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1 Introduction

Surgeons, anesthesiologists, intensivists, radiologists, interventional cardiologists, and nephrologists, among others, are keenly interested in preserving renal function in patients undergoing surgical interventions or other procedures, as well as in intensive care unit (ICU) patients. The well-known strong association between acute kidney injury (AKI) and its sequel, chronic kidney disease (CKD) with mortality and with severe cardiac and other organ morbidity [1–5] makes practitioners even more mindful of kidney function in these patients. No effective new therapy for AKI has been introduced so far; thus better avenues for progress may be novel diagnostic tests and a clearer understanding of the factors associated with the development of AKI in both surgical and critically ill patients and how to prevent it.

Around 2000, the lack of novel pharmacologic strategies for AKI therapy seemed to awaken a critical mass of epidemiologists and nephrologists: worldwide a reassessment of the most fundamental questions about AKI was spurred, and nephrology literature from 2004 onward was eventually unfolded.

The first most urgent questions were related on AKI definition, how best AKI could be classified, what is its etiology, and how best to prevent it. If indeed prevention is the only way of reducing the burden of AKI and of its sequelae (outside of renal replacement therapy [RRT]), then clarifying definition was the obligatory first step.

2 The Evolution of AKI Definition

The lack of uniformity in naming and defining AKI has been a serious impediment to progress in the field’s epidemiology [6]. From the standpoint of nomenclature, the older term “acute renal failure” (ARF) was predominant until 2005 when the term AKI emerged. The term ARF is now obsolete as an acronym in medicine and nephrology.

The significance of this change in nomenclature was felt by many in the nephrology community to be of great, even revolutionary importance because generally the older references in the nephrology and critical care literature had often defined ARF less precisely than the newer term AKI would be defined. For example, in a 1999 review Nissenson defined ARF in the critical care setting as “the abrupt decline in glomerular filtration rate (GFR) resulting from ischemic or toxic injury to the kidney” [7]. Some authors defined ARF as azotemia with or without oliguria. Other authors had recorded increases in blood urea nitrogen (BUN) to diagnose ARF and omitted serum creatinine (sCr) measurements. In others, the timing of sCr or BUN samples was incompletely documented. Some authors noted rehydration as a precondition for diagnosing ARF, while others did not specify the presence or absence of rehydration as a part of this definition. In the seminal critical care paper in which the first exact definition of AKI was introduced, Bellomo et al. [8] noted that some 30 definitions of ARF had hitherto been used at different times in the literature.

From 2002 onward, three different consensus definitions, from three different workgroups, have emerged and become accepted, and the reader needs to be aware of the differences between them when comparing studies. No single consensus definition has yet emerged as the standard definition, but the use of KDIGO definition [9] (see below) is currently recommended for epidemiologic and research purposes.

2.1 The ADQI Workgroup Was Formed to Address a Lack of Consensus Over How Best to Treat AKI with RRT: Eventually, the Group Produced RIFLE, an Acronym Defining AKI by Its Severity in Stages

The Acute Dialysis Quality Initiative (ADQI) [8, 10] Workgroup was founded in 2000 by representatives from the US National Institutes of Health (NIH), American Society of Nephrology (ASN), and the Society of Critical Care Medicine (SCCM), among others. In 2004, its founding members identified a definition and classification system for AKI. It employed the mnemonic acronym RIFLE (for “risk,” “injury,” “failure,” “loss” of renal function, and “end-stage” kidney disease). The various levels of AKI were defined according to azotemia (serum creatinine) and urinary output (UO) criteria (Table 2.1) [8]. Note that the most severe criteria in either the azotemia or oliguria columns should be applied when assigning a RIFLE stratum: i.e., one should use whichever criterion that assigns the most severe class of AKI.

Table 2.1 The acute dialysis quality initiative (ADQI) workgroup criteria and classification for AKI
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2.2 The AKIN Diagnostic and Staging Criteria for AKI Emphasize Azotemia

The members of the Acute Kidney Injury Network (AKIN) first met in 2005 and proposed a diagnostic criterion for AKI [11] (see Table 2.2) in order to improve some of RIFLE drawbacks. The AKIN workgroup classified AKI into three degrees of severity called stages 1, 2, and 3 (Table 2.3). Note that, as the AQDI definition did, these resemble the “R,” “I,” and “F” strata, which also take into account creatinine increase over baseline as well as oliguria. The AKIN guideline also stipulates adequate fluid resuscitation prior to diagnosis of AKI.

Table 2.2 AKIN diagnostic criteria for AKI
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Table 2.3 Staging of AKI according to AKIN
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2.3 The KDIGO Defines AKI Using Similar Azotemia and Oliguria Criteria and Includes a GFR Criterion for Patients Younger than 18 Years of Age

In 2003, the Kidney Disease: Improving Global Outcomes (KDIGO) was formed with the aim of implementing clinical practice guidelines for patients with kidney disease. In March 2012, KDIGO published its far-ranging guidelines for the evaluation and management of AKI (Table 2.4) [9].

Table 2.4 Diagnosis and staging of AKI according to the KDIGO workgroup
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2.4 The US National Kidney Foundation and Others Weigh in on These Three Definitions

A study group of the US National Kidney Foundation, called the NKF-KDOQI (National Kidney Foundation—Kidney Disease Quality Outcome Initiative), reported mixed sentiments about the KDIGO guidelines [12]. The initiative was a group of renal specialists who generally applauded the melding of ADQI, AKIN and KDIGO AKI definitions but was less enthusiastic about the recommendations for AKI management proposed in the KDIGO guidelines. The KDOQI’s concern was that many of the management recommendations, though sensible or at least plausible as first-approaches, were unsubstantiated by well-powered controlled clinical studies [12]. Likewise, the Canadian Society of Nephrology (CSN) [13] and the European Renal Best Practices (ERBP) society [14] were hesitant to embrace the KDIGO guideline. By way of the struggle to define and clarify the definition of AKI, and to take the first steps to make the treatment of AKI more evidence-based, much information about the incidence and progression of AKI has been brought to light, even in the absence of any radically new science.

2.5 Summary of the Definitions of AKI

The importance of recounting these steps in the evolving definition of AKI is twofold: first, comparing research papers about AKI requires some understanding of the differences between the RIFLE, AKIN, and KDIGO definitions, since they vary in their respective criteria of azotemia, oliguria, estimated glomerular filtration rate (eGFR), and time intervals over which AKI must occur. Secondly, they have different names for each stage of severity. There is yet no consensus on which definition is predominant. The RIFLE acronym [8] is popular in the literature and in medical records, but the KDIGO definition [9, 12] implies future screening and initial management recommendations and is likewise popular. Any of these classifications can be utilized to stratify AKI severity and are used to report incidence and outcome. So far, no one has yet identified a better serum marker than creatinine or better functional criteria than oliguria and GFR to characterize AKI. All three are used one way or another in these three workgroup definitions, for classifying AKI. They are likely all robust and close enough to be reliably used presently.

3 The Incidence of AKI

Table 2.5 provides a summary of some relevant publications addressing the incidence of AKI among postoperative and medical inpatients. Various risk factors associated with AKI are also briefly summarized.

Table 2.5 Some studies describing the incidence and risk factors associated with acute kidney injury (AKI)
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It is clear from these studies that elective adult patients undergoing planned, especially noncardiac procedures have a lower incidence of AKI as compared to more severely ill categories of patients [14–22]. For example, AKI has been reported to occur in 0.8 % of patients undergoing low-risk surgeries [16], in 1.82 % of patients undergoing orthopedic procedures (shoulder, hip, and knee) [14], and in 7.4 % of patients undergoing any noncardiac intervention [15], while the incidence of AKI is much higher in patients undergoing high-risk (7.5 %) [17] or urgent/emergent surgical procedures (hazard ratio for AKI 1.9 [20]) [15, 16, 18], in ischemic and congestive heart failure patients (hazard ratio for AKI 2.0 [17]), in survivors after cardiac arrest (43 %) [19], in patients admitted to ICU for sepsis (10–20 %) [20, 21], and in both elective or emergent high-risk vascular surgery patients (48 %) [22]. The greatest risk of AKI is borne by those with preexisting CKD which is tenfold over the risk of patients who do not have a diagnosis of CKD [23].

4 Improving the Diagnosis of AKI: From Creatinine Clearance to the New Biomarkers

Practical assessment of day-to-day kidney function in patients is done implicitly, with simple measurement of UO and sCr, comparing it with a baseline (premorbid) value. According to many authors, however, the benchmark or “gold standard” for measuring renal function is the GFR [24], defined as the amount of blood filtrate per minute emerging from the glomeruli into the proximal tubule lumen, for both kidneys. The practicality of obtaining GFR remains controversial, yet some authors have addressed the complex issue of using serum creatinine as a proxy for actual GFR measurements [25]. Endre et al. [25] noted that the two measurements are not the same, of course, and argued that AKI definitions might do well to avoid GFR criteria. However, they suggested that the estimation of GFR with shorter collection times (e.g., 2–4 h) might indeed be practical and make actual GFR, in association with biomarkers of renal injury, sensitive and feasible on a daily basis. Discussion here will merely address that acceptance of spot sCr and the use of eGFR equations like Cockroft-Gault and MDRD are the nearly universally accepted means of estimating GFR.

Normal GFR, in the absence of CKD, is defined as greater than or equal to 90 mL/min 1.73 m2 of body surface area (BSA). If CKD has been diagnosed, a patient with a GFR ≥90 mL/min 1.73 m2 would be said to have KDIGO CKD stage G1. A GFR between 60 and 89 mL/min 1.73 m2 is said to be mildly decreased (KDIGO stage G2 CKD). Note that this pertains to CKD, not AKI.

4.1 The Most Promising Novel Biomarkers of AKI: uAlb/uCr, CysC, NGAL, IL-18, and KIM-1

Though well accepted as a noninvasive marker of GFR, sCr has limitations. It is known to vary with muscle mass, age, gender, liver function, and nonrenal gastrointestinal elimination [26]. Its measurement may be also confounded by exogenous creatinine ingestion. Most importantly, it is well known that sCr is a late indicator of kidney injury [27–29] and that also the reduction in sCr lags as an indicator of improvement in renal function [30]. Moreover, hemodilution may cause a reduction in sCr indicating falsely an improvement in renal function. Finally, its production is decreased in sepsis, unfortunately, just when its use as a marker of AKI makes it a focus of clinical attention [31].

As the need arises to identify AKI earlier and more sensitively than serum creatinine, other biomarkers have been proposed [32]. Table 2.6 shows some features of recently studied biomarkers, including the overall quality of the indicator (i.e., its sensitivity and specificity) as quantitated by its receiver-operator characteristic (ROC) area under the curve (AUC) [33–35]. An AUC value which approaches 1.0 indicates high sensitivity and specificity.

Table 2.6 Some recent investigations describing novel diagnostic biomarkers of AKI
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Five of these new biomarkers are among the most promising and will be discussed briefly: urine albumin/creatinine ratio (uAlb/uCr), cystatin-C (CysC), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), and kidney injury molecule-1 (KIM-1) [36].

Some authors have merely reexamined the sensitivity and specificity of urine albumin in conjunction with urine creatinine in an attempt to increase the sensitivity and specificity of the two markers, already available in most routine clinical lab panels. Tziakas et al. [29] found the ratio of urinary albumin to creatinine (uAlb/uCr) to have a significant predictive value for AKI with an AUC of 0.725, superior to some more modern biomarkers under investigation. Others reported the use of albumin-creatinine ratio as a biomarker of increased risk for cardiovascular morbidity and mortality and all-cause mortality [37].

CysC is a post-gamma-globulin protein first described in 1984 [38]. It belongs to a large class of cysteine proteinase inhibitors. These inhibitors are found in all tissues and bodily fluids, and the enzymes which they inhibit are normally stored in lysozymes produced primarily by nucleated cells throughout the body. It is a small (13 kDa), nonglycosylated, basic protein consisting of 120-amino acid residues [39].

Recent evidence suggests that CysC may be as useful as creatinine or, more so, as a marker for glomerular filtration and AKI. For purposes of assessing renal function, CysC is useful due to its low molecular weight, electrostatic (charge) characteristics, and physical stability: all of these make it easily filtered by the glomerulus. Moreover, its serum concentration is independent of gender, age, or muscle mass, all confounding factors when using creatinine to assess GFR. CysC or the gene coding for it (CST3) has also been studied as a biomarker for coronary artery disease [40], congestive heart failure (CHF) [41], squamous cell carcinoma of the head and neck [39], Alzheimer’s disease [42, 43], and age-related macular degeneration [43]. This is relevant because the assay for CysC may become more widely used and less expensive and possibly included in clinical laboratory panels in the future.

NGAL, also known as human neutrophil lipocalin (HNL), lipocalin 2, siderocalin, or 24p3, is a small, 25 kDa monomer peptide or a 45 kDa dimer peptide [37]. It is linked covalently with gelatinase (matrix metalloproteinase 9, MMP-9). Its function is thought to be as a modulator of early inflammation, where it is thought to inhibit bacterial growth, scavenge iron and induce epithelial growth. Plasma NGAL is freely filtered by the glomerulus and then largely reabsorbed by proximal tubular cells. More importantly though, upon renal tubular injury NGAL reabsorption is decreased and NGAL synthesis in epithelial cells of the loop of Henle and of distal tubule segments is strongly upregulated. This makes it an early, sensitive indicator of kidney injury of many etiologies, including diabetic nephropathy [44], ureteral obstruction, nephrotic syndrome and interstitial nephritis, as shown in a variety of animal models and in human disease [45]. It is possible that NGAL might be developed into an early-responding biomarker. In an interesting head-to-head prospective observational study comparing NGAL, CysC, creatinine, and other markers, Ralib et al. [46] measured levels of all these biomarkers beginning at presentation in the emergency room (ER). The study was performed on a small (n = 77) cohort of patients admitted to the ER with conditions likely to result in AKI (hypotension, ruptured abdominal aortic aneurysm, etc.) and who were followed at very short intervals: 0, 4, 8, and 16 h and 2, 4, and 7 days in the ICU. Of all the biomarkers, only plasma NGAL diagnosed AKI correctly at all time points, including at presentation, and urinary NGAL was best at predicting the composite outcome of mortality or dialysis. Among the sea of candidate biomarkers NGAL merits following as other investigators study it.

IL-18 is a 24 kDa, nonglycosylated polypeptide member of the IL-1β interleukin superfamily of inflammatory cytokines [47]. Its precursor is produced in mononuclear cells in the blood and processed by caspase and then IL-18 is secreted outside the cell to assist in innate and acquired immune responses. This is done by inducing IFN-γ production from T lymphocytes and macrophages and by enhancing cytotoxicity of natural killer [42]. IL-18 is also produced in most endothelial cells of the gastrointestinal tract and kidney (tubular epithelial cells, mesangial cells, and podocytes) [48], thus its potential value as a marker of AKI.

KIM-1 is a larger molecule, a 104 kDa type I transmembrane glycoprotein that contains both an immunoglobulin-like domain and a mucin domain in its extracellular portion [49, 50]. It is expressed at baseline in low levels in healthy proximal tubule cells in the kidney. It is thought to promote apoptotic clearance after ischemia and reperfusion injury of the kidney [49]. Indeed, after kidney ischemia or toxicity, KIM-1 is highly upregulated and released into the extracellular space and urine [49–51], where it is a putative marker of kidney injury.

All these biomarkers have acceptable but not outstanding sensitivities and specificities (AUC values) when used alone (see Table 2.6). An early trend in the literature is of combining two or more biomarkers to increase the composite AUC and thus the overall diagnostic strength of the test [52]. Indeed, a 2014 review of 32 different urine biomarkers, used to predict the progression of acute kidney injury following cardiac surgery, showed that the most sensitive and specific (thus greatest AUC) biomarker was the combination of IL-18 and KIM-1. They had an AUC of 0.93 in predicting an AKIN 3 (RIFLE “F”) stage or death [32].

Which of these new biomarkers will enter into common use (in addition to sCr, which is already widely accepted and embedded in several versions of eGFR equations and should be probably preserved as the standard)? The answer will be determined by the following factors: (1) the biomarker must be excellent in terms of sensitivity and specificity (as measured by AUC) alone or in combination with other biomarkers; (2) it must be fast, leading, not lagging, as a marker (of both onset and recovery of AKI); (3) it must be inexpensive with regard to time, convenience of sampling, labor, ingredients, and assay complexity; (4) it must be accepted by the medical community, the workgroups, and the payers; in other words it must be an acknowledged improvement over the eGFR status quo using sCr; and (5) it must be suitable to health institutions by appearing in an eGFR equation like MDRD; therefore, (6) according to the National Institute of Health (NIH) [53] any candidate biomarker value must be inserted into a so-called IDMS-traceable eGFR equation. An isotope dilution mass spectrometry (IDMS)-traceable equation is an eGFR equation (e.g., MDRD) that is “traceable to” or calibrated by IDMS, an extremely precise means of quantitating GFR. In other words, any eGFR equation must essentially be grounded in creatinine assays that are super-accurate, by way of IDMS calibration.

A detailed discussion of this issue is beyond the scope of this chapter but it is treated exhaustively by Myers et al. [54].

5 Outcome Following AKI

As mentioned, a number of published studies (summarized in Table 2.5) addressed the incidence of AKI in various clinical settings, e.g., total joint arthroplasty in elective patients [14], ICU patients [20], cardiology patients monitored for hypotension in the ICU [16], patients with intraoperative hypotension [15], noncardiac general surgery patients with preexisting normal kidney function [18], patients with sepsis or diabetes or both [20, 55, 56], patients resuscitated from cardiac arrest [19], high-risk vascular surgery patients [22], etc. Several authors were able to incorporate long-term outcomes (primarily mortality) in their surveys of AKI patients. Table 2.7 summarizes some of the more widely known studies in which outcome following AKI was examined.

Table 2.7 Some recent investigations reporting AKI outcomes
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Overall, patients experiencing AKI after surgery have significant increases in mortality. In a very large study including 65,043 patients undergoing major noncardiac surgery, an eightfold increase in 30-day mortality was reported in those who developed postoperative AKI [16]. AKI markedly increases mortality also in ICU patients. Several studies show a clear correlation between the degree of AKI (according to the AKIN and RIFLE criteria) and mortality [57, 58]. In a large retrospective study of 22,303 patients from 22 ICUs, Osterman et al. [57] found a mortality of 10.7 % in patients without AKI, of 20.1 % (odds ratio [OR] 2.59) in those with AKIN stage 1 (RIFLE “R”) AKI, of 25.9 % (OR 3.24) in those with stage 2 (“I”) AKI, and of 49.6 % (OR 9.38) in those with stage 3 (“F”) AKI.

However, an independent association of the various stages of AKI with ICU mortality is harder to demonstrate. In the study by Osterman et al. [57], only AKI stage 3 was independently associated with increased ICU mortality. Stage 2 AKI was not independently associated with increased ICU mortality. Surprisingly, stage 1 AKI and RRT were independently associated with reduced ICU mortality. The authors acknowledged that because AKIN criteria allowed including all patients on RRT as AKI stage 3, and because some 583 persons began to receive RRT before their AKI had actually progressed to AKI stage 3, the picture may be confused.

The 6-month outcomes of surviving AKI patients in a large Finnish study using the KDIGO AKI definition have been recently reported [59]. Among 933 patients studied, 224 patients (35.3 %) with AKI died within 6 months, as compared with 154 (16.5 %) patients without AKI. Surviving AKI patients had lower quantitative quality-of-life indices 6 months later, as opposed to those who did not have AKI. Surprisingly though, their self-reported assessments of well-being were equivalent to survivors without AKI.

6 Summary and Discussion

The reexamination of AKI from a standpoint of its definition, classification, and diagnosis began around 2000 when the first definitions of AKI were propounded.

Paired with improvements in the definition of AKI was the problem of how to diagnose it. The traditional, “gold standard” methods (clearances of various inert compounds such as phenol red and inulin) had long ago evolved to more practical spot assays of serum creatinine and albumin. The problems with creatinine are, however, that it is a late (24–48 h), indirect indicator of kidney injury [27, 28], and that its production times are impaired in sepsis (a high-risk condition for the kidney) [60] and they also decrease in cachexia or extremes of age.

From this conundrum came a new starting point. Better understanding of AKI has led to discrimination between the various mechanisms of kidney injury. Apart from preexisting CKD [2, 23], sepsis is the most powerful risk factor in developing AKI [20, 56, 61, 62]. As a rule, AKI will develop predictably in about 19 % of patients with “moderate” sepsis (fever or hypothermia with infection, tachycardia, tachypnea, and leukocytosis), 23 % of patients with severe sepsis (the above plus lactatemia, oliguria, or mental status changes), and 51 % of patients with septic shock (all the above plus systolic blood pressure less than 90 mmHg after fluid resuscitation) when blood cultures are positive [56, 62, 63]. Better knowledge about this type of kidney injury may lead to better diagnosis of at-risk patients and more rapid therapy of sepsis. Likewise better biomarker-led diagnosis of septic AKI might result in intervention hours or days before azotemia or oliguria develop. Novel biomarkers, such as IL-18, are differentially sensitive to AKI caused by different mechanisms. IL-18 is thought to increase in early (3 h) sepsis-induced AKI as opposed to a slower rise in AKI from ischemia in hypotensive states [61, 64, 65]. Indeed, it is thought that the pathophysiological mechanisms for AKI from sepsis or non-septic etiologies (e.g., ischemia) are completely different [61]. With research targeted at the most harmful intermediaries in the septic process, therapeutic or preventative drugs or biologics may be found to protect the kidney in systemic inflammatory response syndrome (SIRS) and sepsis.

Other approaches might prevent or mitigate AKI in patients at risk for renal ischemia. As shown in the papers by Lehman et al. [18], Osterman et al. [57], and Raimundo et al. [66], huge databases of ICU time-series blood pressure readings and other clinical data have been mined to show the most sensitive criterion for adequate perfusion of the kidney in ICU and surgical patients. The time-honored 90 mmHg systolic threshold may soon, in routine clinical practice, be replaced by the more sensitive and specific 55 mmHg mean pressure as the commonly taught threshold for immediate intervention with vasopressor medication or fluids. Other hemodynamic and respiratory factors appear to contribute to the risk of AKI with unclear mechanisms: obesity, hyperuricemia, low indexed systemic oxygen delivery, hyperlactatemia, elevated central venous pressure, and the use of mechanical ventilation have been shown to be important but ill-defined factors [57, 66].

The ischemia-reperfusion paradigm so widely invoked in studies of stroke and myocardial infarction may likewise provide a framework for studying AKI from causes other than sepsis. However, it is generally felt that AKI from sepsis (but also, e.g., after cardiopulmonary bypass) is via other, largely inflammatory pathways. Accordingly, the mere restoration or improvement of renal perfusion will be insufficient to reverse kidney damage [67]. Other authors, using a combinatorial systems biology and proteomic approach, have identified the glutaminergic signaling pathway, induced by overactivation of N-methyl-D-aspartate receptors, as perhaps the inciting factor in AKI [68].

Lastly, bioinformatics approaches enable wide surveys of thousands of genes [69, 70] that are activated or repressed in AKI, as well as epigenetic changes that occur with AKI [71]. New candidate gene products and pathways discovered from this research will, it is hoped, open avenues to explore and to better prevent and mitigate AKI in the future.