Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Critical Care
Published in: Critical Care 1/2022

Open Access 01-12-2022 | Acute Kidney Injury | Review

Subphenotypes in acute kidney injury: a narrative review

Authors: Suvi T. Vaara, Pavan K. Bhatraju, Natalja L. Stanski, Blaithin A. McMahon, Kathleen Liu, Michael Joannidis, Sean M. Bagshaw

Published in: Critical Care | Issue 1/2022

Login to get access

Abstract

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.
Literature
1.
Nisula S, Kaukonen KM, Vaara ST, et al. Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: the FINNAKI study. Intensive Care Med. 2013;39(3):420–8.PubMedCrossRef
2.
Hoste EA, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41(8):1411–23.PubMedCrossRef
3.
4.
Kidney Diseases Improving Global Outcomes. KDIGO clinical practice guideline for acute kidney injury. Kidney Inter. 2012;2:1–138.
5.
6.
Gallagher KM, O’Neill S, Harrison EM, et al. Recent early clinical drug development for acute kidney injury. Expert Opin Investig Drugs. 2017;26(2):141–54.PubMedCrossRef
7.
Bhatraju PK, Mukherjee P, Robinson-Cohen C, et al. Acute kidney injury subphenotypes based on creatinine trajectory identifies patients at increased risk of death. Crit Care. 2016;20(1):372.PubMedPubMedCentralCrossRef
8.
Bhatraju PK, Zelnick LR, Herting J, et al. Identification of acute kidney injury subphenotypes with differing molecular signatures and responses to vasopressin therapy. Am J Respir Crit Care Med. 2019;199(7):863–72.PubMedPubMedCentralCrossRef
9.
Xu Z, Chou J, Zhang XS, et al. Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks. J Biomed Inform. 2020;102:103361.PubMedCrossRef
10.
Wiersema R, Jukarainen S, Vaara ST, et al. Two subphenotypes of septic acute kidney injury are associated with different 90-day mortality and renal recovery. Crit Care. 2020;24(1):150.PubMedPubMedCentralCrossRef
11.
Joannidis M, Druml W, Forni LG, et al. Prevention of acute kidney injury and protection of renal function in the intensive care unit: update 2017: Expert opinion of the working group on prevention, AKI section, European society of intensive care medicine. Intensive Care Med. 2017;43(6):730–49.PubMedPubMedCentralCrossRef
12.
Pickkers P, Mehta RL, Murray PT, et al. 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(19):1998–2009.PubMedPubMedCentralCrossRef
13.
14.
Ranieri VM, Rubenfeld GD, Thompson BT, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA. 2012;307(23):2526–33.PubMed
15.
Singer M, Deutschman CS, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.PubMedPubMedCentralCrossRef
16.
Reddy K, Sinha P, O’Kane CM, et al. Subphenotypes in critical care: translation into clinical practice. Lancet Respir Med. 2020;8(6):631–43.PubMedCrossRef
17.
Stanski NL, Wong HR. Prognostic and predictive enrichment in sepsis. Nat Rev Nephrol. 2020;16(1):20–31.PubMedCrossRef
18.
19.
Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157–68.PubMedPubMedCentralCrossRef
20.
Sinha P, Calfee CS, Delucchi KL. Practitioner’s guide to latent class analysis: methodological considerations and common pitfalls. Crit Care Med. 2021;49(1):e63–79.PubMedPubMedCentralCrossRef
21.
Wiersema R, Jukarainen S, Eck RJ, et al. Different applications of the KDIGO criteria for AKI lead to different incidences in critically ill patients: a post hoc analysis from the prospective observational SICS-II study. Crit Care. 2020;24(1):164.PubMedPubMedCentralCrossRef
22.
Guitterez NV, Diaz A, Timmis GC, et al. Determinants of serum creatinine trajectory in acute contrast nephropathy. J Interv Cardiol. 2002;15(5):349–54.PubMedCrossRef
23.
24.
Smith TD, Soriano VO, Neyra JA, et al. Identifying KDIGO trajectory phenotypes associated with increased inpatient mortality. IEEE nternational Conference on Healthcare Informatics. 2019.
25.
Ozrazgat-Baslanti T, Loftus TJ, Ren Y, et al. Association of persistent acute kidney injury and renal recovery with mortality in hospitalised patients. BMJ Health Care Inform. 2021;28(1):e100458.PubMedPubMedCentralCrossRef
26.
Siew ED, Abdel-Kader K, Perkins AM, et al. Timing of recovery from moderate to severe AKI and the risk for future loss of kidney function. Am J Kidney Dis. 2020;75(2):204–13.PubMedCrossRef
27.
Bhatraju PK, Zelnick LR, Chinchilli VM, et al. Association between early recovery of kidney function after acute kidney injury and long-term clinical outcomes. JAMA Netw Open. 2020;3(4):e202682.PubMedPubMedCentralCrossRef
28.
Chawla LS, Davison DL, Brasha-Mitchell E, et al. Development and standardization of a furosemide stress test to predict the severity of acute kidney injury. Crit Care. 2013;17(5):R207.PubMedPubMedCentralCrossRef
29.
Rewa OG, Bagshaw SM, Wang X, et al. The furosemide stress test for prediction of worsening acute kidney injury in critically ill patients: a multicenter, prospective, observational study. J Crit Care. 2019;52:109–14.PubMedPubMedCentralCrossRef
30.
Lumlertgul N, Peerapornratana S, Trakarnvanich T, et al. Early versus standard initiation of renal replacement therapy in furosemide stress test non-responsive acute kidney injury patients (the FST trial). Crit Care. 2018;22(1):101.PubMedPubMedCentralCrossRef
31.
Ostermann M, Zarbock A, Goldstein S, et al. Recommendations on acute kidney injury biomarkers from the acute disease quality initiative consensus conference: a consensus statement. JAMA Netw Open. 2020;3(10):e2019209.PubMedCrossRef
32.
Haase M, Devarajan P, Haase-Fielitz A, et al. The outcome of neutrophil gelatinase-associated lipocalin-positive subclinical acute kidney injury: a multicenter pooled analysis of prospective studies. J Am Coll Cardiol. 2011;57(17):1752–61.PubMedPubMedCentralCrossRef
33.
Nickolas TL, Schmidt-Ott KM, Canetta P, et al. Diagnostic and prognostic stratification in the emergency department using urinary biomarkers of nephron damage: a multicenter prospective cohort study. J Am Coll Cardiol. 2012;59(3):246–55.PubMedPubMedCentralCrossRef
34.
Coca SG, Garg AX, Thiessen-Philbrook H, et al. Urinary biomarkers of AKI and mortality 3 years after cardiac surgery. J Am Soc Nephrol. 2014;25(5):1063–71.PubMedCrossRef
35.
Joannidis M, Forni LG, Haase M, et al. Use of cell cycle arrest biomarkers in conjunction with classical markers of acute kidney injury. Crit Care Med. 2019;47(10):e820–6.PubMedCrossRef
36.
Dépret F, Hollinger A, Cariou A, et al. Incidence and outcome of subclinical acute kidney injury using penKid in critically Ill patients. Am J Respir Crit Care Med. 2020;202(6):822–9.PubMedCrossRef
37.
Hoste E, Bihorac A, Al-Khafaji A, et al. Identification and validation of biomarkers of persistent acute kidney injury: the RUBY study. Intensive Care Med. 2020;46(5):943–53.PubMedPubMedCentralCrossRef
38.
Russell JA, Walley KR, Singer J, et al. Vasopressin versus norepinephrine infusion in patients with septic shock. New Engl J Med. 2008;358(9):877–87.PubMedCrossRef
39.
Lu JC, Coca SG, Patel UD, et al. Searching for genes that matter in acute kidney injury: a systematic review. Clin J Am Soc Nephrol. 2009;4(6):1020–31.PubMedPubMedCentralCrossRef
40.
Bhatraju PK, Cohen M, Nagao RJ, et al. Genetic variation implicates plasma angiopoietin-2 in the development of acute kidney injury sub-phenotypes. BMC Nephrol. 2020;21(1):284.PubMedPubMedCentralCrossRef
41.
Reilly JP, Wang F, Jones TK, et al. Plasma angiopoietin-2 as a potential causal marker in sepsis-associated ARDS development: evidence from Mendelian randomization and mediation analysis. Intensive Care Med. 2018;44(11):1849–58.PubMedPubMedCentralCrossRef
42.
Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361–9.PubMedCrossRef
43.
Siroux V, González JR, Bouzigon E, et al. Genetic heterogeneity of asthma phenotypes identified by a clustering approach. Eur Respir J. 2014;43(2):439–52.PubMedCrossRef
44.
Maslove DM, Tang B, Shankar-Hari M, et al. Redefining critical illness. Nat Med. 2022;28(6):1141–8.PubMedCrossRef
45.
Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med. 2016;375(19):1823–33.PubMedCrossRef
46.
Schmid P, Cortes J, Pusztai L, et al. Pembrolizumab for early triple-negative breast cancer. N Engl J Med. 2020;382(9):810–21.PubMedCrossRef
47.
Choueiri TK, Tomczak P, Park SH, et al. Adjuvant pembrolizumab after nephrectomy in renal-cell carcinoma. N Engl J Med. 2021;385(8):683–94.PubMedCrossRef
48.
Ortega HG, Liu MC, Pavord ID, et al. Mepolizumab treatment in patients with severe eosinophilic asthma. N Engl J Med. 2014;371(13):1198–207.PubMedCrossRef
49.
Pavord ID, Chanez P, Criner GJ, et al. Mepolizumab for eosinophilic chronic obstructive pulmonary disease. N Engl J Med. 2017;377(17):1613–29.PubMedCrossRef
50.
Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014;2(8):611–20.PubMedPubMedCentralCrossRef
51.
Famous KR, Delucchi K, Ware LB, et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. Am J Respir Crit Care Med. 2017;195(3):331–8.PubMedPubMedCentralCrossRef
52.
Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med. 2018;6(9):691–8.PubMedPubMedCentralCrossRef
53.
Sinha P, Delucchi KL, Chen Y, et al. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax. 2022;77(1):13–21.PubMedCrossRef
54.
Sinha P, Delucchi KL, McAuley DF, et al. Development and validation of parsimonious algorithms to classify acute respiratory distress syndrome phenotypes: a secondary analysis of randomised controlled trials. Lancet Respir Med. 2020;8(3):247–57.PubMedPubMedCentralCrossRef
55.
Bos LD, Schouten LR, van Vught LA, et al. Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Thorax. 2017;72(10):876–83.PubMedCrossRef
56.
Bhavani SV, Carey KA, Gilbert ER, et al. Identifying novel sepsis subphenotypes using temperature trajectories. Am J Respir Crit Care Med. 2019;200(3):327–35.PubMedPubMedCentralCrossRef
57.
Seymour CW, Kennedy JN, Wang S, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–17.PubMedPubMedCentralCrossRef
58.
Zador Z, Landry A, Cusimano MD, et al. Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care. Crit Care. 2019;23(1):247.PubMedPubMedCentralCrossRef
59.
Bhavani SV, Wolfe KS, Hrusch CL, et al. Temperature trajectory subphenotypes correlate with immune responses in patients with sepsis. Crit Care Med. 2020;48(11):1645–53.PubMedPubMedCentralCrossRef
60.
Shakoory B, Carcillo JA, Chatham WW, et al. Interleukin-1 receptor blockade is associated with reduced mortality in sepsis patients with features of macrophage activation syndrome: reanalysis of a prior phase III trial. Crit Care Med. 2016;44(2):275–81.PubMedPubMedCentralCrossRef
61.
Meyer NJ, Reilly JP, Anderson BJ, et al. Mortality benefit of recombinant human interleukin-1 receptor antagonist for sepsis varies by initial interleukin-1 receptor antagonist plasma concentration. Crit Care Med. 2018;46(1):21–8.PubMedPubMedCentralCrossRef
62.
Wong HR, Cvijanovich NZ, Anas N, et al. Developing a clinically feasible personalized medicine approach to pediatric septic shock. Am J Respir Crit Care Med. 2015;191(3):309–15.PubMedPubMedCentralCrossRef
63.
Antcliffe DB, Burnham KL, Al-Beidh F, et al. Transcriptomic signatures in sepsis and a differential response to steroids. From the VANISH randomized trial. Am J Respir Crit Care Med. 2019;199(8):980–6.PubMedPubMedCentralCrossRef
64.
65.
Chaudhary K, Vaid A, Duffy Á, et al. Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury. Clin J Am Soc Nephrol. 2020;15(11):1557–65.PubMedPubMedCentralCrossRef
66.
Basu RK, Hackbarth R, Gillespie S, et al. Clinical phenotypes of acute kidney injury are associated with unique outcomes in critically ill septic children. Pediatr Res. 2021;90(5):1031–8.PubMedPubMedCentralCrossRef
67.
Stanski NL, Stenson EK, Cvijanovich NZ, et al. PERSEVERE biomarkers predict severe acute kidney injury and renal recovery in pediatric septic shock. Am J Respir Crit Care Med. 2020;201(7):848–55.PubMedPubMedCentralCrossRef
68.
Maddali MV, Churpek M, Pham T, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir Med. 2022;10(4):367–77.PubMedCrossRef
Metadata
Title
Subphenotypes in acute kidney injury: a narrative review
Authors
Suvi T. Vaara
Pavan K. Bhatraju
Natalja L. Stanski
Blaithin A. McMahon
Kathleen Liu
Michael Joannidis
Sean M. Bagshaw
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Critical Care / Issue 1/2022
Electronic ISSN: 1364-8535
DOI
https://doi.org/10.1186/s13054-022-04121-x

Other articles of this Issue 1/2022

Critical Care 1/2022 Go to the issue

Comment

Intravenous vitamin C in adults with sepsis in the intensive care unit: still LOV’IT?

Research

Respiratory support in patients with severe COVID-19 in the International Severe Acute Respiratory and Emerging Infection (ISARIC) COVID-19 study: a prospective, multinational, observational study

Research

A universal predictive and mechanistic urinary peptide signature in acute kidney injury

Review

Obesity and critical care nutrition: current practice gaps and directions for future research

Research

Dehydration is associated with production of organic osmolytes and predicts physical long-term symptoms after COVID-19: a multicenter cohort study

Research

Respiratory distress observation scales to predict weaning outcome