Biomarker Enrichment in Sepsis Associated Acute Kidney Injury: Finding High-Risk Patients in the ICU

被引:5
作者
Baeseman, Louis [1 ]
Gunning, Samantha [1 ]
Koyner, Jay L. [1 ,2 ]
机构
[1] Univ Chicago, Dept Med, Sect Nephrol, Chicago, IL USA
[2] Univ Chicago, Dept Med, Sect Nephrol, 5841 South Maryland Ave, Suite S-507, MC5100, Chicago, IL 60637 USA
关键词
Acute Kidney Injury; Sepsis; Biomarkers; Clinical Trials; Phenotypes; Endotypes; Outcomes; Renal Replacement Therapy; CRITICALLY-ILL PATIENTS; SEPTIC SHOCK; MORTALITY; THERAPY; LEVEL; AKI;
D O I
10.1159/000534608
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Background: Sepsis associated Acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis associated AKI over and above the use of serum creatinine and urine output.Summary: Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses this data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis associated AKI. While not all of these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis associated AKI.Key messages: Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology respectively.Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g. changes in creatinine or urine output); and can predict other adverse outcomes (e.g. severe consensus definition AKI, inpatient mortality).Finally, emerging artificial intelligence and machine learning derived risk models are able to predict sepsis associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.
引用
收藏
页码:72 / 85
页数:14
相关论文
共 57 条
[1]  
[Anonymous], 2018, KIDNEY INT REP, V3, P1424
[2]   Timing of Initiation of Renal-Replacement Therapy in Acute Kidney Injury [J].
Bagshaw, Sean M. ;
Wald, Ron ;
Adhikari, Neill K. J. ;
Bellomo, Rinaldo ;
da Costa, Bruno R. ;
Dreyfuss, Didier ;
Gallagher, Martin P. ;
Gaudry, Stephane ;
Hoste, Eric A. ;
Lamontagne, Francois ;
Joannidis, Michael ;
Landoni, Giovanni ;
Liu, Kathleen D. ;
McAuley, Daniel F. ;
McGuinness, Shay P. ;
Neyra, Javier A. ;
Nichol, Alistair D. ;
Ostermann, Marlies ;
Palevsky, Paul M. ;
Pettila, Ville ;
Quenot, Jean-Pierre ;
Qiu, Haibo ;
Rochwerg, Bram ;
Schneider, Antoine G. ;
Smith, Orla M. ;
Thome, Fernando ;
Thorpe, Kevin E. ;
Vaara, Suvi ;
Weir, Matthew ;
Wang, Amanda Y. ;
Young, Paul ;
Zarbock, Alexander .
NEW ENGLAND JOURNAL OF MEDICINE, 2020, 383 (03) :240-251
[3]   What is the real impact of acute kidney injury? [J].
Bedford, Michael ;
Stevens, Paul E. ;
Wheeler, Toby W. K. ;
Farmer, Christopher K. T. .
BMC NEPHROLOGY, 2014, 15
[4]   Acute renal failure - definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group [J].
Bellomo, R ;
Ronco, C ;
Kellum, JA ;
Mehta, RL ;
Palevsky, P .
CRITICAL CARE, 2004, 8 (04) :R204-R212
[5]   Efficacy and safety of recombinant human activated protein C for severe sepsis. [J].
Bernard, GR ;
Vincent, JL ;
Laterre, P ;
LaRosa, SP ;
Dhainaut, JF ;
Lopez-Rodriguez, A ;
Steingrub, JS ;
Garber, GE ;
Helterbrand, JD ;
Ely, EW ;
Fisher, CJ .
NEW ENGLAND JOURNAL OF MEDICINE, 2001, 344 (10) :699-709
[6]  
Beunders R, 2017, J APPL LAB MED, V2, P400, DOI 10.1373/jalm.2017.023598
[7]   Assessment of kidney proximal tubular secretion in critical illness [J].
Bhatraju, Pavan K. ;
Chai, Xin-Ya ;
Sathe, Neha A. ;
Ruzinski, John ;
Siew, Edward D. ;
Himmelfarb, Jonathan ;
Hoofnagle, Andrew N. ;
Wurfel, Mark M. ;
Kestenbaum, Bryan R. .
JCI INSIGHT, 2021, 6 (10)
[8]   Genetic variation implicates plasma angiopoietin-2 in the development of acute kidney injury sub-phenotypes [J].
Bhatraju, Pavan K. ;
Cohen, Max ;
Nagao, Ryan J. ;
Morrell, Eric D. ;
Kosamo, Susanna ;
Chai, Xin-Ya ;
Nance, Robin ;
Dmyterko, Victoria ;
Delaney, Joseph ;
Christie, Jason D. ;
Liu, Kathleen D. ;
Mikacenic, Carmen ;
Gharib, Sina A. ;
Liles, W. Conrad ;
Zheng, Ying ;
Christiani, David C. ;
Himmelfarb, Jonathan ;
Wurfel, Mark M. .
BMC NEPHROLOGY, 2020, 21 (01)
[9]   Identification of Acute Kidney Injury Subphenotypes with Differing Molecular Signatures and Responses to Vasopressin Therapy [J].
Bhatraju, Pavan K. ;
Zelnick, Leila R. ;
Herting, Jerald ;
Katz, Ronit ;
Mikacenic, Carmen ;
Kosamo, Susanna ;
Morrell, Eric D. ;
Robinson-Cohen, Cassianne ;
Calfee, Carolyn S. ;
Christie, Jason D. ;
Liu, Kathleen D. ;
Matthay, Michael A. ;
Hahn, William O. ;
Dmyterko, Victoria ;
Slivinski, Natalie S. J. ;
Russell, Jim A. ;
Walley, Keith R. ;
Christiani, David C. ;
Liles, W. Conrad ;
Himmelfarb, Jonathan ;
Wurfel, Mark M. .
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2019, 199 (07) :863-872
[10]   Subclinical and clinical acute kidney injury share similar urinary peptide signatures and prognosis [J].
Boutin, Louis ;
Latosinska, Agnieszka ;
Mischak, Harald ;
Deniau, Benjamin ;
Asakage, Ayu ;
Legrand, Matthieu ;
Gayat, Etienne ;
Mebazaa, Alexandre ;
Chadjichristos, Christos E. ;
Depret, Francois .
INTENSIVE CARE MEDICINE, 2023, 49 (10) :1191-1202