1. Prediction of severe Acute Kidney Injury (AKI) using novel biomarkers in patients with sepsis
Marco Fiorentino, MD
Visiting Scholar
Center for Critical Care Nephrology
Department of Critical Care Medicine
University of Pittsburgh

2. Background
AKI is present in 5% of all hospitalized patients and up to 70% of patients in ICUs
Sepsis is the most common cause of AKI in patients admitted to ICUs, accounting for >50% of AKI cases
Patients that develop sepsis-associated AKI have worse short- and long-term outcomes
Even small increases in serum creatinine may greatly impact long-term outcomes

3. Pathophysiological mechanisms of sepsis-induced AKI
From a pathophysiological point of view, sepsis-associated AKI is still not well understood. If previous studies focused on ischemic injury as the primary mechanism involved in sepsis-associated AKI, recently it has been shown that it occurs in the setting of renal vasodilatation and increased renal blood flow. Others mechanisms may be responsible for AKI. During sepsis, inflammatory mediators derived from pathogens (PAMPs) and activated immune cells (DAMPs) mediate host cellular injury. Activation of the endothelium by circulating cytokines leads to increased expression of endothelial adhesion molecules, leucocyte activation that increase the proinflammatory response. Cytokines are also important in inducing procoagulant effect. Microvascular dysfunction is often detected in patients with sepsis; this may play an important role in amplifying the damage; in fact a growing body of evidence shows that sepsis is not associated with renal hypoperfusion but instead is associated with a profound alteration in microvascular blood flow distribution. The microvascular dysfunction is altered by several mechanisms, as endothelial cell injury, mithocondrial dysfunction, coagulopathy; this leads to oxidative stress. Moreover, mithocondrial dysfunction play a key role in the adaptive response of cells to injury, with a reduction in ATP production and activation of cell death pathways, and this causes a decrease in cell functionality and so organ dysfunction. Moreover, microvascular dysfunction causes the release of microparticles, that contribute to the procoagulant state, endothelial dysfunction. So all these mechanisms together may be responsible of the development of sepsis-associated AKI.
Umbro et al., J Infect 2015

4. AKI biomarkers tubular cell enzymes released after renal injury;
inflammatory mediators or cytokines released by kidney-specific cells or by inflammatory cells after damage;
low-molecular-weight proteins, which either are filtered freely in the glomeruli and not adequately reabsorbed by injured tubular cells, or are released by injured tubular cells after acute damage.
Cell cycle arrest biomarkers (TIMP-2 and IGFBP-7)

5. Biomarker integrated model of AKI
This is an integrated model of AKI that shows how AKI biomarkers are molecular determinants of the course of AKI. After renal insult, GRF continues to fall because of worsening injury from hemodynamic alterations, inflammations, apoptosis and necrosis of tubular cells; in this phase, the transition from mild to severe AKI may be promoted by proinjury marker IL-18. Angiotensinogen also may promote renal injury during the extension phase through activation of RAS, as well as IGFBP-7 through its effect as an IGF-1 antagonist. Conversely, the increase in renoprotective NGAL and L-FABP likely represent a response to kidney injury, attenuating renal injury through antiapoptotic and antioxidant mechanisms. TIMP-2 and IGFBP-7 may play a renoprotective role at the same time. During the maintenance and repair phases, KIM-1 promotes renal recovery by conferring a phagocytic phenotype to surviving, proliferating renal tubular cells, allowing these cells to clean up debris from injury and facilitating tissue remodelling. Similarly, NGAL increases cellular proliferation, which is necessary for repopulation of the denuded tubular epithelium. TIMP-2 may also promove recovery. Finally, although the mechanistic link between AKI and progression ot CKD is unknown, the RAS is likely involved and elevated AGT could predict chronicity of renal injury.

6. Aims
Analyze the performance of urinary AKI biomarkers to predict severe AKI(KDIGO stage 2-3 within 12 and 24 hours) in a subcohort of patients enrolled in Protocolized Care for Early Septic Shock (ProCESS) study

7. A randomized Trial of Protocol-Based Care for Early Septic Shock

8. One-year survival by AKI status (No AKI, AKI stage 1, 2 or 3)
One-year survival for AKI stage 2-3 by recovery (complete, partial, none)

9. Cohort study ProCESS Trial (N=1,341) No admission creatinine (N=7)
Chronic dialysis (N=83)
Reference creatinine ≥ 4 (N=8)
ProGReSS AKI subcohort
(N=1,243)
AKI at baseline (N=626)
Baseline biomarkers available (N=150)
Hour 6 biomarkers available (N=135)
AKI stageable in 12 hours
(N=61)
AKI stageable in 24 hours
(N=150)
(N=135)
(N=57)

10. Methods
AKI biomarkers (KIM-1, urine collagen IV, TIMP-2, IGFBP- 7) at time 0 and 6 hours after resuscitation (measured by ELISA). Biomarker performance was assessed using the area under the receiver operating characteristic curve (AUROC).

11. Hour 0 Biomarkers Prediction

12. Hours 6 Biomarkers Prediction

13. The Sapphire Study

14. TIMP-2 and IGFBP7 Outperform Existing Biomarkers
AUC for [TIMP-2]•[IGFBP7] was significantly greater than any existing biomarkers.

15. Isolated Oliguria or Azotemia + [TIMP-2][IGFBP7]
Endpoint= Death, Dialysis or Stage 3 AKI within 7 days * * * *
* P < 0.05 *
OR comparing to T2*I7 ≤0.3 or ≤1.0 and no oliguria or azotemia

16. N for biomarker cohorts*
Methods
Integrated models including AKI biomarkers, serum creatinine and urine output at 6 hours to predict the composite outcome (AKI stage 3, need of RRT or death at day 7) by logistic regression
N for biomarker cohorts*
Biomarker N
KIM-1 hr 6
389
TIMP-2 x IGFBP-7 hr 6
190
NGAL hr 6
199
* Excluding any patient with missing serum creatinine or KDIGO AKI stage 3 at baseline.

17. Exposures and composite outcome
Variable
Categorical definitions & notes
Max serum creatinine Hours 1-6
+ MAX(sCr)/refCr ≥ 1.5
- MAX(sCr)/refCr < 1.5
Urine output Hours 1-6
+ UO_vol/body mass < 3 mL/kg
- UO_vol/body mass ≥ 3 mL/kg
u-KIM-1
+ KIM-1 > ng/mL
- KIM-1 ≤ ng/mL
u-[TIMP-2 x IGFBP-7]
+ TIMP-2 x IGFP-7/1000 ≥ 0.3 ng2/mL2 1000
- TIMP-2 x IGFP-7/1000 < 0.3 ng2/mL2 1000
u-NGAL
+ NGAL ≥ 150 ng/mL
- NGAL < 150 ng/mL
OUTCOME: AKI Stage 3, need of RRT or death on day 7

18. KIM-1 Hour 6 Model Results
Urine output >= 3 mL/kg
Urine output < 3 mL/kg
MAX(sCr)/refCr < 1.5
MAX(sCr)/refCr ≥ 1.5
KIM-1 > ng/mL
3/21 (14.3%)
3/12 (25.0%)
2/11 (18.2%)
11/18 (61.1%)
KIM-1 ≤ ng/mL
7/121 (5.8%)
10/40 (25.0%)
6/19 (31.6%)
3/15 (20.0%)
Odds Ratio (95%CI)
Likelihood ratio test against model without biomarker p-value=0.026
AUROC = 0.74 (ROC Contrast vs no biomarker model p-value=0.07
Three-way interaction term p-value=0.07
2 (0.32, 12.33)
2.5 (0.5, 12.47)
sCr +
U.O. +
sCr -
KIM-1 +
2 (0.33, 11.97)
2.71(0.64, 11.47)
sCr +
U.O. -
sCr -
0.68 (0.2, 2.28)
1.61 (0.54, 4.77)
sCr +
U.O. +
sCr -
KIM-1 -
5.43 (1.91, 15.46)
1 (REFERENCE)
sCr +
U.O. -
sCr -

19. [TIMP-2*IGFBR-7] Hour 6 Model Results
Urine output >= 3 mL/kg
Urine output < 3 mL/kg
MAX(sCr)/refCr < 1.5
MAX(sCr)/refCr ≥ 1.5
IGFBP7xTIMP2/1000 ≥ 0.3 ng2/mL2
3/24 (12.5%)
4/14 (28.6%)
3/10 (30.0%)
8/16 (50.0%)
IGFBP7xTIMP2/1000 < 0.3 ng2/mL2
3/52 (5.8%)
1/8 (12.5%)
0/3 (0.0%)
0/0
Odds Ratio (95%CI)
1.24 (0.24, 6.43)
1.1 (0.21, 5.67)
2.63 (0.52, 13.35)
2.3 (0.47, 11.29)
sCr +
Likelihood ratio test against model without biomarker p-value=0.85
AUROC = 0.73 (ROC Contrast vs no biomarker model p-value=0.11
Three-way interaction term p-value=0.91
U.O. +
sCr -
TIMP2*IGFBP7 +
sCr +
U.O. -
sCr -
2.27 (0.2, 26.39)
2.54 (0.51, 12.54)
2.83 (0.33, 24.47)
1 (Reference)
sCr +
U.O. +
sCr -
TIMP2*IGFBP7 -
sCr +
U.O. -
sCr -

20. NGAL Hour 6 Model Results
Urine output >= 3 mL/kg
Urine output < 3 mL/kg
MAX(sCr)/refCr < 1.5
MAX(sCr)/refCr ≥ 1.5
NGAL ≥ 150 ng/mL
1/9 (11.1%)
3/18 (16.7%)
4/7 (57.1%)
2/4 (50.0%)
NGAL < 150 ng/mL
3/57 (5.3%)
5/12 (41.7%)
1/10 (10.0%)
4/13 (30.8%)
Odds Ratio (95%CI)
0.23 (0.02, 2.59)
2.67 (0.19, 36.76)
5.71 (0.53, 61.41)
2.25 (0.21, 24.36)
sCr +
U.O. +
sCr -
Likelihood ratio test against model without biomarker p-value=0.16
AUROC = 0.72 (ROC Contrast vs no biomarker model p-value=0.26
Three-way interaction term p-value=0.2389
NGAL +
sCr +
U.O. -
sCr -
1.11 (0.16, 7.97)
1.85 (0.39, 8.72)
3.6 (0.66, 19.7)
1 (REFERENCE)
sCr +
U.O. +
sCr -
NGAL -
sCr +
U.O. -
sCr -

21. Conclusions
This study shows that urinary biomarkers, such as baseline TIMP-2 and 6-hour-KIM-1, have good performance in predicting severe AKI in patients with sepsis.
The ability to forecast severe AKI may allow appropriate therapeutic interventions in order to reduce adverse short- and long-term outcomes in patients with sepsis.
The future integration of AKI biomarkers in clinical risk- prediction models may be important to phenotype AKI patients with different outcomes.

22. GRAZIE PER L’ATTENZIONE THANK YOU FOR YOUR ATTENTION