Acute kidney injury (AKI) is a complex syndrome with a paucity of therapeutic development. One aspect that could explain the lack of implementation science in the AKI field is the vast heterogeneity of the AKI syndrome, which hinders precise therapeutic applications for specific AKI subpopulations. In this context, there is a consensual focus of the scientific community toward the development and validation of tools to better subphenotype AKI and therefore facilitate precision medicine approaches. The subphenotyping of AKI requires the use of specific methodologies suitable for interrogation of multimodal data inputs from different sources such as electronic health records, organ support devices, and/or biospecimens and tissues. Over the past years, the surge of artificial intelligence applied to health care has yielded novel machine learning methodologies for data acquisition, harmonization, and interrogation that can assist with subphenotyping of AKI. However, one should recognize that although risk classification and subphenotyping of AKI is critically important, testing their potential applications is even more important to promote implementation science. For example, risk-classification should support actionable interventions that could ameliorate or prevent the occurrence of the outcome being predicted. Furthermore, subphenotyping could be applied to predict therapeutic responses to support enrichment and adaptive platforms for pragmatic clinical trials.
|Journal||Seminars in Nephrology|
|State||Published - May 2022|
Bibliographical noteFunding Information:
Financial support: Supported by National Institute of Diabetes and Digestive and Kidney Diseases grants R01DK128208, U01DK129989, and P30 DK079337 (J.A.N.); and by a Canada Research Chair in Critical Care Outcomes and Systems Evaluation (S.M.B.). Conflict of interest statement: Javier A. Neyra has received consulting fees from Baxter and Leadiant Biosciences; and Sean M. Bagshaw has received speaker and unrestricted research funding from Baxter, and scientific advisory fees from Baxter, Novartis, and BioPorto. The remaining authors disclose no conflicts.
Financial support: Supported by National Institute of Diabetes and Digestive and Kidney Diseases grants R01DK128208 , U01DK129989 , and P30 DK079337 (J.A.N.); and by a Canada Research Chair in Critical Care Outcomes and Systems Evaluation (S.M.B.).
- acute kidney injury
- machine learning
- risk classification
ASJC Scopus subject areas