Prédire la récupéra1on rénale. Michaël DARMON Réanima1on polyvalente CHU de Saint- E1enne

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1 Prédire la récupéra1on rénale Michaël DARMON Réanima1on polyvalente CHU de Saint- E1enne

2 Non- specific AKI in the ICU

3 Lameire et al. Lancet 2005 The classical view

4 Intense renal vasoconstric1on r= ; P=0.002 r=0.54; P=0.019 Experimental model of AKI (Sheep) Control (n=7) E. Coli infusion 48 hours (n=7) E. Coli infusion + recovery (n=9) Langenberg et al. Crit Care Med 2014

5 Tubular necrosis and apoptosis Systema1c review Classical features of ATN Human : 26 /184 AKI pa1ents (14%) Primates : 7/19 animals (37%) Rodents: 23% Langenberg et al. Crit Care 2008

6 Tubular necrosis and apoptosis Control Sepsis Post- mortem analysis 39 pa1ents (36 with AKI including 14 requiring RRT) Features of ATN were common (30/39) and tubular sloughing was constant (35/36) Takasu et al. Am J Respir Crit Care Med 2013

7 Tubular necrosis and apoptosis Tubular injury affected 5.9% (+/- 10%) to 10.3% (+/- 9.5%) of tubules KIM- 1 expression was higher in RRT dependent pa1ents (44.4% vs. 25.4%; P<0.01) Takasu et al. Am J Respir Crit Care Med 2013

8 Molitoris et al. J Clin Invest 2014

9 Transient vs. Persistent AKI

10 Prognos1c impact Kellum et al. Am J Respir Crit Care Med 2016

11 Prognos1c impact Unadjusted confounders however Renal recovery is a 1me dependent factor Affected by death / discharge Kellum et al. Am J Respir Crit Care Med 2016

12 In cri1cally- ill pa1ents n= P<0.001 Perinel et al. Crit Care Med 2015

13 Prognos1c impact of AKI reversibility Final logis1c regression model AKI Reversibility No AKI Transient AKI Persistent AKI OR (95%CI) Ref 1.26 ( ) 1.72 ( ) Final model when AKI severity is taken into account AKI Reversibility No AKI Transient AKI Persistent AKI Ref 1.05 ( ) 1.33 ( ) P AKI stage ( ) 0.03 Perinel et al. Crit Care Med 2015

14 Prognos1c impact Log- rank test : P<0.01 Prognos1c impact Time- dependency taken into account N=3576 Persistent AKI = AKI without recovery at day 4 Truche AS et al. Submiied

15 Interest of biomarkers

16 Urinary indices 203 ICU pa1ents AUC ROC curve : 0.62 [95%CI ] AUC ROC curve : 0.59 [95%CI ] Darmon et al. Crit Care 2011

17 Urinary indices Pons et al. Crit Care 2013

18 Urinary indices Prospec1ve monocentre study 107 consecu1ve pa1ents (28 no AKI, 57 transient AKI, 22 persistent AKI) Performance in detec7ng persistent AKI AUC ROC curve FeNa FeUrea 0.36 Urinary NGAL 0.67 Both high FeNa + (>0.36%) and FeUrea (>31.5%) nega1ve predic1ve value : 92% Vanmassenhove et al. Crit Care 2013

19 Biomarkers of renal injury Prospec1ve mul1centre study 489 pa1ents including: 285 pa1ents without AKI 90 pa1ents with transient AKI (61 with FeNa + <1%) 113 pa1ents with persistent AKI NGAL (µg/mmolcr) P=0.052 P<0.001 KIM-1 (µg/mmolcr) P=0.028 P< No AKI Pre-renal AKI Persistent AKI 0 No AKI Pre-renal AKI Persistent AKI Nejat et al. Kidney interna1onal 2012

20 Biomarkers of renal injury Serum crea1nine eleva1on Both serum crea1nine eleva1on and oliguria Vanmassenhove et al. Crit Care 2013

21 Doppler- based RI Darmon et al. Intensive Care Med 2010

22 Doppler- based RI meta- analysis Sensitivity 1 SROC Curve 0,9 0,8 Symmetric SROC AUC = 0,9163 SE(AUC) = 0,0365 Q* = 0,8492 SE(Q*) = 0,0414 0,7 0,6 0,5 0,4 0,3 0,2 0,1 I²= 73% 0 0 0,2 0,4 0,6 0,8 1 1-specificity Ninet et al. submiied

23 Contrast- enhanced US Injec1on de microbulles Evalua1on de la perfusion par séquences «destruc1on- refilling» Schneider et al. Crit Care 2013

24 Applica1on pra1que - Nordadrénaline 12 pa1ents Etat de choc avant/après Noradrénaline Absence de corréla1on entre CEUS et caractéris1ques des pa1ent Schneider et al. Crit Care 2014

25 Autres limites ou incer1tudes Au- delà de l absence de corréla1on aux caractéris1ques cliniques: Variabilité de la mesure: ~ 25% mtt: 25% RBV: 12.5% Mesure discordantes dans 25% des cas: EvoluHon contradictoire du mtt et du RBV

26 AUC in predic1ng renal recovery 57 Pa1ents Prospec1ve monocentre study Recovery at day 3 AUC in predic7ng Renal recovery MAKE FeNa + (%) 0.65 ( ) 0.64 ( ) Resis1ve index 0.69 ( ) 0.79 ( ) TIMP- 2 IGFBP ( ) 0.79 ( ) Plasma NGAL 0.70 ( ) 0.68 ( ) Kine1c egfr (H24) 0.87 ( ) 0.81 ( ) Chawla et al. CJASN 2015

27 Clinical relevancy

28 Persistent AKI and risk of RRT Post- test probability é Pre- test probability Perinel et al.

29 Time frame to define recovery? Rate of RRT according to transient / persistent AKI and 1me frame Truche et al. Submiied

30 Sta1s1cs and clinical relevancy

31 Assessing diagnos1c test performance Sensi7vity Specificity Intrinsic performance at a specific cut- off Unrelated to the disease prevalence/incidence Unrelated to clinical relevancy

32 Assessing diagnos1c test performance Sensi7vity Specificity Area under ROC curve Overall overview of intrinsic diagnos1c test performance Global overview of quan1ta1ve or semi- quan1ta1ve tests Avoid overes1ma1on at a specific cut- off Unrelated to prevalence / incidence Unrelated to clinical relevancy

33 Assessing diagnos1c test performance Sensi7vity Specificity Area under ROC curve Posi7ve and nega7ve predic7ve value Performance according to prevalence/incidence Closer to clinical relevancy What is the studied popula1on?

34 Assessing diagnos1c test performance Sensi7vity Specificity Area under ROC curve Posi7ve and nega7ve predic7ve value Likelihood ra7o and pre/post- test probabili7es

35 Assessing diagnos1c test performance Baiaglia et al. Arch Intern Med 2006

36 Assessing diagnos1c test performance Clinical probability assessment (bedside) Biomarker are of liile interest when the event (prevalence) is high or low Baiaglia et al. Arch Intern Med 2006

37 Assessing diagnos1c test performance Sensi7vity Specificity Area under ROC curve Posi7ve and nega7ve predic7ve value Likelihood ra7o and pre/post- test probabili7es Net reclassifica7on index (and IDI)

38 Assessment of incremental diagnos1c/prognos1c impact of a diagnos1c test Highly dependant of the chosen model taken as baseline Poorly calibrated models / overconfident risk predic1on leads to misleading results Sta1st Med 2014

39 Conclusion

40 Conclusion Short term reversibility may reflect AKI severity rather than dis1nct pathophysiological mechanisms Pa1ents with persistent AKI : Have a higher AKI severity Have more frequently both serum crea1nine eleva1on and oliguria Require RRT more frequently Persistent AKI is a relevant surrogate in predic1ng need for RRT

41 Conclusion Biomarker are a fantas1c and promising research tool We should not use them yet at bedside

42 Conclusion They may be useful in risk stra1fica1on, toxicity avoidance or biomarker driven strategies Unvalidated hypothesis How not to use biomarker: - As a binary result / as a magic bullet - Without assessment of pre- test probability - Without func1onal biomarkers - Without a predefined purpose

43 Thank you for your aien1on