Proteomic identification of early biomarkers of acute kidney injury after cardiac surgery in children

Abstract

Background: Serum creatinine is a delayed marker of acute kidney injury (AKI). Our purpose is to discover and validate novel early urinary biomarkers of AKI after cardiac surgery.

Study design: Diagnostic test study.

Setting & participants: Children undergoing cardiopulmonary bypass surgery. The test set included 15 participants with AKI and 15 matched controls (median age, 1.5 year) of 45 participants without AKI. The validation set included 365 children (median age, 1.9 year).

Index tests: Biomarkers identified using proteomic profiling: α(1)-microglobulin, α(1)-acid glycoprotein, and albumin.

Reference test: AKI, defined as ≥50% increase in serum creatinine level from baseline within 3 days of surgery.

Results: Proteomic profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) showed 3 protein peaks that appeared consistently within 2 hours in children who developed AKI after cardiopulmonary bypass surgery. The proteins were identified as α(1)-microglobulin, α(1)-acid glycoprotein, and albumin. Using clinical assays, results were confirmed in a test set and validated in an independent prospective cohort. In the validation set, 135 (37%) developed AKI, in whom there was a progressive increase in urinary biomarker concentrations with severity of AKI. Areas under the curve for urinary α(1)-microglobulin, α(1)-acid glycoprotein, and albumin at 6 hours after cardiac surgery were 0.84 (95% CI, 0.79-0.89), 0.87 (95% CI, 0.83-0.91), and 0.76 (95% CI, 0.71-0.81), respectively. Participants with increasing quartiles of biomarkers showed increasing lengths of hospital stays and durations of AKI (P < 0.001).

Limitations: Single-center study of children with normal kidney function at recruitment. The SELDI-TOF MS technique has limited sensitivity for the detection of proteins greater than the 20-kDa range.

Conclusions: Urinary α(1)-microglobulin, α(1)-acid glycoprotein, and albumin represent early, accurate, inexpensive, and widely available biomarkers of AKI after cardiac surgery. They also offer prognostic information about the duration of AKI and length of hospitalization after cardiac surgery.

Figure 1

Trends over time of the three biomarkers by severity of AKI after cardiopulmonary bypass. Values represent means±SEM for biomarker levels at each time-point. X axis represents various time-points in the validation set. Post-operative concentrations of all three biomarkers were significantly higher at all time-points between the four groups of severity of AKI (P<0.001). 1a: AIM (α₁-microglobulin), 1b: AAG (α₁-acid glycoprotein), 1c: Alb (albumin).

Figure 1

Trends over time of the three biomarkers by severity of AKI after cardiopulmonary bypass. Values represent means±SEM for biomarker levels at each time-point. X axis represents various time-points in the validation set. Post-operative concentrations of all three biomarkers were significantly higher at all time-points between the four groups of severity of AKI (P<0.001). 1a: AIM (α₁-microglobulin), 1b: AAG (α₁-acid glycoprotein), 1c: Alb (albumin).

Figure 1

Trends over time of the three biomarkers by severity of AKI after cardiopulmonary bypass. Values represent means±SEM for biomarker levels at each time-point. X axis represents various time-points in the validation set. Post-operative concentrations of all three biomarkers were significantly higher at all time-points between the four groups of severity of AKI (P<0.001). 1a: AIM (α₁-microglobulin), 1b: AAG (α₁-acid glycoprotein), 1c: Alb (albumin).

Figure 2

Receiver-operating characteristic curves showing diagnostic accuracy of the novel biomarkers for the prediction of AKI when measured at 6 hours post-CPB. AIM, α₁-microglobulin; AAG, α₁-acid glycoprotein; Alb, albumin.

Figure 3

Association of quartiles of biomarker levels with severity of clinical outcomes. All the associations of biomarkers with outcomes are significant at P<0.005. 3a, association of biomarkers with increase in serum creatinine; 3b, association of biomarkers with length of hospital stay.

Figure 3

Association of quartiles of biomarker levels with severity of clinical outcomes. All the associations of biomarkers with outcomes are significant at P<0.005. 3a, association of biomarkers with increase in serum creatinine; 3b, association of biomarkers with length of hospital stay.

Figure 4

Conditional probability of the three biomarkers in the setting of AKI after cardiopulmonary bypass. Initial assessment of the likelihood of disease (pre-test probability) was estimated, a test performed to shift suspicion one way or the other, and a final assessment of disease likelihood determined (post-test probability). AIM, α₁-microglobulin; AAG, α₁-acid glycoprotein; Alb, albumin. The lines in the graphs represent the likelihood ratios (LR) for each of the tests at their optimal cut-offs, as an estimate of how much one should shift clinical suspicion for a particular test result. Since tests can be positive or negative, there are at least two LRs for each test. In Figure 4a, the positive LR estimates the increase in disease probability if the test is positive. In Figure 4b, the negative LR estimates the decrease in disease probability if the test is negative. There are several scenarios when the pre-test probability of AKI is between 30–70% where the post-test probability of AKI is dramatically changed based on the biomarker test result.

Figure 4

Conditional probability of the three biomarkers in the setting of AKI after cardiopulmonary bypass. Initial assessment of the likelihood of disease (pre-test probability) was estimated, a test performed to shift suspicion one way or the other, and a final assessment of disease likelihood determined (post-test probability). AIM, α₁-microglobulin; AAG, α₁-acid glycoprotein; Alb, albumin. The lines in the graphs represent the likelihood ratios (LR) for each of the tests at their optimal cut-offs, as an estimate of how much one should shift clinical suspicion for a particular test result. Since tests can be positive or negative, there are at least two LRs for each test. In Figure 4a, the positive LR estimates the increase in disease probability if the test is positive. In Figure 4b, the negative LR estimates the decrease in disease probability if the test is negative. There are several scenarios when the pre-test probability of AKI is between 30–70% where the post-test probability of AKI is dramatically changed based on the biomarker test result.