Abstract: SA-OR11
Identification of Molecularly Distinct Sub-Phenotypes in AKI and Association with Long-Term Clinical Outcomes
Session Information
- AKI: Repair, Stay Put, or Transition to CKD
November 06, 2021 | Location: Simulive, Virtual Only
Abstract Time: 04:30 PM - 06:00 PM
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