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Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes

医学 急性肾损伤 危险系数 生物标志物 内科学 泌尿系统 透析 肾脏疾病 比例危险模型 队列 队列研究 重症监护医学 置信区间 生物化学 化学
作者
Pavan K. Bhatraju,David K. Prince,Sherry G. Mansour,T. Alp İkizler,Edward D. Siew,Vernon M. Chinchilli,Amit X. Garg,Alan S. Go,James S. Kaufman,Paul L. Kimmel,Steven G. Coca,Chirag R. Parikh,Mark M. Wurfel,Jonathan Himmelfarb
出处
期刊:American Journal of Kidney Diseases [Elsevier BV]
卷期号:82 (3): 311-321.e1 被引量:6
标识
DOI:10.1053/j.ajkd.2023.01.449
摘要

Rationale & Objective

Acute kidney injury (AKI) is a heterogeneous clinical syndrome with varying causes, pathophysiology, and outcomes. We incorporated plasma and urine biomarker measurements to identify AKI subgroups (subphenotypes) more tightly linked to underlying pathophysiology and long-term clinical outcomes.

Study Design

Multicenter cohort study.

Setting & Participants

769 hospitalized adults with AKI matched with 769 without AKI, enrolled from December 2009 to February 2015 in the ASSESS-AKI Study.

Predictors

29 clinical, plasma, and urinary biomarker parameters used to identify AKI subphenotypes.

Outcome

Composite of major adverse kidney events (MAKE) with a median follow-up period of 4.7 years.

Analytical Approach

Latent class analysis (LCA) and k-means clustering were applied to 29 clinical, plasma, and urinary biomarker parameters. Associations between AKI subphenotypes and MAKE were analyzed using Kaplan-Meier curves and Cox proportional hazard models.

Results

Among 769 AKI patients both LCA and k-means identified 2 distinct AKI subphenotypes (classes 1 and 2). The long-term risk for MAKE was higher with class 2 (adjusted HR, 1.41 [95% CI, 1.08-1.84]; P=0.01) 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 long-term chronic kidney disease progression and dialysis. The top variables that were different between classes 1 and 2 included plasma and urinary biomarkers of inflammation and epithelial cell injury; serum creatinine ranked 20th out of the 29 variables for differentiating classes.

Limitations

A replication cohort with simultaneously collected blood and urine sampling in hospitalized adults with AKI and long-term outcomes was unavailable.

Conclusions

We identify 2 molecularly distinct AKI subphenotypes with differing risk of long-term outcomes, independent of the current criteria to risk stratify AKI. Future identification of AKI subphenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI.

Plain-Language Summary

Acute kidney injury (AKI) occurs commonly in hospitalized patients and is associated with high morbidity and mortality. The AKI definition lumps many different types of AKI together, but subgroups of AKI may be more tightly linked to the underlying biology and clinical outcomes. We used 29 different clinical, blood, and urinary biomarkers and applied 2 different statistical algorithms to identify AKI subtypes and their association with long-term outcomes. Both clustering algorithms identified 2 AKI subtypes with different risk of chronic kidney disease, independent of the serum creatinine concentrations (the current gold standard to determine severity of AKI). Identification of AKI subtypes may facilitate linking therapies to underlying biology to prevent long-term consequences after AKI.
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