• 1.

    Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 2012; 120: c179c184. doi: 10.1159/000339789

    • Search Google Scholar
    • Export Citation
  • 2.

    Lin J, et al. False-positive rate of AKI using consensus creatinine-based criteria. Clin J Am Soc Nephrol 2015; 10:17231731. doi: 10.2215/CJN.02430315

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    El-Khoury JM, et al. AACC guidance document on laboratory investigation of acute kidney injury. J Appl Lab Med [published online ahead of print May 11, 2021]. doi: 10.1093/jalm/jfab020; https://academic.oup.com/jalm/advance-article-abstract/doi/10.1093/jalm/jfab020/6272705?redirectedFrom=fulltext

    • Search Google Scholar
    • Export Citation
  • 4.

    European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database. http://biologicalvariation.eu/search?q¼creatinine

  • 5.

    Jones GRD. Estimates of within-subject biological variation derived from pathology databases: An approach to allow assessment of the effects of age, sex, time between sample collections, and analyte concentration on reference change values. Clin Chem 2019; 65: 579588. doi: 10.1373/clinchem.2018.290841

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Chronopoulos A, et al. Acute kidney injury in elderly intensive care patients: A review. Intensive Care Med 2010; 36:14541464. doi: 10.1007/s00134-010-1957-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Perazella MA, et al. Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients. Clin J Am Soc Nephrol 2010; 5:402408. doi:10.2215/CJN.06960909

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Cavanaugh C, Perazella MA. Urine sediment examination in the diagnosis and management of kidney disease: Core curriculum 2019. Am J Kidney Dis 2019; 73:258272. doi: 10.1053/j.ajkd.2018.07.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Shaikh S, Selzter J. The resurgence of urine microscopy. Kidney News 2021; 13(7):20. https://www.kidneynews.org/view/journals/kidney-news/13/7/article-p20_17.xml?tab_body=fulltex

    • Search Google Scholar
    • Export Citation
  • 10.

    Muriithi AK, et al. Utility of urine eosinophils in the diagnosis of acute interstitial nephritis. Clin J Am Soc Nephrol 2013; 8:18571862. doi: 10.2215/CJN.01330213

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Wen Y, Parikh CR. Current concepts and advances in bio-markers of acute kidney injury. Crit Rev Clin Lab Sci 2021; 58:354368. doi: 10.10800/10408363.2021.1879000

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Hsu C-Y, et al. Post-acute kidney injury proteinuria and subsequent kidney disease progression: The assessment, serial evaluation, and subsequent sequelae in acute kidney injury (ASSESS-AKI) Study. JAMA Intern Med 2020; 180:402410. doi: 10.1001/jamainternmed.2019.6390

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Puthumana J, et al. Biomarkers of inflammation and repair in kidney disease progression. J Clin Invest 2021; 131:e139927. doi: 10.1172/JCI139927 doi:10.1172/JCI139927

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Meersch M, et al. Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: The PrevAKI randomized controlled trial. Intensive Care Med 2017; 43:15511561. doi: 10.1007/s00134-016-4670-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Kashani K, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care 2013; 17:R25. doi: 10.1186/cc12503

  • 16.

    Seegmiller JC, et al. Moving toward standardization of urine albumin measurements. EJIFCC 2017; 28:258267. http://www.ifcc.org/media/476804/ejifcc2017vol-28no4pp258-267.pdf

    • Search Google Scholar
    • Export Citation

Laboratory Evaluation of Acute Kidney Injury

  • 1 Yumeng Wen, MD, is Clinical and Research Fellow, and Chirag R. Parikh, MD, PhD, is Division Chief, Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, MD.
Full access

Acute kidney injury (AKI) is common in hospitalized patients and is associated with long-term risks of chronic kidney disease (CKD) and end stage kidney disease (ESKD). An abrupt increase in serum creatinine (SCr) over 48–72 hours is the key finding in the diagnosis of AKI, as recommended by the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guideline (1). Despite advances in biomarkers of AKI, over- and under-diagnosis remain challenges in the evaluation of AKI. In patients with CKD, the false positive rate of AKI diagnosis can occur in 30.5% of patients, possibly due to a lack of appreciation of the analytic variability of SCr (2,3).

Most laboratories in the US use one of two types of SCr assays: Jaffe alkaline picrate or enzymatic methodology (3). The coefficient of variation of these assays ranges from 2.7% to 5.3%, according to the College of American Pathologists 2019 survey, which means some small variation is expected when comparing results across different labs and among serial measurements in a single patient. Also, biological variability of SCr is estimated to be approximately 4.5% in individuals with and without CKD (35). Taken together, for most US laboratories using enzymatic or Jaffe methods, a change in SCr from baseline by less than 20% is within the range of normal lab variation and is unlikely to represent significant change in glomerular filtration rate (GFR) (3). Conversely, in critically ill patients, elderly patients, and those with a rapid change in volume status, SCr may not increase by 0.3 mg/dL until significant decline in GFR has developed (6). Therefore, a recent report proposed a revised threshold of SCr change to diagnose AKI: an increase of SCr by 0.2 mg/dL or of 20% from baseline, whichever is higher (3). Further studies are needed to compare the performance of these proposed AKI criteria against the current KDIGO definition.

In addition to SCr, urine microscopy is often used in the evaluation of AKI. The presence of cellular casts, dysmorphic red blood cells, and certain crystals is highly informative in differentiating the etiology of AKI. In patients with suspected acute tubular injury, a validated scoring system that includes the number of casts and renal tubular epithelial cells has been shown to predict AKI severity (7). Automated systems have been increasingly incorporated into routine urinalysis to assess for the presence of casts and crystals. However, these systems are less likely to detect many important pathologic features, such as dysmorphic red blood cells, renal tubular epithelial cells, granular casts, and crystals, when compared to nephrologists' manual review (8). Therefore, while these automated systems do have diagnostic value, clinicians should not rely solely on them to diagnose diseases such as acute tubular injury, glomerulonephritis, and crystal nephropathy. Furthermore, the art of manual urine microscopy review is a valuable skill that should continue to be emphasized in nephrology fellowship training (9).

There are several common tests that, despite their widespread use, offer limited or no value in the diagnosis of AKI. Urine sodium and fractional excretion of sodium (FENa) are not useful in differentiating prerenal azotemia from intrinsic AKI, since the former can be easily diagnosed by assessing fluid responsiveness. This is because urine sodium and FENa can be low in diseases when the kidney is sodium avid and are influenced by dietary sodium intake. A study once commonly performed when interstitial nephritis was suspected was urine eosinophils. This study has been shown not to be useful in differentiating acute interstitial nephritis from other causes of AKI and thus has widely been abandoned (10).

Fortunately, many novel biomarkers are on the verge of clinical application to dissect the phenotype and prognosis of AKI and differentiate parenchymal kidney injury from hemodynamic changes. These biomarkers could be used to predict the progression of AKI and predict AKI to CKD transition and may help to guide AKI management, as delineated in our recent review (11). In the ASSESS-AKI (Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury) study, the increase in urine albumin and urine chitinase 3-like protein (YKL-40) and the decrease in uromodulin at 3 months after AKI hospitalization were found to be independently associated with developing CKD or CKD progression (12, 13). Insulin-like growth factor-binding protein-7 (IGFBP-7) and tissue inhibitor of metalloproteinase 2 (TIMP-2) have been found to predict AKI in critically ill patients, and intervention based on urinary TIMP-2 * IGFBP-7 > 0.3 after cardiac surgery was shown to reduce AKI incidence in one pilot trial (14, 15). Although urine albumin measurement is heading toward standardization, many biomarkers, including TIMP-2 and IGFBP-7, are measured by various commercially available immunoassay platforms (16). Establishing the proper reference interval, determining the analytical variability across measurement platforms, and understanding biological variability across patient populations by close collaboration with laboratorians are crucial for the effective clinical implementation of these novel biomarkers.

There have been many advances in our approaches to diagnose AKI since the KDIGO AKI definition was published in 2012 (Table 1). Clinicians, researchers, and laboratory scientists must continue to work together to fill in the remaining gaps in our understanding of these testing strategies.

tbl1

References

  • 1.

    Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 2012; 120: c179c184. doi: 10.1159/000339789

    • Search Google Scholar
    • Export Citation
  • 2.

    Lin J, et al. False-positive rate of AKI using consensus creatinine-based criteria. Clin J Am Soc Nephrol 2015; 10:17231731. doi: 10.2215/CJN.02430315

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    El-Khoury JM, et al. AACC guidance document on laboratory investigation of acute kidney injury. J Appl Lab Med [published online ahead of print May 11, 2021]. doi: 10.1093/jalm/jfab020; https://academic.oup.com/jalm/advance-article-abstract/doi/10.1093/jalm/jfab020/6272705?redirectedFrom=fulltext

    • Search Google Scholar
    • Export Citation
  • 4.

    European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database. http://biologicalvariation.eu/search?q¼creatinine

  • 5.

    Jones GRD. Estimates of within-subject biological variation derived from pathology databases: An approach to allow assessment of the effects of age, sex, time between sample collections, and analyte concentration on reference change values. Clin Chem 2019; 65: 579588. doi: 10.1373/clinchem.2018.290841

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Chronopoulos A, et al. Acute kidney injury in elderly intensive care patients: A review. Intensive Care Med 2010; 36:14541464. doi: 10.1007/s00134-010-1957-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Perazella MA, et al. Urine microscopy is associated with severity and worsening of acute kidney injury in hospitalized patients. Clin J Am Soc Nephrol 2010; 5:402408. doi:10.2215/CJN.06960909

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Cavanaugh C, Perazella MA. Urine sediment examination in the diagnosis and management of kidney disease: Core curriculum 2019. Am J Kidney Dis 2019; 73:258272. doi: 10.1053/j.ajkd.2018.07.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Shaikh S, Selzter J. The resurgence of urine microscopy. Kidney News 2021; 13(7):20. https://www.kidneynews.org/view/journals/kidney-news/13/7/article-p20_17.xml?tab_body=fulltex

    • Search Google Scholar
    • Export Citation
  • 10.

    Muriithi AK, et al. Utility of urine eosinophils in the diagnosis of acute interstitial nephritis. Clin J Am Soc Nephrol 2013; 8:18571862. doi: 10.2215/CJN.01330213

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Wen Y, Parikh CR. Current concepts and advances in bio-markers of acute kidney injury. Crit Rev Clin Lab Sci 2021; 58:354368. doi: 10.10800/10408363.2021.1879000

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Hsu C-Y, et al. Post-acute kidney injury proteinuria and subsequent kidney disease progression: The assessment, serial evaluation, and subsequent sequelae in acute kidney injury (ASSESS-AKI) Study. JAMA Intern Med 2020; 180:402410. doi: 10.1001/jamainternmed.2019.6390

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Puthumana J, et al. Biomarkers of inflammation and repair in kidney disease progression. J Clin Invest 2021; 131:e139927. doi: 10.1172/JCI139927 doi:10.1172/JCI139927

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Meersch M, et al. Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: The PrevAKI randomized controlled trial. Intensive Care Med 2017; 43:15511561. doi: 10.1007/s00134-016-4670-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Kashani K, et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care 2013; 17:R25. doi: 10.1186/cc12503

  • 16.

    Seegmiller JC, et al. Moving toward standardization of urine albumin measurements. EJIFCC 2017; 28:258267. http://www.ifcc.org/media/476804/ejifcc2017vol-28no4pp258-267.pdf

    • Search Google Scholar
    • Export Citation
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