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Abstract: SA-OR11

Identification of Molecularly Distinct Sub-Phenotypes in AKI and Association with Long-Term Clinical Outcomes

Session Information

Category: Acute Kidney Injury

  • 101 AKI: Epidemiology, Risk Factors, and Prevention

Authors

  • Bhatraju, Pavan K., University of Washington, Seattle, Washington, United States
  • Prince, David K., University of Washington, Seattle, Washington, United States
  • Mansour, Sherry, Yale University, New Haven, Connecticut, United States
  • Ikizler, Talat Alp, Vanderbilt University, Nashville, Tennessee, United States
  • Siew, Edward D., Vanderbilt University, Nashville, Tennessee, United States
  • Garg, Amit X., Western University, London, Ontario, Canada
  • Go, Alan S., University of California San Francisco, San Francisco, California, United States
  • Kaufman, James S., New York University, New York, New York, United States
  • Kimmel, Paul L., The George Washington University Milken Institute of Public Health, Washington, District of Columbia, United States
  • Coca, Steven G., Mount Sinai Health System, New York, New York, United States
  • Parikh, Chirag R., Johns Hopkins University, Baltimore, Maryland, United States
  • Wurfel, Mark M., University of Washington, Seattle, Washington, United States
  • Himmelfarb, Jonathan, University of Washington, Seattle, Washington, United States
Background

AKI is a heterogeneous clinical syndrome with varying causes, pathophysiology and diverse clinical outcomes; however, staging AKI by serum creatinine does not fully capture underlying patient heterogeneity. Our goal was to identify AKI sub-phenotypes more tightly linked to underlying pathophysiology and long-term clinical outcomes.

Methods

We independently applied latent class analysis (LCA) and k-Means clustering to 29 clinical, plasma and urinary biomarker data measured during hospitalization to identify AKI sub-phenotypes in the ASSESS-AKI study. AKI sub-phenotype associations were examined with the composite of major adverse kidney events (MAKE), defined as incident or progressive chronic kidney disease, long-term dialysis, or all-cause death during study follow-up.

Results

Among 769 AKI patients both LCA and k-Means clustering identified two AKI sub-phenotypes. Class 1 was characterized by a higher prevalence of prior congestive heart failure and favorable blood inflammatory and urinary tubular injury biomarkers, while class 2 was characterized by higher rates of prior chronic kidney disease and less favorable biomarkers. After a median follow-up of 4.7 years, the risk for MAKE was higher with class 2 (HR 1.41; 95% CI, 1.08 to 1.84) compared with class 1 adjusting for demographics, hospital level factors and KDIGO Stage of AKI. The higher risk of MAKE among class 2 was explained by a higher risk of chronic kidney disease progression and dialysis (Figure 1).

Conclusion

In this analysis, we identify two molecularly distinct AKI sub-phenotypes with differing risk of long-term outcomes, independent of current criteria to risk stratify AKI. Future identification of AKI sub-phenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI.

Funding

  • NIDDK Support