;62:1075–80. doi: 10.1046/j.1523-1755.2002.00523.x.
    1. du Cheyron D, Lucidarme O, Terzi N, Charbonneau P. Blood volume- and blood temperature-controlled hemodialysis in critically ill patients: a 6-month, case-matched, open-label study. Blood Purif. 2010;29:245–51. doi: 10.1159/000266481.
    1. Rosenberg AL, Dechert RE, Park PK, Bartlett RH. Review of a large clinical series: association of cumulative fluid balance on outcome in acute lung injury: a retrospective review of the ARDSnet tidal volume study cohort. J Intensive Care Med. 2009;24:35–46. doi: 10.1177/0885066608329850.
    1. Sakr Y, Vincent JL, Reinhart K, Groeneveld J, Michalopoulos A, Sprung CL, et al. High tidal volume and positive fluid balance are associated with worse outcome in acute lung injury. Chest. 2005;128:3098–108. doi: 10.1378/chest.128.5.3098.
    1. Humphrey H, Hall J, Sznajder I, Silverstein M, Wood L. Improved survival in ARDS patients associated with a reduction in pulmonary capillary wedge pressure. Chest. 1990;97:1176–80. doi: 10.1378/chest.97.5.1176.
    1. Kinet JP, Soyeur D, Balland N, Saint-Remy M, Collignon P, Godon JP. Hemodynamic study of hypotension during hemodialysis. Kidney Int. 1982;21:868–76. doi: 10.1038/ki.1982.111.
    1. Schortgen F. Hypotension during intermittent hemodialysis: new insights into an old problem. Intensive Care Med. 2003;29:1645–9. doi: 10.1007/s00134-003-1945-2.
    1. Daugirdas JT. Dialysis hypotension: a hemodynamic analysis. Kidney Int. 1991;39:233–46. doi: 10.1038/ki.1991.28.
    1. Daugirdas JT. Pathophysiology of dialysis hypotension: an update. Am J Kidney Dis. 2001;38(Suppl 4):S11–17. doi: 10.1053/ajkd.2001.28090.
    1. Michard F, Teboul JL. Predicting fluid responsiveness in ICU patients: a critical analysis of the evidence. Chest. 2002;121:2000–8. doi: 10.1378/chest.121.6.2000.
    1. Pinsky MR. Functional haemodynamic monitoring. Curr Opin Crit Care. 2014;20:288–93. doi: 10.1097/MCC.0000000000000090.
    1. Cavallaro F, Sandroni C, Marano C, La Torre G, Mannocci A, De Waure C, et al. Diagnostic accuracy of passive leg raising for prediction of fluid responsiveness in adults: systematic review and meta-analysis of clinical studies. Intensive Care Med. 2010;36:1475–83. doi: 10.1007/s00134-010-1929-y.
    1. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270:2957–63. doi: 10.1001/jama.1993.03510240069035.
    1. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707–10. doi: 10.1007/BF01709751.
    1. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.
    1. Wiegand H. Survey sampling. Biom Z. 1968;10:88–9. doi: 10.1002/bimj.19680100122.
    1. Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team. Linear and nonlinear mixed effects models. R package version 3.1-124. 2015. Available at: .
    1. Bates D, Maechler M, Bolker B, Walker S. Linear mixed-effects models using Eigen and S4. R package version 1.1-7. 2014. Available at: .
    1. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. doi: 10.1186/1471-2105-12-77.
    1. López-Ratón M, Rodriguez-Alvarez MX, Cadarso-Suárez C, Gude-Sampedro F. OptimalCutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw. 2014;61:1–36. doi: 10.18637/jss.v061.i08.
    1. Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med. 1997;16:2349–80. doi: 10.1002/(SICI)1097-0258(19971030)16:20<2349::AID-SIM667>;2-E.
    1. Jabot J, Teboul JL, Richard C, Monnet X. Passive leg raising for predicting fluid responsiveness: importance of the postural change. Intensive Care Med. 2009;35:85–90. doi: 10.1007/s00134-008-1293-3.
    1. Cherpanath TG, Hirsch A, Geerts BF, Lagrand WK, Leeflang MM, Schultz MJ, Groeneveld AB. Predicting fluid responsiveness by passive leg raising: a systematic review and meta-analysis of 23 clinical trials. Crit Care Med. 2016;doi: 10.1097/CCM.0000000000001556
    1. Hamzaoui O, Monnet X, Richard C, Osman D, Chemla D, Teboul JL. Effects of changes in vascular tone on the agreement between pulse contour and transpulmonary thermodilution cardiac output measurements within an up to 6-hour calibration-free period. Crit Care Med. 2008;36:434–40. doi: 10.1097/01.CCM.OB013E318161FEC4.
    1. Guerin L, Monnet X, Teboul JL. Monitoring volume and fluid responsiveness: from static to dynamic indicators. Best Pract Res Clin Anaesthesiol. 2013;27:177–85. doi: 10.1016/j.bpa.2013.06.002.

Source: PubMed

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