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Abstract: TH-OR07

Machine Learning Models Uncover Subphenotypes of AKI With Unique Signatures That Associate With Differing Clinical Outcomes

Session Information

Category: Acute Kidney Injury

  • 102 AKI: Clinical‚ Outcomes‚ and Trials

Authors

  • Vasquez-Rios, George, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Oh, Wonsuk, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Lee, Samuel, Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Bhatraju, Pavan K., University of Washington, Seattle, Washington, United States
  • Mansour, Sherry, Yale University, New Haven, Connecticut, United States
  • Moledina, Dennis G., Yale University, New Haven, Connecticut, United States
  • Siew, Edward D., Vanderbilt University, Nashville, Tennessee, United States
  • Garg, Amit X., Western University, London, Ontario, Canada
  • Chinchilli, Vernon M., The Pennsylvania State University, University Park, Pennsylvania, United States
  • Kaufman, James S., New York University, New York, New York, United States
  • Hsu, Chi-yuan, University of California San Francisco, San Francisco, California, United States
  • Liu, Kathleen D., University of California San Francisco, San Francisco, California, United States
  • Kimmel, Paul L., National Institutes of Health, Bethesda, Maryland, United States
  • Go, Alan S., University of California San Francisco, San Francisco, California, United States
  • Wurfel, Mark M., University of Washington, Seattle, Washington, United States
  • Himmelfarb, Jonathan, University of Washington, Seattle, Washington, United States
  • Parikh, Chirag R., Johns Hopkins University, Baltimore, Maryland, United States
  • Coca, Steven G., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Nadkarni, Girish N., Icahn School of Medicine at Mount Sinai, New York, New York, United States
Background

Acute kidney injury (AKI) is defined through serum creatinine and urine output metrics. However, these markers do not capture the complexity of AKI and do not fully inform on the future risk of kidney and clinical events.

Methods

We evaluated clinical and biomarker data from AKI patients during the acute hospitalization from ASSESS-AKI via three machine learning algorithms to uncover different AKI composites. We compared key characteristics within each subphenotype via classic statistics and then examined the time to event for kidney events (CKD incidence and progression), cardiovascular events, and death by subphenotype.

Results

We included 748 AKI patients. The mean age (± SD) was 64 (13) years, 67.9% were men, and the median follow-up was 4.8 years. Patients with AKI subphenotype 1 (‘cardiorenal injury’, N=181) were characterized by prevalent CVD (78%, P<0.001) and the highest levels of KIM-1, urinary IL-18, and Troponin T. Subphenotype 2 (‘benign’, N=250) was comprised of individuals with a low prevalence of comorbid conditions and high uromodulin levels, a marker of tubular repair. AKI subphenotype 3 (‘cardiorenal inflammation, N=159) comprised patients with markedly high levels of pro-BNP, TNFRs and low kidney injury (KIM-1, NGAL). Finally, patients subphenotype 4 (‘sepsis-AKI’, N=158) had high rates of infections and dialysis-requiring AKI. These patients had the highest levels of vascular/kidney (YKL-40, MCP-1), and injury activity. AKI subphenotype 3 and 4 were independently associated with a higher risk of death: adjusted hazard ratios (aHR) of 2.9 (95% CI: 1.8 – 4.6, p<0.001) and 1.6 (1.01 – 2.6, p=0.04), respectively. Subphenotype 3 was also independently associated with triple the risk of CKD outcomes (aHR: 2.6, CI: 1.6 – 4.2) and CVD events (aHR: 2.6, CI: 1.6 – 4.1).

Conclusion

We discovered four novel and clinically meaningful AKI subphenotypes that inform on potential pathway abnormalities that associate with differing risks for long-term events. We found a new role for biomarkers when they are evaluated in an agnostic fashion, which can serve to advance precision medicine in AKI care.

Funding

  • NIDDK Support