Acute kidney injury subphenotypes based on creatinine trajectory identifies patients at increased risk of death

Acute kidney injury (AKI) is a heterogeneous syndrome defined by the Kidney Disease: Improving Global Outcomes (KDIGO) group as an increase in serum creatinine (SCr) of ≥0.3 mg/dl or >50% from baseline. The KDIGO group classifies patients from stage 0 (no AKI) to stage 3 AKI, based on maximum change in SCr or minimum urine output throughout the hospital stay. This definition for AKI includes a broad range of underlying pathophysiologic processes that would be expected to have different risks for poor clinical outcomes and may need to be treated differently. For instance, the KDIGO AKI definition does not differentiate between rises in creatinine due to temporary hemodynamic changes (e.g., volume depletion) versus true parenchymal injury (e.g., acute tubular necrosis). Even after classification by KDIGO severity stage, there is likely to be considerable clinical and biological heterogeneity. These limitations of the current AKI definition hamper the ability to better understand the pathophysiology of AKI and, potentially, the identification of effective novel therapies [1].

In clinical syndromes such as cancer, acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and asthma, identification of subphenotypes has led to insights into their pathogenesis and the development of personalized approaches to care [2‐8]. For example, Calfee et al. showed in patients with ARDS that indirect or direct lung injury is characterized by a unique biomarker pattern indicative of endothelial or epithelial dysfunction, respectively, suggestive of differences in the underlying pathobiology leading to these two forms of ARDS [5]. Additionally, in studies of asthma and COPD, researchers have employed a broad panel of clinical factors to identify subphenotypes [6, 8]. In our present study, we used creatinine kinetics to identify subphenotypes within AKI.

The trajectory of renal dysfunction is a potentially important and clinically intuitive parameter by which to understand AKI. Classifying AKI on the basis of trajectory rather than maximal creatinine change gets around the requirement for a preadmission “baseline” creatinine, which often is lacking for patients admitted to the intensive care unit (ICU) [9]. Classification based on trajectory also takes into account a patient’s response to early medical interventions and uses the added information provided by serial measures of renal dysfunction. Thus, identification of AKI subphenotypes based on the trajectory of SCr might improve the precision of risk stratification and provide more homogeneous groups of AKI cases.

We hypothesized that classifying patients with AKI into resolving and nonresolving subphenotypes on the basis of the trajectory of changes in SCr within the first 72 h of enrollment would result in groups with low and high associations with death, respectively. We also hypothesized that trajectory-based classification of AKI would be strongly linked to risk of death even after accounting for KDIGO severity stage.

In two distinct, large, heterogeneous ICU populations, we demonstrated that the trajectory of SCr defines subphenotypes of AKI and that these subphenotypes are independently associated with hospital mortality, length of hospital stay, and length of ICU stay. Despite significant differences in baseline clinical characteristics, etiologies for renal dysfunction, and ICU-level therapies between group 1 (trauma) and group 2 (mixed medical-surgical), the association between AKI subphenotypes and short-term clinical outcomes persisted. Critically ill patients with a nonresolving subphenotype compared with no renal dysfunction had a greater than 60% increased risk of hospital mortality. Additionally, patients with a resolving subphenotype had the same risk of death as those having no AKI. Of even greater interest, when we controlled for KDIGO severity of AKI, both AKI subphenotypes maintained their associations with hospital mortality. Notably, even among patients with KDIGO stage 1 AKI, the nonresolving subphenotype was associated with double the risk of death compared with the resolving subphenotype. These findings show that there exists considerable variability in risk for poor outcomes within the KDIGO stages of AKI and that even relatively small decreases in SCr from the maximal value have important implications for hospital outcomes.

Our findings extend and clarify those of prior studies seeking to subclassify patients with AKI. In previous studies, researchers evaluated the relationship between duration of AKI (transient versus persistent) and hospital mortality but found contradictory results [16, 17, 24]. In two studies, researchers found that separating patients on the basis of duration of AKI between transient (less than 72 h) and persistent (greater than 72 h) did not lead to the identification of patients at increased risk for mortality [16, 17]. In contrast, researchers in a third study of patients experiencing AKI after elective surgery found that a group of patients with a long duration of AKI were at higher risk of death than a group with a short AKI duration [25]. One potential explanation for these conflicting results is that the authors mandated that the baseline creatinine be based on an outpatient value, which is often unavailable in ICU patients [9]. For baseline values that were missing, researchers in these studies used mathematical formulas to impute this baseline SCr value. Given that these formulas were derived from relatively healthy outpatients in the steady state [26] with a “normal” expected glomerular filtrate rate, the application of these formulas to critically ill patients may lead to significant inaccuracies [27, 28]. In our present study, we sought to address this problem by developing an approach to subclassify AKI on the basis of patterns of SCr values after ICU admission, obviating the need for an outpatient or premorbid SCr value.

Our approach shows robust associations between nonresolving AKI and poor hospital outcomes in two large ICU cohorts. We selected the definition used to identify the two AKI subphenotypes—resolving and nonresolving—in a cohort of patients with a low prevalence of preexisting kidney disease and in whom the temporal relationship between injury and development of AKI was known. We then applied this definition for AKI subphenotypes to a considerably more diverse ICU population (mixed medical-surgical population). Markers of severity of illness, such as APACHE III score, vasopressor use, and mechanical ventilation, did not differentiate the AKI subphenotypes on ICU admission. Thus, we have identified a novel marker for risk of death over and above traditional risk factors for AKI and even well-established AKI severity scores. Of interest, three of the four AKI subphenotype definitions tested in group 1 showed differential risks for mortality. This suggests that the optimal definition may remain undetermined. Nonetheless, our work clearly shows that patients with a rapidly improving SCr have a very different outcome from a nonresolving SCr.

There are several ways in which classifying patients by AKI subphenotypes could be useful. First, refining the AKI phenotype could aid studies evaluating the pathophysiology of AKI by providing a more uniform study population. For instance, identifying AKI subphenotypes might enrich genetic studies for a particular pathologic subtype of AKI, thus reducing misclassification and improving the power to identify genetic risk factors associated with the development of AKI. Second, knowledge of AKI subphenotypes that are associated with differential risk of clinical outcomes could aid in triage decisions for severely ill patients. Third, enrollment in clinical trials could be directed at subgroups of patients most at risk of poor outcomes who might benefit from a novel therapeutic intervention. Fourth, existing biomarkers, such as neutrophil gelatinase-associated lipocalin, have had mixed results in identifying patients with AKI. In the cohorts studied, the KDIGO stage of AKI influenced the effectiveness of biomarkers to predict the development of AKI with lower test performance characteristics in patients with less severe AKI [29‐31]. Because the trajectory of SCr identifies patients with increased risk of poor clinical outcomes, it is possible that identifying AKI subphenotypes may improve biomarker performance. Grouping patients with a resolving versus nonresolving trajectory increases the heterogeneity of AKI and likely limits biomarker development.

Our study has some limitations. First, we did not have data on urine output, which can define an AKI event by KDIGO criteria. The inclusion of urine output may have increased the number of subjects classified as AKI cases and improved our ability to categorize AKI [32]. However, given that our definitions for the subphenotypes were based on the trajectory of SCr values, a better marker of true glomerular filtration rate and risk of death [26, 33], it is unlikely that our findings would have been different to a meaningful degree. Second, to determine severity of AKI, we used the nadir of SCr rather than a preadmission baseline value. Prehospital SCr values are often lacking in ICU patients, particularly during the early part of their admission, and thus an approach that bypasses the requirement for this information could allow for a more timely identification of AKI subphenotypes. Prehospital SCr values may have improved the accuracy of the KDIGO severity of AKI, but it is unlikely to have influenced the association of trajectory with outcomes. Additionally, if we had used prehospital SCr, then fluid administration in the emergency room or ICU may have created a dilution effect and decreased the incidence of AKI. In contrast, using a nadir of SCr overcomes this limitation by accounting for changes in SCr secondary to fluid administration. Third, group 1 included few patients with KDIGO AKI stage 2 or 3. The lack of these stages of AKI may limit the generalizability of trajectory-based findings to a trauma population with severe AKI. Fourth, previous studies have compared patients with and without AKI and have found an association with fluid accumulation and mortality in AKI [34, 35]. While most patients in the study were likely vigorously hydrated early in their ICU care, in accordance with ICU practice, we lacked accurate fluid balance data. Thus, it is unknown how fluid balance was associated with AKI subphenotype during a patient’s hospital stay.

Our study has several strengths. First, our definitions of AKI subphenotypes are based on changes in serial SCr values that are widely available in many existing ICU datasets. This will allow our subphenotype definitions to be quickly determined in large numbers of critically ill patients and their relationships with outcomes to be assessed. Furthermore, future application of our definitions to prospectively identify AKI subphenotypes should be straightforward. Second, we observed large and robust increases in risk of death associated with the nonresolving AKI subphenotype that were independent of the most widely established measure of AKI severity, the KDIGO staging system. This suggests that our approach could add value to the current classification schemes for AKI. Third, we used a large and diverse set of ICU patients who ranged from victims of major trauma to patients with severe pneumonia enrolled in randomized clinical trials of ARDS. Furthermore, the patients in group 1 were racially diverse. These factors suggest that our findings will be generalizable to other critically ill patient populations. Our results need to be validated in additional larger multicenter cohorts of critically ill patients.

We identified two distinct subphenotypes of AKI that are associated with different risks for hospital mortality. These subphenotypes also identify subjects with differences in risk for other short-term outcomes, including prolonged course in the ICU and time requiring mechanical ventilator support. Our observations should prompt future research to molecularly characterize these subphenotypes and to evaluate predictive biomarkers that might enable earlier identification of AKI subphenotypes. Refining the classification of AKI in ICU populations will allow recognition of patients at high risk of poor outcomes who might be targeted for enrollment in clinical trials, improve triage decisions, and focus the search for genetic risk factors.