and TIMP-2
IGFBP-7 and TIMP-2 are proteins that are implicated in G1 cell cycle arrest as a response to renal insult and unlike other candidate molecules do not exhibit non-specific elevations [45, 46]. In postoperative patients a pre-planned post hoc analysis from both trials including cardiothoracic and emergency surgery demonstrated an AUROC of 0.84 in terms of predicting severe AKI within a 12-h window. Subsequent studies in cardiothoracic patients demonstrated interesting kinetics of these biomarkers of renal stress. Levels of TIMP-2/IGFBP-7 were significantly increased on the first postoperative day (AUROC 0.7), but on the day of surgery, the values were significantly lower [47].
A subsequent study on 107 major general surgical, non-cardiac patients (i.e. trauma, hepatobiliary and vascular) performed postoperatively on the day of surgery delivered an AUROC of 0.85 with high TIMP-2/IGFBP-7 levels being associated with an increased risk of requiring renal replacement therapy and overall mortality [48].
Subclinical AKI: Dysfunction or Injury?
Clearly there is now a significant body of literature that has investigated AKI biomarkers in a variety of clinical settings and in diverse patient groups. However, there remains an almost slavish addiction to highlighting the performance of AKI biomarkers to predict AKI defined by the classical parameters. Although this is clearly of interest, one must also question what message these biomarkers are telling us if any. Figure 2 highlights some theoretical scenarios where a biomarker associated with AKI may inform us above and beyond that of the SCr. For example, in a previously healthy individual with “normal” renal function, a significant tubular injury may occur undetected if one relies solely on the SCr as a marker of AKI, whereas employing urine output criteria in isolation may result in misclassification of AKI. Under both conditions, a biomarker of AKI may inform beyond that of the SCr and urine output particularly where renal dysfunction rather than true damage has occurred. Therefore, AKI biomarkers may allow a diagnosis of AKI to be made earlier but also indicate where renal injury has occurred even in the absence of subsequent dysfunction [49]. This concept of sub-clinical or silent AKI implies that although AKI may not clinically manifest, it has occurred but has escaped detection by conventional means. This requires a shift in approach from a predictive role of the AKI biomarker to that of a diagnostic or indeed prognostic tool. Coupled with this would be the acceptance that acknowledging such “biomarker positivity” or subclinical (silent) AKI would reflect on the epidemiology of AKI as well as affect the management of patients and perhaps result in better outcomes. However, this approach implies that the presence of a positive biomarker may result in poorer outcomes. Indeed there is evidence that this is the case. For example, a pooled analysis on over 2,000 patients with cardiorenal syndrome examined both plasma and urinary NGAL. Patients were classified as NGAL+ve or NGAL–ve according to the study specific cut-offs for AKI prediction as well as SCr+ve or SCr–ve according to conventional AKI criteria [50]. Over 19% of study patients were NGAL+ve/SCr–ve and this group had a higher observed in-hospital mortality when compared to NGAL–ve/SCr+ve (12.4 vs. 8.4%). In all groups, there were incremental increased risks associated with biomarker positivity including length of both ICU and hospital stay and requirement for renal replacement therapy. Interestingly in this cohort, both plasma and urinary NGAL conferred an increased risk. Whatever the mechanism, there is a clear signal that the presence of markers associated with renal injury confer an increased risk for relevant clinical endpoints and as such should not be ignored. With the future increase in the number of potential AKI biomarkers, it may be that a combination will provide much more timely information regarding pathophysiology and outcomes rather than simply predicting a rise in conventional markers of renal function.
Fig. 2. Subclinical acute kidney injury (AKI). Three scenarios are depicted, which may highlight the clinical use of AKI biomarkers over and above the prediction of AKI stage. Subject A has normal renal function with a significant renal reserve. Despite a fall in glomerular filtration rate (GFR) by about 40%, the rise in serum creatinine (SCr) does not reach the diagnostic threshold for AKI, but the subject has a positive biomarker in keeping with significant tubular damage and subclinical AKI. In case B, there is a much smaller fall in GFR (and significantly reduced renal reserve) and following insult a small fall in GFR translates to AKI stage 1. Under certain circumstances, the biomarker may be negative such as following administration of drugs that interfere with creatinine tubular secretion. Case C highlights a scenario where the urine output criteria for AKI are satisfied, but the biomarker is not elevated. This may represent physiological oliguria following major surgery, for example.
Can Biomarkers Be Used to Guide Therapy?
Ultimately the desired aim of integrating AKI biomarkers into clinical practice is to improve clinical outcomes with one of the driving forces being early recognition and hence, timely intervention. However, a potential application of AKI biomarkers may be the enrichment of patient selection in clinical trials. Such integration may reduce misclassification of AKI and allow more accurate diagnosis improving statistical power in tandem with reducing sample size. Application of prognostic biomarkers may enhance the enrollment of patients likely to successfully meet trial end points enabling the use of specific therapies particularly as they may help identify a cohort at higher risk of developing AKI, for example. This is of particular value. Of late, such trial enrichment has indeed been employed with success. The PrevAKI trial examined the implementation of a care bundle in cardiac surgery patients deemed at high risk for AKI. Of 1,046 patients identified, 882 were screened of which 111 were unsuitable. This left a cohort of 771 patients, 276 of whom were randomized following risk stratification with a biomarker (TIMP-2/IGFBP-7) [51]. The high-risk group were defined by TIMP-2.IGFBP-7 >0.3 with a primary endpoint defined as the rate of AKI as defined by KDIGO criteria within 72 h of surgery. AKI was significantly reduced with the intervention compared to controls (55.1 vs. 71.7%; [95% CI 5.5–27.9]; p = 0.004). The implementation of the bundle resulted in significantly improved hemodynamic parameters at different time points (p < 0.05), less hyperglycemia (p < 0.001) and use of ACEi/ARBs (p < 0.001) compared to similar parameters in controls. Rates of moderate to severe AKI were also significantly reduced by the intervention compared to controls, although in over 80% of cases AKI was defined by changes in urine output alone rather than SCr criteria or both, which accounted for about 5%. However, no effects were seen on 30-day outcomes including mortality. Certainly if this biomarker approach were not employed in this study, then a far greater sample size would have been needed to achieve the desired statistical power and this may have made the approach untenable.
Even more recently, a similar study in major non-cardiac surgery again demonstrated that biomarker-triggered KDIGO care bundle implementation translated into improved outcomes. Subgroup analysis demonstrated a significantly reduced incidence of AKI after major abdominal surgery together with reduced length of stay [52]. Although these are small single-centre studies, they may hold great promise in terms of risk stratification and directing therapy to those individuals at risk of AKI but, importantly those who exhibit a degree of renal stress. It may be that the benefits are not seen immediately but may be reflected in the long-term reduction in CKD and the inherent risks associated with the development of chronic kidney disease.
Conclusions
