Kidney biomarkers and differential diagnosis of patients with cirrhosis and acute kidney injury

Abstract

Acute kidney injury (AKI) is common in patients with cirrhosis and associated with significant mortality. The most common etiologies of AKI in this setting are prerenal azotemia (PRA), acute tubular necrosis (ATN), and hepatorenal syndrome (HRS). Accurately distinguishing the etiology of AKI is critical, as treatments differ markedly. However, establishing an accurate differential diagnosis is extremely challenging. Urinary biomarkers of kidney injury distinguish structural from functional causes of AKI and may facilitate more accurate and rapid diagnoses. We conducted a multicenter, prospective cohort study of patients with cirrhosis and AKI assessing multiple biomarkers for differential diagnosis of clinically adjudicated AKI. Patients (n = 36) whose creatinine returned to within 25% of their baseline within 48 hours were diagnosed with PRA. In addition, 76 patients with progressive AKI were diagnosed by way of blinded retrospective adjudication. Of these progressors, 39 (53%) patients were diagnosed with ATN, 19 (26%) with PRA, and 16 (22%) with HRS. Median values for neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1), liver-type fatty acid binding protein (L-FABP), and albumin differed between etiologies and were significantly higher in patients adjudicated with ATN. The fractional excretion of sodium (FENa) was lowest in patients with HRS, 0.10%, but did not differ between those with PRA, 0.27%, or ATN, 0.31%, P = 0.54. The likelihood of being diagnosed with ATN increased step-wise with the number of biomarkers above optimal diagnostic cutoffs.

Conclusion: Urinary biomarkers of kidney injury are elevated in patients with cirrhosis and AKI due to ATN. Incorporating biomarkers into clinical decision making has the potential to more accurately guide treatment by establishing which patients have structural injury underlying their AKI. Further research is required to document biomarkers specific to HRS.

Figure 1. Process for Determination of Differential Diagnosis

The process by which patients with cirrhosis and AKI had the etiology of their AKI determined. *7 patients who progressed were enrolled during the pilot phase of the study and had incomplete data collection. These patients were excluded from adjudication to avoid information bias. In addition, 2 patients who did not have 2/3 adjudicator diagnostic agreement were excluded. Of the remaining 74, 3/3 adjudicators agreed for 37 patients and 2/3 for 37 patients. **Of the non-progressors with rapid recovery who were assigned a diagnosis of PRA, 6 (17%) were additionally adjudicated and all 6/6 were adjudicated as having PRA.

Figure 2

a. Biomarker Values for Patients With and Without ATN Biomarker values are shown for patients with and without ATN. Dark horizontal lines represent medians while the shaded boxes represent interquartile ranges. Biomarkers values are statistically significantly higher in patients with ATN for all biomarkers. b. FENa and Albumin for Patients with PRA, HRS and ATN FENa and albumin values are shown for patients with PRA, HRS and ATN. Dark horizontal lines represent medians while the shaded boxes represent interquartile ranges. FENa is statistically significantly lower in patients with HRS as compared to both PRA and ATN while albumin is significantly higher in patients with ATN than in those with either PRA or HRS.

Figure 2

a. Biomarker Values for Patients With and Without ATN Biomarker values are shown for patients with and without ATN. Dark horizontal lines represent medians while the shaded boxes represent interquartile ranges. Biomarkers values are statistically significantly higher in patients with ATN for all biomarkers. b. FENa and Albumin for Patients with PRA, HRS and ATN FENa and albumin values are shown for patients with PRA, HRS and ATN. Dark horizontal lines represent medians while the shaded boxes represent interquartile ranges. FENa is statistically significantly lower in patients with HRS as compared to both PRA and ATN while albumin is significantly higher in patients with ATN than in those with either PRA or HRS.

Figure 3. Graph of Conditional Probabilities For Urine Biomarkers

Abbreviations: NGAL, neutrophil gelatinase-associated lipocalin; ILK-18, interleukin-18; FENa, fractional excretion of sodium; ATN, acute tubular necrosis Figure depicts the conditional probabilities for the diagnosis of ATN utilizing biomarkers at their optimal cutoff. For each pre-test probability, a post-test probability is calculated utilizing a positive (NGAL, IL-18, albumin) or negative (FENa) likelihood ratio. Formula: Likelihood ratio− = (1-sensitivity)/specificity; Likelihood ratio+ = sensitivity/(1-specificity); pretest odds = pretest probability/(1-pretest probability); posttest odds = pretest odds×LR; posttest probability = posttest odds/(posttest odds + 1)

Figure 4. Association Between Biomarker Elevation and Diagnosis

The percentage of patients with pre-renal azotemia (PRA), hepatorenal syndrome (HRS) and acute tubular necrosis (ATN) by the number of biomarkers of structural injury above their optimal cutoff for the diagnosis of ATN.