Summary
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.
Keywords
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REFERENCES
- World incidence of AKI: a meta-analysis.Clin J Am Soc Nephrol. 2013; 8: 1482-1493
- Acute kidney injury[mdash]epidemiology, outcomes and economics.Nat Rev Nephrol. 2014; 10: 193-207
- A prospective international multicenter study of AKI in the intensive care unit.Clin J Am Soc Nephrol. 2015; 10: 1324-1331
- Cost of acute kidney injury in hospitalized patients.J Hosp Med. 2017; 12: 70-76
- Long-term risk of coronary events after AKI.J Am Soc Nephrol. 2014; 25: 595-605
- The impact of acute kidney injury on the long-term risk of stroke.J Am Heart Assoc. 2014; 3e000933
- AKI and long-term risk for cardiovascular events and mortality.J Am Soc Nephrol. 2017; 28: 377-387
- The severity of acute kidney injury predicts progression to chronic kidney disease.Kidney Int. 2011; 79: 1361-1369
- Acute kidney injury recovery pattern and subsequent risk of CKD: an analysis of Veterans Health Administration Data.Am J Kidney Dis. 2016; 67: 742-752
- Acute kidney injury episodes and chronic kidney disease risk in diabetes mellitus.Clin J Am Soc Nephrol. 2011; 6: 2567-2572
- KDIGO clinical practice guideline for acute kidney injury.Kidney Int. 2012; 2: 1-138
- Subphenotypes of acute kidney injury in adults.Curr Opin Crit Care. 2022; 28: 599-604
- Subphenotypes in acute kidney injury: a narrative review.Crit Care. 2022; 26: 251
- Artificial intelligence for AKI!Now: let's not await Plato's utopian republic.Kidney360. 2022; 3: 376-381
- AKI transition of care: a potential opportunity to detect and prevent CKD.Clin J Am Soc Nephrol. 2013; 8: 476-483
- Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup.Nat Rev Nephrol. 2017; 13: 241-257
- Acute kidney injury and chronic kidney disease as interconnected syndromes.N Engl J Med. 2014; 371: 58-66
- Post-acute kidney injury proteinuria and subsequent kidney disease progression: the Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) atudy.JAMA Intern Med. 2020; 180: 402-410
- Risk stratification for postoperative acute kidney injury in major noncardiac surgery using preoperative and intraoperative data.JAMA Netw Open. 2019; 2e1916921
- A clinical score to predict acute renal failure after cardiac surgery.J Am Soc Nephrol. 2005; 16: 162-168
- Predictive accuracy of a perioperative laboratory test-based prediction model for moderate to severe acute kidney injury after cardiac surgery.JAMA. 2022; 327: 956-964
- Internal and external validation of a machine learning risk score for acute kidney injury.JAMA Netw Open. 2020; 3e2012892
- The development of a machine learning inpatient acute kidney injury prediction model.Crit Care Med. 2018; 46: 1070-1077
- Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor.Crit Care. 2019; 23: 282
- AKIpredictor, an online prognostic calculator for acute kidney injury in adult critically ill patients: development, validation and comparison to serum neutrophil gelatinase-associated lipocalin.Intensive Care Med. 2017; 43: 764-773
- A simple real-time model for predicting acute kidney injury in hospitalized patients in the US: a descriptive modeling study.PLoS Med. 2019; 16e1002861
- Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury.Clin J Am Soc Nephrol. 2020; 15: 1557-1565
- Candidate surrogate end points for ESRD after AKI.J Am Soc Nephrol. 2016; 27: 2851-2859
- Prediction of mortality and major adverse kidney events in critically ill patients with acute kidney injury.Am J Kidney Dis. Published online July 19, 2022; https://doi.org/10.1053/j.ajkd.2022.06.004
- Derivation and external validation of prediction models for advanced chronic kidney disease following acute kidney injury.JAMA. 2017; 318: 1787-1797
- Validation of risk prediction models to inform clinical decisions after acute kidney injury.Am J Kidney Dis. 2021; 78: 28-37
- Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis.JAMA. 2016; 315: 164-174
- Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.Diabetologia. 2021; 64: 1504-1515
- Dapagliflozin in patients with chronic kidney disease.N Engl J Med. 2020; 383: 1436-1446
- Empagliflozin and progression of kidney disease in type 2 diabetes.N Engl J Med. 2016; 375: 323-334
- Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes.N Engl J Med. 2020; 383: 2219-2229
- Biomarkers during recovery from AKI and prediction of long-term reductions in estimated GFR.Am J Kidney Dis. 2022; 79 (e641): 646-656
- Septic acute kidney injury in critically ill patients: clinical characteristics and outcomes.Clin J Am Soc Nephrol. 2007; 2: 431-439
- Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment.Kidney Int. 2019; 96: 1083-1099
- A unified theory of sepsis-induced acute kidney injury: inflammation, microcirculatory dysfunction, bioenergetics, and the tubular cell adaptation to injury.Shock. 2014; 41: 3-11
- Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis.JAMA. 2019; 321: 2003-2017
- Clinical trials for AKI: lessons learned from the ARDS network.Semin Nephrol. 2020; 40: 243-246
- Design of clinical trials in acute kidney injury: lessons from the past and future directions.Semin Nephrol. 2016; 36: 42-52
- Why have clinical trials in sepsis failed?.Trends Mol Med. 2014; 20: 195-203
- The intensive care medicine research agenda on septic shock.Intensive Care Med. 2017; 43: 1294-1305
- Clinical phenotypes of acute kidney injury are associated with unique outcomes in critically ill septic children.Pediatr Res. 2021; 90: 1031-1038
- Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury.Clin J Am Soc Nephrol. 2020; 15: 1557-1565
- PERSEVERE biomarkers predict severe acute kidney injury and renal recovery in pediatric septic shock.Am J Respir Crit Care Med. 2020; 201: 848-855
- Implications of heterogeneity of treatment effect for reporting and analysis of randomized trials in critical care.Am J Respir Crit Care Med. 2015; 192: 1045-1051
- Redefining critical illness.Nat Med. 2022; 28: 1141-1148
- Serial measurement of cell-cycle arrest biomarkers [TIMP-2]. [IGFBP7] and risk for progression to death, dialysis, or severe acute kidney injury in patients with septic shock.Am J Respir Crit Care Med. 2020; 202: 1262-1270
- Identification of acute kidney injury subphenotypes with differing molecular signatures and responses to vasopressin therapy.Am J Respir Crit Care Med. 2019; 199: 863-872
- Effect of human recombinant alkaline phosphatase on 7-day creatinine clearance in patients with sepsis-associated acute kidney injury: a randomized clinical trial.JAMA. 2018; 320: 1998-2009
- Restrictive fluid management versus usual care in acute kidney injury (REVERSE-AKI): a pilot randomized controlled feasibility trial.Intensive Care Med. 2021; 47: 665-673
- Timing of renal-replacement therapy in patients with acute kidney injury and sepsis.N Engl J Med. 2018; 379: 1431-1442
- High dose coupled plasma filtration and adsorption in septic shock patients. Results of the COMPACT-2: a multicentre, adaptive, randomised clinical trial.Intensive Care Med. 2021; 47: 1303-1311
- Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials.Lancet Respir Med. 2014; 2: 611-620
- Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis.Thorax. 2017; 72: 876-883
- Optimizing the design and analysis of future AKI trials.J Am Soc Nephrol. 2022; 33: 1459-1470
- Machine learning for prediction in electronic health data.JAMA Netw Open. 2018; 1e181404
- Deep learning for electronic health records: a comparative review of multiple deep neural architectures.J Biomed Inform. 2020; 101103337
- Artificial intelligence in healthcare.Nat Biomed Eng. 2018; 2: 719-731
- Medical big data is not yet available: why we need realism rather than exaggeration.Endocrinol Metab. 2019; 34: 349-354
- Mining electronic health records (EHRs). A survey.ACM Comput Surv. 2018; 50: 1-40
- Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration.J Biomed Inform. 2015; 53: 220-228
- Real-time mortality prediction in the intensive care unit.in: Paper Presented at a Conference: AMIA Annual Symposium Proceedings. Washington, DC, November 6, 2017 - November 8, 2017
- Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis.IEEE J Biomed Health Inform. 2017; 22: 1589-1604
- Attention is all you need.Adv Neural Inform Processing Syst. 2017; : 30
- Supervised sequence labelling with recurrent neural networks, Vol 385. Springer, 2012 (ISBN 978-3-642-24796-5)
- On the properties of neural machine translation: encoder-decoder approaches.arXiv preprint. 2014; (arXiv:1409.1259)
- Patient subtyping via time-aware LSTM networks+.in: Paper presented at a Conference: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. Halifax NS, Canada, August 13 - 17, 2017
- Results from the TRIBE-AKI study found associations between post-operative blood biomarkers and risk of chronic kidney disease after cardiac surgery.Kidney Int. 2021; 99: 716-724
- Urinary EGF and MCP-1 and risk of CKD after cardiac surgery.JCI Insight. 2021; 6e147464
- Angiopoietins as prognostic markers for future kidney disease and heart failure events after acute kidney injury.J Am Soc Nephrol. 2022; 33: 613-627
Article info
Publication history
Published online: January 04, 2023
Footnotes
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.
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© 2022 Published by Elsevier Inc.
