Advertisement

Performance of Doppler-based resistive index and semi-quantitative renal perfusion in predicting persistent AKI: results of a prospective multicenter study

  • Michael Darmon
  • Aurelie Bourmaud
  • Marie Reynaud
  • Stéphane Rouleau
  • Ferhat Meziani
  • Alexandra Boivin
  • Mourad Benyamina
  • François Vincent
  • Alexandre Lautrette
  • Christophe Leroy
  • Yves Cohen
  • Matthieu Legrand
  • Jérôme Morel
  • Jeremy Terreaux
  • David Schnell
Original

Abstract

Purpose

The Doppler-based resistive index (RI) and semi-quantitative evaluation of renal perfusion using color Doppler (SQP) have shown promising results for predicting persistent acute kidney injury (AKI) in preliminary studies. This study aimed at evaluating the performance of RI and SQP to predict short-term renal prognosis in critically ill patients.

Methods

Prospective multicenter cohort study including unselected critically ill patients. Renal Doppler was performed at admission to the intensive care unit. The diagnostic performance of RI and SQP to predict persistent AKI at day 3 was evaluated.

Results

Overall, 371 patients were included, of whom 351 could be assessed for short-term renal recovery. Two thirds of the included patients had AKI (n = 233; 66.3%), of whom 136 had persistent AKI (58.4%). Doppler-based RI was higher and SQP lower in AKI patients and according to AKI recovery. Overall performance in predicting persistent AKI was however poor with area under ROC curve of respectively 0.58 (95% CI 0.52–0.64) and 0.59 (95% CI 0.54–0.65) for RI and SQP. Optimal cutoff was respectively 0.71 and 2 for RI and SQP. At optimal cutoff, sensitivity and specificity were 50% (95% CI 41–58%) and 68% (62–74%) for RI and 39% (32–45%) and 75% (66–82%) for SQP.

Conclusion

Although statistically associated with AKI occurrence, RI and SQP perform poorly in predicting persistent AKI at day 3. Further studies are needed to adequately describe factors influencing Doppler-based assessment of renal perfusion and to delineate whether these indicators may be useful at the bedside.

Clinicaltrial.gov

NCT02355314.

Keywords

Acute kidney injury Doppler Resistive index Sensitivity Specificity Renal replacement therapy 

Abbreviations

AKI

Acute kidney injury

AUROC curve

Area under the receiver-operating characteristic curve

CI

Confidence interval

ICU

Intensive care unit

IQR

Interquartile range

LOD

Logistic organ dysfunction

MDRD

Modification of diet in renal disease

MV

Mechanical ventilation

OR

Odds ratio

RI

Doppler-based renal resistive index

ROC curve

Receiver-operating characteristic curve

RRT

Renal replacement therapy

SQP

Semi-quantitative perfusion

Notes

Funding

This study was supported by Saint-Etienne University Hospital

Compliance with ethical standards

Disclosures

M.D. declares having received administrative support from his former institution (Saint-Etienne University Hospital) to conduct this study and having received research support from Astute Medical unrelated to the current study. The other authors declare no conflict of interest related to this manuscript.

Supplementary material

134_2018_5386_MOESM1_ESM.docx (53 kb)
Supplementary material 1 (DOCX 52 kb)
134_2018_5386_MOESM2_ESM.pptx (179 kb)
Supplementary material 2 (PPTX 179 kb)

References

  1. 1.
    Prowle JR, Liu Y-L, Licari E, Bagshaw SM, Egi M, Haase M et al (2011) Oliguria as predictive biomarker of acute kidney injury in critically ill patients. Crit Care 15(4):R172CrossRefGoogle Scholar
  2. 2.
    Bagshaw SM (2008) Short- and long-term survival after acute kidney injury. Nephrol Dial Transplant 23(7):2126–2128CrossRefGoogle Scholar
  3. 3.
    Metnitz PGH, Krenn CG, Steltzer H, Lang T, Ploder J, Lenz K et al (2002) Effect of acute renal failure requiring renal replacement therapy on outcome in critically ill patients. Crit Care Med 30(9):2051–2058CrossRefGoogle Scholar
  4. 4.
    Mehta RL, Kellum JA, Shah SV, Molitoris BA, Ronco C, Warnock DG et al (2007) Acute kidney injury network: report of an initiative to improve outcomes in acute kidney injury. Crit Care 11(2):R31CrossRefGoogle Scholar
  5. 5.
    Waikar SS, Bonventre JV (2009) Creatinine kinetics and the definition of acute kidney injury. J Am Soc Nephrol 20(3):672–679CrossRefGoogle Scholar
  6. 6.
    Kellum JA, Lameire N (2013) KDIGO AKI guideline work group. diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Crit Care 17(1):204CrossRefGoogle Scholar
  7. 7.
    Darmon M, Schortgen F, Vargas F, Liazydi A, Schlemmer B, Brun-Buisson C et al (2011) Diagnostic accuracy of Doppler renal resistive index for reversibility of acute kidney injury in critically ill patients. Intensive Care Med 37(1):68–676CrossRefGoogle Scholar
  8. 8.
    Lerolle N, Guérot E, Faisy C, Bornstain C, Diehl J-L, Fagon J-Y (2006) Renal failure in septic shock: predictive value of Doppler-based renal arterial resistive index. Intensive Care Med 32(10):1553–1559CrossRefGoogle Scholar
  9. 9.
    Schnell D, Deruddre S, Harrois A, Pottecher J, Cosson C, Adoui N et al (2012) Renal resistive index better predicts the occurrence of acute kidney injury than cystatin C. Shock 38(6):592–597CrossRefGoogle Scholar
  10. 10.
    Platt JF, Rubin JM, Ellis JH (1991) Acute renal failure: possible role of duplex Doppler US in distinction between acute prerenal failure and acute tubular necrosis. Radiology 179(2):419–423CrossRefGoogle Scholar
  11. 11.
    Schnell D, Reynaud M, Venot M, Le Maho AL, Dinic M, Baulieu M et al (2014) Resistive Index or color-Doppler semi-quantitative evaluation of renal perfusion by inexperienced physicians: results of a pilot study. Minerva Anestesiol 80(12):1273–1281PubMedGoogle Scholar
  12. 12.
    Dewitte A, Coquin J, Meyssignac B, Joannès-Boyau O, Fleureau C, Roze H et al (2012) Doppler resistive index to reflect regulation of renal vascular tone during sepsis and acute kidney injury. Crit Care 16(5):R165CrossRefGoogle Scholar
  13. 13.
    Schnell D, Camous L, Guyomarc’h S, Duranteau J, Canet E, Gery P et al (2013) Renal perfusion assessment by renal Doppler during fluid challenge in sepsis. Crit Care Med 41(5):1214–1220CrossRefGoogle Scholar
  14. 14.
    Ninet S, Schnell D, Dewitte A, Zeni F, Meziani F, Darmon M (2015) Doppler-based renal resistive index for prediction of renal dysfunction reversibility: a systematic review and meta-analysis. J Crit Care 30(3):629–635CrossRefGoogle Scholar
  15. 15.
    Schnell D, Darmon M (2012) Renal Doppler to assess renal perfusion in the critically ill: a reappraisal. Intensive Care Med 38(11):1751–1760CrossRefGoogle Scholar
  16. 16.
    Lerolle N (2012) Please don’t call me RI anymore; I may not be the one you think I am! Crit Care 16(6):174CrossRefGoogle Scholar
  17. 17.
    Dewitte A, Joannès-Boyau O, Sidobre C, Fleureau C, Bats M-L, Derache P et al (2015) Kinetic eGFR and Novel AKI Biomarkers to predict renal recovery. Clin J Am Soc Nephrol 10(11):1900–1910CrossRefGoogle Scholar
  18. 18.
    Kellum JA, Lameire N (2013) KDIGO AKI guideline work group. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Crit Care 17(1):204CrossRefGoogle Scholar
  19. 19.
    Perinel S, Vincent F, Lautrette A, Dellamonica J, Mariat C, Zeni F et al (2015) Transient and persistent acute kidney injury and the risk of hospital mortality in critically ill patients: results of a multicenter cohort study. Crit Care Med 43(8):269–275CrossRefGoogle Scholar
  20. 20.
    Chawla LS, Bellomo R, Bihorac A, Goldstein SL, Siew ED, Bagshaw SM et al (2017) Acute kidney disease and renal recovery: consensus report of the acute disease quality initiative (ADQI) 16 workgroup. Nat Rev Nephrol 13(4):241–257CrossRefGoogle Scholar
  21. 21.
    Le Gall JR, Klar J, Lemeshow S, Saulnier F, Alberti C, Artigas A et al (1996) The logistic organ dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU scoring group. JAMA 276(10):802–810CrossRefGoogle Scholar
  22. 22.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13(10):818–829CrossRefGoogle Scholar
  23. 23.
    Levy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D et al (2003) 2001 SCCM/ESICM/ACCP/ATS/SIS international sepsis definitions conference. Intensive Care Med 29(4):530–538CrossRefGoogle Scholar
  24. 24.
    Hajian-Tilaki K (2014) Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform 48:193–204CrossRefGoogle Scholar
  25. 25.
    Vittinghoff E, McCulloch CE (2007) Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol 165(6):710–718CrossRefGoogle Scholar
  26. 26.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845CrossRefGoogle Scholar
  27. 27.
    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C et al (2011) pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinform 17(12):77CrossRefGoogle Scholar
  28. 28.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874CrossRefGoogle Scholar
  29. 29.
    Perkins NJ, Schisterman EF (2006) The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol 163(7):670–675CrossRefGoogle Scholar
  30. 30.
    Kellum JA, Sileanu FE, Murugan R, Lucko N, Shaw AD, Clermont G (2015) Classifying AKI by urine output versus serum creatinine level. J Am Soc Nephrol 26(9):2231–2238CrossRefGoogle Scholar
  31. 31.
    Izumi M, Sugiura T, Nakamura H, Nagatoya K, Imai E, Hori M (2000) Differential diagnosis of prerenal azotemia from acute tubular necrosis and prediction of recovery by Doppler ultrasound. Am J Kidney Dis 35(4):713–719CrossRefGoogle Scholar
  32. 32.
    Stevens PE, Gwyther SJ, Hanson ME, Boultbee JE, Kox WJ, Phillips ME (1990) Noninvasive monitoring of renal blood flow characteristics during acute renal failure in man. Intensive Care Med 16(3):153–158CrossRefGoogle Scholar
  33. 33.
    Tublin ME, Tessler FN, Murphy ME (1999) Correlation between renal vascular resistance, pulse pressure, and the resistive index in isolated perfused rabbit kidneys. Radiology 213(1):258–264CrossRefGoogle Scholar
  34. 34.
    Bude RO, Rubin JM (1999) Relationship between the resistive index and vascular compliance and resistance. Radiology 211(2):411–417CrossRefGoogle Scholar
  35. 35.
    Murphy ME, Tublin ME (2000) Understanding the Doppler RI: impact of renal arterial distensibility on the RI in a hydronephrotic ex vivo rabbit kidney model. J Ultrasound Med 19(5):303–314CrossRefGoogle Scholar
  36. 36.
    Darmon M, Schortgen F, Leon R, Moutereau S, Mayaux J, Di Marco F et al (2009) Impact of mild hypoxemia on renal function and renal resistive index during mechanical ventilation. Intensive Care Med 35(6):1031–1038CrossRefGoogle Scholar
  37. 37.
    Naesens M, Heylen L, Lerut E, Claes K, De Wever L, Claus F et al (2013) Intrarenal resistive index after renal transplantation. N Engl J Med 369(19):1797–1806CrossRefGoogle Scholar
  38. 38.
    Cook NR (2007) Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115(7):928–935CrossRefGoogle Scholar
  39. 39.
    de Grooth H-J, Parienti J-J, Schetz M (2018) AKI biomarkers are poor discriminants for subsequent need for renal replacement therapy, but do not disqualify them yet. Intensive Care Med 44(7):1156–1158CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and ESICM 2018

Authors and Affiliations

  • Michael Darmon
    • 1
    • 2
    • 3
  • Aurelie Bourmaud
    • 4
  • Marie Reynaud
    • 5
  • Stéphane Rouleau
    • 6
  • Ferhat Meziani
    • 7
    • 8
  • Alexandra Boivin
    • 7
  • Mourad Benyamina
    • 9
  • François Vincent
    • 10
  • Alexandre Lautrette
    • 11
  • Christophe Leroy
    • 11
  • Yves Cohen
    • 12
  • Matthieu Legrand
    • 2
    • 9
  • Jérôme Morel
    • 5
    • 13
  • Jeremy Terreaux
    • 14
    • 15
  • David Schnell
    • 6
    • 7
  1. 1.Medical ICUSaint-Louis University Hospital, AP-HPParisFrance
  2. 2.Faculté de MédecineUniversité Paris-Diderot, Sorbonne-Paris-CitéParisFrance
  3. 3.ECSTRA Team, Biostatistics and Clinical EpidemiologyUMR 1153 (Center of Epidemiology and Biostatistic, Sorbonne Paris Cité, CRESS), INSERMParisFrance
  4. 4.Hygée Centre and Public Health DepartmentLucien Neuwirth Cancerology InstituteSaint-Priest-En-JarezFrance
  5. 5.Surgical ICUSaint-Etienne University HospitalSaint-EtienneFrance
  6. 6.Medical-Surgical ICUAngoulême HospitalAngoulêmeFrance
  7. 7.Faculté de Médecine, Service de RéanimationUniversité de Strasbourg (UNISTRA), Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital CivilStrasbourgFrance
  8. 8.INSERM (French National Institute of Health and Medical Research), UMR 1260, Regenerative Nanomedicine (RNM), FMTSStrasbourgFrance
  9. 9.Surgical ICU and Burn UnitSaint-Louis University Hospital, AP-HPParisFrance
  10. 10.Medical Surgical ICUGHIC Le Raincy-MontfermeilMontfermeilFrance
  11. 11.Intensive Care UnitGabriel Montpied Hospital, University Hospital of Clermont-FerrandClermont-FerrandFrance
  12. 12.Medical-Surgical ICUAvicenne University Hospital, AP-HPParisFrance
  13. 13.Saint-Etienne University, Jacques Lisfranc Medical SchoolSaint-EtienneFrance
  14. 14.Medical-Surgical ICUSaint-Etienne University HospitalSaint-EtienneFrance
  15. 15.Cardiology UnitSaint-Etienne University HospitalSaint-EtienneFrance

Personalised recommendations