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AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

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Page 1: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

AKI to CKD Epidemiology and Predictive Models

Lakhmir S. Chawla, MD

Page 2: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Overview

• Background• Clinical Epidemiology• Mechanism of Post-AKI to CKD Progression• Trial Design

Page 3: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Study’s Conclusion

Page 4: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Coca et al, Kidney International, 2011

Page 5: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 6: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

CKD

CON

ARF

ATN

Page 7: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

AKI Progression to CKDPediatrics

Page 8: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

From: Long-term Renal Prognosis of Diarrhea-Associated Hemolytic Uremic Syndrome:  A Systematic Review, Meta-analysis, and Meta-regression

JAMA. 2003;290(10):1360-1370. doi:10.1001/jama.290.10.1360

These studies had a higher proportion of patients with death or permanentend-stage renal disease (ESRD) at follow-up, explaining 10% of the between-studyvariability (P = .02), and a higher proportion ofpatients with a glomerular filtration rate (GFR) lower than 80 mL/min per1.73 m2, hypertension, or proteinuria at last follow-up, explaining15% of the between-study variability (P<.001).The area of each circle is proportional to the number of patients in eachstudy. Curves are best-fit lines from meta-regression. See "Methods" section.

Figure Legend:

49 studies, 3,476 patients

Page 9: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 10: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 11: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

• 15/29 (59%) had at least one sign of renal injury (hyperfiltration, decr. GFR, or HTN)

• Most conservative estimate– 15/126 (11.9%)

Page 12: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

• Fifty-two patients requiring RRT for AKI• Thirteen available for 12-18 year

follow-up• 9/13 had one sign/symptom of CKD• Majority of patients in both studies

unavailable for follow-up

Page 13: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

PICU Study

• BC Children’s prospective study• AKI defined by AKIN criteria• CKD = < 60 ml/min/1.73m2

• CKD risk– 60 to 90 ml/min/1.73m2 OR– > 150 ml/min/1.73m2

• Microalbuminuria• BP > 95th percentile

Page 14: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 15: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 16: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Summary

• De novo AKI is associated with Incident CKD and ESRD

• Precise estimates of the incidence of CKD progression after AKI in children are lacking due to incomplete follow up

• Children who survive an episode of AKI requiring RRT deserve long-term follow up

Page 17: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

AKIDe novo and ACRF

10-15% Mortality

AKI Survivors Round I

10% ESRD

AKI SurvivorsRound IIAKI SurvivorsRound II

20% CKD 4

AKI SurvivorsRound IIIAKI SurvivorsRound III

0 30d 60-90d 24 mo > 3 yrs

2 million 1.7 million

1.5 million

300K170K

300K

1 Billion

Page 18: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

How does AKI progress to CKD?

• Host Predisposition: genetics / co-morbidities• Nephron loss followed by glomerular

hypertrophy• Fibrosis and Maladaptive repair• Vascular drop out as a consequence of

endothelial injury

Page 19: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Wynn, Nature Med, 2010

Page 20: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Bechtel, Nature Medicine 16, 544–550 (2010) 5 azacytidine

Page 21: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Acute Kidney Injury

Normal Repair and Recovery

Moderate Injury Severe Injury

Cell Cycle Arrest

TGF-Beta1 Predominates

Epigenetic Modification

Sustained Myofibroblast Activation

Interstitial Fibrosis

Page 22: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

.

Spurgeon K R et al. Am J Physiol Renal Physiol 2005;288:F568-F577

©2005 by American Physiological Society

Page 23: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

*Post-AKI vascular density does NOT return to normal*VEGF 121 given early after AKI preserves vascular density*High Na diet promotes fibrosis and progression to CKD

Page 24: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 25: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Can We Intervene?

• So what?• Just like all AKI, if we don’t dialyze it now, we

will have to dialyze it later

• Identification of patients at risk• What are the risk factors?

Page 26: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Derivation Cohort – 5,351 -> Hospitalized patients with ATN or ARF, without CKDValidation Cohort - 11,589 -> Hospitalized patients with MI or Pneumonia and AKI - RIF

Page 27: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 28: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Derivation Cohort Validation Cohort

Model 1 - Full C = 0.82, p < 0.0001 C = 0.81, p < 0.0001

Model 2 - Abbreviated C = 0.81, p < 0.0001 C = 0.81, p < 0.0001

Model 3 – Sentinel Events C = 0.77, p < 0.0001 C =0.82, p < 0.0001

Page 29: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD
Page 30: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

One Year Survivors of AKI

Page 31: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Interventions

• Nephrologist (CKD clinic) See the patient?– HTN control– ACEi– Low protein diet

• TGF-Beta inhibition• VEGF promotion (early post-AKI)• p53 inhibition (early post-AKI)

Page 32: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Summary

• Severity of AKI is associated with CKD progression in AKI survivors

• Decreased concentration of serum albumin is associated with progression to CKD– Likely a marker if increased inflammation

• Breaking the vicious cycle of AKI to CKD to AKI to ESRD could have significant impacts on disease burden

Page 33: AKI to CKD Epidemiology and Predictive Models Lakhmir S. Chawla, MD

Future Directions

• Beta-blocker for MI allegory

• Primary prevention study in AKI survivors to prevent progression to CKD

• Identify patients at risk• Enroll, randomize

• 2 x 2 factorial design• Interventions: BP control, RAAS inhibition, anti-

inflammatory agents,