Identification of Acute Kidney Injury Subphenotypes with Differing Molecular Signatures and Responses to Vasopressin Therapy

The combination of kidney function variables with cell cycle arrest biomarkers identifies distinct subphenotypes of sepsis-associated acute kidney injury: a post-hoc analysis (the PHENAKI study)

BACKGROUND

The severity and course of sepsis-associated acute kidney injury (SA-AKI) are correlated with the mortality rate. Early detection of SA-AKI subphenotypes might facilitate the rapid provision of individualized care.

PATIENTS AND METHODS

In this post-hoc analysis of a multicenter prospective study, we combined conventional kidney function variables with serial measurements of urine (tissue inhibitor of metalloproteinase-2 [TIMP-2])* (insulin-like growth factor-binding protein [IGFBP7]) at 0, 6, 12, and 24 h) and then using an unsupervised hierarchical clustering of principal components (HCPC) approach to identify different phenotypes of SA-AKI. We then compared the subphenotypes with regard to a composite outcome of in-hospital death or the initiation of renal replacement therapy (RRT).

RESULTS

We included 184 patients presenting SA-AKI within 6 h of the initiation of catecholamines. Three distinct subphenotypes were identified: subphenotype A (99 patients) was characterized by a normal urine output (UO), a low SCr and a low [TIMP-2]*[IGFBP7] level; subphenotype B (74 patients) was characterized by existing chronic kidney disease (CKD), a higher SCr, a low UO, and an intermediate [TIMP-2]*[IGFBP7] level; and subphenotype C was characterized by very low UO, a very high [TIMP-2]*[IGFBP7] level, and an intermediate SCr level. With subphenotype A as the reference, the adjusted hazard ratio (aHR) [95%CI] for the composite outcome was 3.77 [1.92-7.42] (p < 0.001) for subphenotype B and 4.80 [1.67-13.82] (p = 0.004) for subphenotype C.

CONCLUSIONS

Combining conventional kidney function variables with urine measurements of [TIMP-2]*[IGFBP7] might help to identify distinct SA-AKI subphenotypes with different short-term courses and survival rates.

Fine-grained subphenotypes in acute kidney injury populations based on deep clustering: Derivation and interpretation

BACKGROUND

Acute kidney injury (AKI) is associated with increased mortality in critically ill patients. Due to differences in the etiology and pathophysiological mechanism, the current AKI criteria put it an embarrassment to evaluate clinical therapy and prognosis.

OBJECTIVE

We aimed to identify subphenotypes based on routinely collected clinical data to expose the unique pathophysiologic patterns.

METHODS

A retrospective study was conducted based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD), and a deep clustering approach was conducted to derive subphenotypes. We conducted further analysis to uncover the underlying clinical patterns and interpret the subphenotype derivation.

RESULTS

We studied 14,189 and 19,382 patients with AKI within 48 h of ICU admission in the two datasets, respectively. Through our approach, we identified seven distinct AKI subphenotypes with mortality heterogeneity in each cohort. These subphenotypes displayed significant variations in demographics, comorbidities, levels of laboratory measurements, and survival patterns. Notably, the subphenotypes could not be effectively characterized using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria alone. Therefore, we uncovered the unique underlying characteristics of each subphenotype through model-based interpretation. To assess the usability of the subphenotypes, we conducted an evaluation, which yielded a micro-Area Under the Receiver Operating Characteristic (AUROC) of 0.81 in the single-center cohort and 0.83 in the multi-center cohort within 48-hour of admission.

CONCLUSION

We derived highly characteristic, interpretable, and usable AKI subphenotypes that exhibited superior prognostic values.

Outcomes after acute kidney injury and critical illness

PURPOSE OF REVIEW

Acute kidney injury (AKI) in critical illness is common, and survivors are faced with a host of adverse outcomes. In this article, we review the current landscape of outcomes and care in survivors of AKI and critical illness.

RECENT FINDINGS

Follow-up care of survivors of AKI and critical illness is prudent to monitor for and mitigate the risk of adverse outcomes. Observational data have suggested improvement in outcomes with nephrology-based follow-up care, and recent interventional studies demonstrate similar findings. However, current post-AKI care is suboptimal with various challenges, such as breakdowns in the transition of care during hospital episodes and into the community, barriers for patients in follow-up, and lack of identification of high-risk patients for nephrology-based follow-up. Tools predictive of renal nonrecovery and long-term outcomes may help to identify high-risk patients who may benefit the most from nephrology-based care post-AKI.

SUMMARY

Follow-up care of survivors of AKI and critical illness may improve outcomes and there is a need to prioritize transitions of care into the community. Further research is needed to elucidate the best ways to risk-stratify and manage post-AKI survivors to improve outcomes.

The Association Between Vasopressin and Adverse Kidney Outcomes in Children and Young Adults Requiring Vasopressors on Continuous Renal Replacement Therapy

OBJECTIVES

Continuous renal replacement therapy (CRRT) and shock are both associated with high morbidity and mortality in the ICU. Adult data suggest renoprotective effects of vasopressin vs. catecholamines (norepinephrine and epinephrine). We aimed to determine whether vasopressin use during CRRT was associated with improved kidney outcomes in children and young adults.

DESIGN

Secondary analysis of Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Disease (WE-ROCK), a multicenter, retrospective cohort study.

SETTING

Neonatal, cardiac, PICUs at 34 centers internationally from January 1, 2015, to December 31, 2021.

PATIENTS/SUBJECTS

Patients younger than 25 years receiving CRRT for acute kidney injury and/or fluid overload and requiring vasopressors. Patients receiving vasopressin were compared with patients receiving only norepinephrine/epinephrine. The impact of timing of vasopressin relative to CRRT start was assessed by categorizing patients as: early (on or before day 0), intermediate (days 1-2), and late (days 3-7).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of 1016 patients, 665 (65%) required vasopressors in the first week of CRRT. Of 665, 248 (37%) received vasopressin, 473 (71%) experienced Major Adverse Kidney Events at 90 days (MAKE-90) (death, renal replacement therapy dependence, and/or > 125% increase in serum creatinine from baseline 90 days from CRRT initiation), and 195 (29%) liberated from CRRT on the first attempt within 28 days. Receipt of vasopressin was associated with higher odds of MAKE-90 (adjusted odds ratio [aOR], 1.80; 95% CI, 1.20-2.71; p = 0.005) but not liberation success. In the vasopressin group, intermediate/late initiation was associated with higher odds of MAKE-90 (aOR, 2.67; 95% CI, 1.17-6.11; p = 0.02) compared with early initiation.

CONCLUSIONS

Nearly two-thirds of children and young adults receiving CRRT required vasopressors, including over one-third who received vasopressin. Receipt of vasopressin was associated with more MAKE-90, although earlier initiation in those who received it appears beneficial. Prospective studies are needed to understand the appropriate timing, dose, and subpopulation for use of vasopressin.

A novel online calculator based on inflammation-related endotypes and clinical features to predict postoperative pulmonary infection in patients with cervical spinal cord injury

BACKGROUND

Postoperative pulmonary infection (POI) of patients with cervical spinal cord injury (CSCI) is highly heterogeneous, while the potential endotypes and related risk factors remain unclear.

METHODS

A retrospective collection of 290 CSCI patients was conducted from January 2010 to July 2024 using 1:1 propensity score matching to compare POI (n = 145) and non-POI (n = 145) groups. We generated laboratory examination data from admission patients and identified endotypes using unsupervised consensus clustering and machine learning. CSCI patients were randomly assigned to the training set (n = 203) and internal validation set (n = 87). A separate cohort comprising 245 CSCI patients were used for external validation. Independent predictors for POI were identified using univariate and multivariate logistic regression. A nomogram and an online calculator were developed and validated, both internally and externally.

RESULTS

Two inflammation-related endotypes were identified: high inflammation endotype (endotype C1) and low inflammation endotype (endotype C2). Eight predictors for POI were identified (including age, operation duration, number of surgical segments, time between injury and surgery, preoperative steroid pulse, American Spinal Injury Association (ASIA) grade, smoking history, and inflammation-related endotype). A nomogram integrating the risk factors showed excellent discrimination in the training set (AUC, 0.976; 95% CI 0.956-0.996), internal validation set (AUC, 0.993; 95% CI 0.981-1.000), and external validation set (AUC, 0.799; 95%CI 0.744-0.854). Calibration curves demonstrated excellent fit, and decision curves highlighted its favorable clinical value. An online calculator (https://tjspine.shinyapps.io/dynnomapp/) was constructed to improve the convenience and efficiency of our prediction model.

CONCLUSIONS

We identified inflammation-related endotype and constructed a web-based calculator for predicting POI in patients with CSCI, exhibiting excellent clinical utility.

Biomarkers in acute kidney injury

Acute kidney injury (AKI) is a multifactorial syndrome with a high risk of short- and long-term complications as well as increased health care costs. The traditional biomarkers of AKI, serum creatinine and urine output, have important limitations. The discovery of new functional and damage/stress biomarkers has enabled a more precise delineation of the aetiology, pathophysiology, site, mechanisms, and severity of injury. This has allowed earlier diagnosis, better prognostication, and the identification of AKI sub-phenotypes. In this review, we summarize the roles and challenges of these new biomarkers in clinical practice and research.

Characteristics and Outcomes of Children and Young Adults With Sepsis Requiring Continuous Renal Replacement Therapy: A Comparative Analysis From the Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Disease (WE-ROCK)

OBJECTIVES

Pediatric sepsis-associated acute kidney injury (AKI) often requires continuous renal replacement therapy (CRRT), but limited data exist regarding patient characteristics and outcomes. We aimed to describe these features, including the impact of possible dialytrauma (i.e., vasoactive requirement, negative fluid balance) on outcomes, and contrast them to nonseptic patients in an international cohort of children and young adults receiving CRRT.

DESIGN

A secondary analysis of Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Disease (WE-ROCK), an international, multicenter, retrospective study.

SETTING

Neonatal, cardiac and PICUs at 34 centers in nine countries from January 1, 2015, to December 31, 2021.

PATIENTS

Patients 0-25 years old requiring CRRT for AKI and/or fluid overload.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Among 1016 patients, 446 (44%) had sepsis at CRRT initiation and 650 (64%) experienced Major Adverse Kidney Events at 90 days (MAKE-90) (defined as a composite of death, renal replacement therapy [RRT] dependence, or > 25% decline in estimated glomerular filtration rate from baseline at 90 d from CRRT initiation). Septic patients were less likely to liberate from CRRT by 28 days (30% vs. 38%; p < 0.001) and had higher rates of MAKE-90 (70% vs. 61%; p = 0.002) and higher mortality (47% vs. 31%; p < 0.001) than nonseptic patients; however, septic survivors were less likely to be RRT dependent at 90 days (10% vs. 18%; p = 0.011). On multivariable regression, pre-CRRT vasoactive requirement, time to negative fluid balance, and median daily fluid balance over the first week of CRRT were not associated with MAKE-90; however, increasing duration of vasoactive requirement was independently associated with increased odds of MAKE-90 (adjusted OR [aOR], 1.16; 95% CI, 1.05-1.28) and mortality (aOR, 1.20; 95% CI, 1.1-1.32) for each additional day of support.

CONCLUSIONS

Septic children requiring CRRT have different clinical characteristics and outcomes compared with those without sepsis, including higher rates of mortality and MAKE-90. Increasing duration of vasoactive support during the first week of CRRT, a surrogate of potential dialytrauma, appears to be associated with these outcomes.

PTEN in kidney diseases: a potential therapeutic target in preventing AKI-to-CKD transition

Renal fibrosis, a critical factor in the development of chronic kidney disease (CKD), is predominantly initiated by acute kidney injury (AKI) and subsequent maladaptive repair resulting from pharmacological or pathological stimuli. Phosphatase and tensin homolog (PTEN), also known as phosphatase and tensin-associated phosphatase, plays a pivotal role in regulating the physiological behavior of renal tubular epithelial cells, glomeruli, and renal interstitial cells, thereby preserving the homeostasis of renal structure and function. It significantly impacts cell proliferation, apoptosis, fibrosis, and mitochondrial energy metabolism during AKI-to-CKD transition. Despite gradual elucidation of PTEN's involvement in various kidney injuries, its specific role in AKI and maladaptive repair after injury remains unclear. This review endeavors to delineate the multifaceted role of PTEN in renal pathology during AKI and CKD progression along with its underlying mechanisms, emphasizing its influence on oxidative stress, autophagy, non-coding RNA-mediated recruitment and activation of immune cells as well as renal fibrosis. Furthermore, we summarize prospective therapeutic targeting strategies for AKI and CKD-treatment related diseases through modulation of PTEN.

Reframing acute kidney injury as a pathophysiological continuum of disrupted renal excretory function

Surrogate measures of glomerular filtration rate (GFR) continue to serve as pivotal determinants of the incidence, severity, and management of acute kidney injury (AKI), as well as the primary reference point underpinning knowledge of its pathophysiology. However, several clinically important deficits in aspects of renal excretory function during AKI other than GFR decline, including acid-base regulation, electrolyte and water balance, and urinary concentrating capacity, can evade detection when diagnostic criteria are built around purely GFR-based assessments. The use of putative markers of tubular injury to detect "sub-clinical" AKI has been proposed to expand the definition and diagnostic criteria for AKI, but their diagnostic performance is curtailed by ambiguity with respect to their biological meaning and context specificity. Efforts to devise new holistic assessments of overall renal functional compromise in AKI would foster the capacity to better personalize patient care by replacing biomarker threshold-based diagnostic criteria with a shift to assessment of compromise along a pathophysiological continuum. The term AKI refers to a syndrome of sudden renal deterioration, the severity of which is classified by precise diagnostic criteria that have unquestionable utility in patient management as well as blatant limitations. Particularly, the absence of an explicit pathophysiological definition of AKI curtails further scientific development and clinical handling, entrapping the field within its present narrow GFR-based view. A refreshed approach based on a more holistic consideration of renal functional impairment in AKI as the basis for a new diagnostic concept that reaches beyond the boundaries imposed by the current GFR threshold-based classification of AKI, capturing broader aspects of pathogenesis, could enhance AKI prevention strategies and improve AKI patient outcome and prognosis.

Acute kidney injury genetic risks: taking it 1 SNP at a time

This commentary addresses some of the strengths, shortcomings, and challenges of the genome-wide association study of acute kidney injury (AKI) report in this issue. This AKI genome-wide association study is well executed and provides significant progress in finding 2 genome-wide significant loci. However, significant interpretive challenges remain, where advancements in methods are needed because of the clinical heterogeneity of the AKI phenotype, plus possible bias due to genetic correlation between index hospitalization risk and AKI risk.

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes

BACKGROUND

Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches.

METHODS

We performed latent profile analyses using clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock observational cohort to derive phenotypes and trained a support vector machine model to assign phenotypes in an internal validation set. We established the clinical relevance of phenotypes and tested for their interaction with common sepsis treatments on patient outcomes. We conducted transcriptomic analyses to delineate phenotype-specific biology and inferred underlying cell subpopulations. Finally, we compared whether latent profile phenotypes overlapped with established gene-expression endotypes and compared survival among patients based on an integrated subclassification scheme.

RESULTS

Among 1071 pediatric septic shock patients requiring vasoactive support on day 1 included, we identified two phenotypes which we designated as Phenotype 1 (19.5%) and Phenotype 2 (80.5%). Membership in Phenotype 1 was associated with ~ fourfold adjusted odds of complicated course relative to Phenotype 2. Patients belonging to Phenotype 1 were characterized by relatively higher Angiopoietin-2/Tie-2 ratio, Angiopoietin-2, soluble thrombomodulin (sTM), interleukin 8 (IL-8), and intercellular adhesion molecule 1 (ICAM-1) and lower Tie-2 and Angiopoietin-1 concentrations compared to Phenotype 2. We did not identify significant interactions between phenotypes, common treatments, and clinical outcomes. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and driven primarily by developing neutrophils among patients designated as Phenotype 1. There was no statistically significant overlap between established gene-expression endotypes, reflective of the host adaptive response, and the newly derived phenotypes, reflective of the host innate response including microvascular endothelial dysfunction. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing patient endophenotypes.

CONCLUSIONS

Our research underscores the reproducibility of latent profile analyses to identify pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Enhancing sepsis biomarker development: key considerations from public and private perspectives

Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.

Molecular Phenotyping of Patients with Sepsis and Kidney Injury and Differential Response to Fluid Resuscitation

Purpose

Previous work has identified two AKI sub-phenotypes (SP1 and SP2) characterized by differences in inflammation and endothelial dysfunction. Here we identify these sub-phenotypes using biospecimens collected in the emergency department and test for differential response to restrictive versus liberal fluid strategy in sepsis-induced hypotension in the CLOVERS trial.

Methods

We applied a previously validated 3-biomarker model using plasma angiopietin-1 and 2, and soluble tumor necrosis factor receptor-1 to classify sub-phenotypes in patients with kidney dysfunction (AKI or end-stage kidney disease [ESKD]). We also compared a de novo latent class analysis (LCA) to the 3-biomarker based sub-phenotypes. Kaplan-Meier estimates were used to test for differences in outcomes and sub-phenotype by treatment interaction.

Results

Among 1289 patients, 846 had kidney dysfunction on enrollment and the 3-variable prediction model identified 605 as SP1 and 241 as SP2. The optimal LCA model identified two sub-phenotypes with high correlation with the 3-biomarker model (Cohen's Kappa 0.8). The risk of 28 and 90-day mortality was greater in SP2 relative to SP1 independent of AKI stage and SOFA scores. Patients with SP2, characterized by more severe endothelial injury and inflammation, had a reduction in 28-day mortality with a restrictive fluid strategy versus a liberal fluid strategy (26% vs 41%), while patients with SP1 had no difference in 28-day mortality (10% vs 11%) (p-value-for-interaction = 0.03).

Conclusion

Sub-phenotypes can be identified in the emergency department that respond differently to fluid strategy in sepsis. Identification of these sub-phenotypes could inform a precision-guided therapeutic approach for patients with sepsis-induced hypotension and kidney injury.

The Road to Precision Medicine for Acute Kidney Injury

OBJECTIVES

Acute kidney injury (AKI) is a common form of organ dysfunction in the ICU. AKI is associated with adverse short- and long-term outcomes, including high mortality rates, which have not measurably improved over the past decade. This review summarizes the available literature examining the evidence of the need for precision medicine in AKI in critical illness, highlights the current evidence for heterogeneity in the field of AKI, discusses the progress made in advancing precision in AKI, and provides a roadmap for studying precision-guided care in AKI.

DATA SOURCES

Medical literature regarding topics relevant to precision medicine in AKI, including AKI definitions, epidemiology, and outcomes, novel AKI biomarkers, studies of electronic health records (EHRs), clinical trial design, and observational studies of kidney biopsies in patients with AKI.

STUDY SELECTION

English language observational studies, randomized clinical trials, reviews, professional society recommendations, and guidelines on areas related to precision medicine in AKI.

DATA EXTRACTION

Relevant study results, statements, and guidelines were qualitatively assessed and narratively synthesized.

DATA SYNTHESIS

We synthesized relevant study results, professional society recommendations, and guidelines in this discussion.

CONCLUSIONS

AKI is a syndrome that encompasses a wide range of underlying pathologies, and this heterogeneity has hindered the development of novel therapeutics for AKI. Wide-ranging efforts to improve precision in AKI have included the validation of novel biomarkers of AKI, leveraging EHRs for disease classification, and phenotyping of tubular secretory clearance. Ongoing efforts such as the Kidney Precision Medicine Project, identifying subphenotypes in AKI, and optimizing clinical trials and endpoints all have great promise in advancing precision medicine in AKI.

Biomarkers in pursuit of precision medicine for acute kidney injury: hard to get rid of customs

Traditional acute kidney injury (AKI) classifications, which are centered around semi-anatomical lines, can no longer capture the complexity of AKI. By employing strategies to identify predictive and prognostic enrichment targets, experts could gain a deeper comprehension of AKI's pathophysiology, allowing for the development of treatment-specific targets and enhancing individualized care. Subphenotyping, which is enriched with AKI biomarkers, holds insights into distinct risk profiles and tailored treatment strategies that redefine AKI and contribute to improved clinical management. The utilization of biomarkers such as N-acetyl-β-D-glucosaminidase, tissue inhibitor of metalloprotease-2·insulin-like growth factor-binding protein 7, kidney injury molecule-1, and liver fatty acid-binding protein garnered significant attention as a means to predict subclinical AKI. Novel biomarkers offer promise in predicting persistent AKI, with urinary motif chemokine ligand 14 displaying significant sensitivity and specificity. Furthermore, they serve as predictive markers for weaning patients from acute dialysis and offer valuable insights into distinct AKI subgroups. The proposed management of AKI, which is encapsulated in a structured flowchart, bridges the gap between research and clinical practice. It streamlines the utilization of biomarkers and subphenotyping, promising a future in which AKI is swiftly identified and managed with unprecedented precision. Incorporating kidney biomarkers into strategies for early AKI detection and the initiation of AKI care bundles has proven to be more effective than using care bundles without these novel biomarkers. This comprehensive approach represents a significant stride toward precision medicine, enabling the identification of high-risk subphenotypes in patients with AKI.

[Recent developments in acute kidney injury : Definition, biomarkers, subphenotypes, and management]

Acute kidney injury (AKI) is a common problem in critically ill patients and is associated with increased morbidity and mortality. Since 2012, AKI has been defined according to the KDIGO (Kidney Disease Improving Global Outcome) guidelines. As some biomarkers are now available that can provide useful clinical information, a new definition including a new stage 1S has been proposed by an expert group of the Acute Disease Quality Initiative (ADQI). At this stage, classic AKI criteria are not yet met, but biomarkers are already positive defining subclinical AKI. This stage 1S is associated with a worse patient outcome, regardless of the biomarker chosen. The PrevAKI and PrevAKI-Multicenter trial also showed that risk stratification with a biomarker and implementation of the KDIGO bundle (in the high-risk group) can reduce the rate of moderate and severe AKI. In the absence of a successful clinical trial, conservative management remains the primary focus of treatment. This mainly involves optimization of hemodynamics and an individualized (restrictive) fluid management. The STARRT-AKI trial has shown that there is no benefit from accelerated initiation of renal replacement therapy. However, delaying too long might be associated with potential harm, as shown in the AKIKI2 study. Prospective studies are needed to determine whether artificial intelligence will play a role in AKI in the future, helping to guide treatment decisions and improve outcomes.

The impact of hypertonic saline on damage control laparotomy after penetrating abdominal trauma

PURPOSE

The inability to achieve primary fascial closure (PFC) after emergency laparotomy increases the rates of adverse outcomes including fistula formation, incisional hernia, and intraabdominal infection. Hypertonic saline (HTS) infusion improves early PFC rates and decreases time to PFC in patients undergoing damage control laparotomy (DCL) after injury. We hypothesized that in patients undergoing DCL after penetrating abdominal injury, HTS infusion would decrease the time to fascial closure as well as the volume of crystalloid required for resuscitation without inducing clinically relevant acute kidney injury (AKI) or electrolyte derangements.

METHODS

We retrospectively analyzed all penetrating abdominal injury patients undergoing DCL within the University of Pennsylvania Health System (January 2015-December 2018). We compared patients who received 3% HTS at 30 mL/h (HTS) to those receiving isotonic fluid (ISO) for resuscitation while the abdominal fascia remained open. Primary outcomes were the rate of early PFC (PFC within 72 h) and time to PFC; secondary outcomes included acute kidney injury, sodium derangement, ventilator-free days, hospital length of stay (LOS), and ICU LOS. Intergroup comparisons occurred by ANOVA and Tukey's comparison, and student's t, and Fischer's exact tests, as appropriate. A Shapiro-Wilk test was performed to determine normality of distribution.

RESULTS

Fifty-seven patients underwent DCL after penetrating abdominal injury (ISO n = 41, HTS n = 16). There were no significant intergroup differences in baseline characteristics or injury severity score. Mean time to fascial closure was significantly shorter in HTS (36.37 h ± 14.21 vs 59.05 h ± 50.75, p = 0.02), and the PFC rate was significantly higher in HTS (100% vs 73%, p = 0.01). Mean 24-h fluid and 48-h fluid totals were significantly less in HTS versus ISO (24 h: 5.2L ± 1.7 vs 8.6L ± 2.2, p = 0.01; 48 h: 1.3L ± 1.1 vs 2.6L ± 2.2, p = 0.008). During the first 72 h, peak sodium (Na) concentration (146.2 mEq/L ± 2.94 vs 142.8 mEq/L ± 3.67, p = 0.0017) as well as change in Na from ICU admission (5.1 mEq/L vs 2.3, p = 0.016) were significantly higher in HTS compared to ISO. Patients in the HTS group received significantly more blood in the trauma bay compared to ISO. There were no intergroup differences in intraoperative blood transfusion volume, AKI incidence, change in chloride concentration (△Cl) from ICU admit, Na to Cl gradient (Na:Cl), initial serum creatinine (Cr), peak post-operative Cr, change in creatinine concentration (△Cr) from ICU admission, creatinine clearance (CrCl), initial serum potassium (K), peak ICU K, change in K from ICU admission, initial pH, highest or lowest post-operative pH, mean hospital LOS, ICU LOS, and ventilator-free days.

CONCLUSIONS

HTS infusion in patients undergoing DCL after penetrating abdominal injury decreases the time to fascial closure and led to 100% early PFC. HTS infusion also decreased resuscitative fluid volume without causing significant AKI or electrolyte derangement. HTS appears to offer a safe and effective fluid management approach in patients who sustain penetrating abdominal injury and DCL to support early PFC without inducing measurable harm.

LEVEL OF EVIDENCE

Level III.

Biological basis of critical illness subclasses: from the bedside to the bench and back again

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis

BACKGROUND

Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.

METHODS

This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation.

RESULTS

Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation.

CONCLUSIONS

These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Inflammatory subphenotypes previously identified in ARDS are associated with mortality at intensive care unit discharge: a secondary analysis of a prospective observational study

BACKGROUND

Intensive care unit (ICU)-survivors have an increased risk of mortality after discharge compared to the general population. On ICU admission subphenotypes based on the plasma biomarker levels of interleukin-8, protein C and bicarbonate have been identified in patients admitted with acute respiratory distress syndrome (ARDS) that are prognostic of outcome and predictive of treatment response. We hypothesized that if these inflammatory subphenotypes previously identified among ARDS patients are assigned at ICU discharge in a more general critically ill population, they are associated with short- and long-term outcome.

METHODS

A secondary analysis of a prospective observational cohort study conducted in two Dutch ICUs between 2011 and 2014 was performed. All patients discharged alive from the ICU were at ICU discharge adjudicated to the previously identified inflammatory subphenotypes applying a validated parsimonious model using variables measured median 10.6 h [IQR, 8.0-31.4] prior to ICU discharge. Subphenotype distribution at ICU discharge, clinical characteristics and outcomes were analyzed. As a sensitivity analysis, a latent class analysis (LCA) was executed for subphenotype identification based on plasma protein biomarkers at ICU discharge reflective of coagulation activation, endothelial cell activation and inflammation. Concordance between the subphenotyping strategies was studied.

RESULTS

Of the 8332 patients included in the original cohort, 1483 ICU-survivors had plasma biomarkers available and could be assigned to the inflammatory subphenotypes. At ICU discharge 6% (n = 86) was assigned to the hyperinflammatory and 94% (n = 1397) to the hypoinflammatory subphenotype. Patients assigned to the hyperinflammatory subphenotype were discharged with signs of more severe organ dysfunction (SOFA scores 7 [IQR 5-9] vs. 4 [IQR 2-6], p < 0.001). Mortality was higher in patients assigned to the hyperinflammatory subphenotype (30-day mortality 21% vs. 11%, p = 0.005; one-year mortality 48% vs. 28%, p < 0.001). LCA deemed 2 subphenotypes most suitable. ICU-survivors from class 1 had significantly higher mortality compared to class 2. Patients belonging to the hyperinflammatory subphenotype were mainly in class 1.

CONCLUSIONS

Patients assigned to the hyperinflammatory subphenotype at ICU discharge showed significantly stronger anomalies in coagulation activation, endothelial cell activation and inflammation pathways implicated in the pathogenesis of critical disease and increased mortality until one-year follow up.

[Artificial intelligence and acute kidney injury]

Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primarily focused on the prediction of AKI, but further approaches are also being used to classify existing AKI into different phenotypes. Different AI models are used for prediction. The area under the receiver operating characteristic curve values (AUROC) achieved with these models vary and are influenced by several factors, such as the prediction time and the definition of AKI. Most models have an AUROC between 0.650 and 0.900, with lower values for predictions further into the future and when applying Acute Kidney Injury Network (AKIN) instead of KDIGO criteria. Classification into phenotypes already makes it possible to categorize patients into groups with different risks of mortality or requirement of renal replacement therapy (RRT), but the etiologies or therapeutic consequences derived from this are still lacking. However, all the models suffer from AI-specific shortcomings. The use of large databases does not make it possible to promptly include recent changes in therapy and the implementation of new biomarkers in a relevant proportion. For this reason, serum creatinine and urinary output, with their known limitations, dominate current AI models for prediction impairing the performance of the current models. On the other hand, the increasingly complex models no longer allow physicians to understand the basis on which the warning of a threatening AKI is calculated and subsequent initiation of therapy should take place. The successful use of AIs in routine clinical practice will be highly determined by the trust of the physicians in the systems and overcoming the aforementioned weaknesses. However, the clinician will remain irreplaceable as the decisive authority for critically ill patients by combining measurable and nonmeasurable parameters.

Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials

Acute kidney injury (AKI) often complicates sepsis and is associated with high morbidity and mortality. In recent years, several important clinical trials have improved our understanding of sepsis-associated AKI (SA-AKI) and impacted clinical care. Advances in sub-phenotyping of sepsis and AKI and clinical trial design offer unprecedented opportunities to fill gaps in knowledge and generate better evidence for improving the outcome of critically ill patients with SA-AKI. In this manuscript, we review the recent literature of clinical trials in sepsis with focus on studies that explore SA-AKI as a primary or secondary outcome. We discuss lessons learned and potential opportunities to improve the design of clinical trials and generate actionable evidence in future research. We specifically discuss the role of enrichment strategies to target populations that are most likely to derive benefit and the importance of patient-centered clinical trial endpoints and appropriate trial designs with the aim to provide guidance in designing future trials.

Identification and validation of ferroptosis-related gene SLC2A1 as a novel prognostic biomarker in AKI

BACKGROUND

Emerging evidence reveals the key role of ferroptosis in the pathophysiological process of acute kidney injury (AKI). Our study aimed to investigate the potential ferroptosis-related gene in AKI through bioinformatics and experimental validation.

METHODS

The AKI single-cell sequencing dataset was retrieved from the GEO database and ferroptosis-related genes were extracted from the GENECARD website. The potential differentially expressed ferroptosis-related genes of AKI were selected. Functional enrichment analysis was performed. Machine learning algorithms were used to identify key ferroptosis-related genes associated with AKI. A multi-factor Cox regression analysis was used to construct a risk score model. The accuracy of the risk score model was validated using receiver operating characteristic (ROC) curve analysis. We extensively explored the immune landscape of AKI using CIBERSORT tool. Finally, expressions of ferroptosis DEGs were validated in vivo and in vitro by Western blot, ICH and transfection experiments.

RESULTS

Three hub genes (BAP1, MDM4, SLC2A1) were identified and validated by constructing drug regulatory network and subsequent screening using experimentally determined interactions. The risk mode showed the low-risk group had significantly better prognosis compared to high-risk group. The risk score was independently associated with overall survival. The ROC curve analysis showed that the prognosis model had good predictive ability. Additionally, CIBERSORT immune infiltration analysis suggest that the hub gene may influence cell recruitment and infiltration in AKI. Validation experiments revealed that SLC2A1 functions by regulating ferroptosis.

CONCLUSIONS

In summary, our study not only identifies SLC2A1 as diagnostic biomarker for AKI, but also sheds light on the role of it in AKI progression, providing novel insights for the clinical diagnosis and treatment of AKI.

Designing acute kidney injury clinical trials

Acute kidney injury (AKI) is a common clinical condition with various causes and is associated with increased mortality. Despite advances in supportive care, AKI increases not only the risk of premature death compared with the general population but also the risk of developing chronic kidney disease and progressing towards kidney failure. Currently, no specific therapy exists for preventing or treating AKI other than mitigating further injury and supportive care. To address this unmet need, novel therapeutic interventions targeting the underlying pathophysiology must be developed. New and well-designed clinical trials with appropriate end points must be subsequently designed and implemented to test the efficacy of such new interventions. Herein, we discuss predictive and prognostic enrichment strategies for patient selection, as well as primary and secondary end points that can be used in different clinical trial designs (specifically, prevention and treatment trials) to evaluate novel interventions and improve the outcomes of patients at a high risk of AKI or with established AKI.

Surviving Sepsis Campaign Research Priorities 2023

OBJECTIVES

To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock.

DESIGN

Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities.

METHODS

Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article.

RESULTS

A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis?

CONCLUSIONS

Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.

Identification of molecular subphenotypes in two cohorts of paediatric ARDS

BACKGROUND

Two subphenotypes of acute respiratory distress syndrome (ARDS), hypoinflammatory and hyperinflammatory, have been reported in adults and in a single paediatric cohort. The relevance of these subphenotypes in paediatrics requires further investigation. We aimed to identify subphenotypes in two large observational cohorts of paediatric ARDS and assess their congruence with prior descriptions.

METHODS

We performed latent class analysis (LCA) separately on two cohorts using biomarkers as inputs. Subphenotypes were compared on clinical characteristics and outcomes. Finally, we assessed overlap with adult cohorts using parsimonious classifiers.

FINDINGS

In two cohorts from the Children's Hospital of Philadelphia (n=333) and from a multicentre study based at the University of California San Francisco (n=293), LCA identified two subphenotypes defined by differential elevation of biomarkers reflecting inflammation and endotheliopathy. In both cohorts, hyperinflammatory subjects had greater illness severity, more sepsis and higher mortality (41% and 28% in hyperinflammatory vs 11% and 7% in hypoinflammatory). Both cohorts demonstrated overlap with adult subphenotypes when assessed using parsimonious classifiers.

INTERPRETATION

We identified hypoinflammatory and hyperinflammatory subphenotypes of paediatric ARDS from two separate cohorts with utility for prognostic and potentially predictive, enrichment. Future paediatric ARDS trials should identify and leverage biomarker-defined subphenotypes in their analysis.

Derivation, validation, and transcriptomic assessment of pediatric septic shock phenotypes identified through latent profile analyses: Results from a prospective multi-center observational cohort

Background

Sepsis poses a grave threat, especially among children, but treatments are limited due to clinical and biological heterogeneity among patients. Thus, there is an urgent need for precise subclassification of patients to guide therapeutic interventions.

Methods

We used clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock cohort to derive phenotypes using latent profile analyses. Thereafter, we trained a support vector machine model to assign phenotypes in a hold-out validation set. We tested interactions between phenotypes and common sepsis therapies on clinical outcomes and conducted transcriptomic analyses to better understand the phenotype-specific biology. Finally, we compared whether newly identified phenotypes overlapped with established gene-expression endotypes and tested the utility of an integrated subclassification scheme.

Findings

Among 1,071 patients included, we identified two phenotypes which we named 'inflamed' (19.5%) and an 'uninflamed' phenotype (80.5%). The 'inflamed' phenotype had an over 4-fold risk of 28-day mortality relative to those 'uninflamed'. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and suggested an overabundance of developing neutrophils, pro-T/NK cells, and NK cells among those 'inflamed'. There was no significant overlap between endotypes and phenotypes. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing endophenotypes.

Interpretation

Our research underscores the reproducibility of latent profile analyses to identify clinical and biologically informative pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury

INTRODUCTION

Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI.

PATIENTS AND METHODS

This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine-Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively.

RESULTS

Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5-128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35-3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38-0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25-1.74]) in another independent multi-centre SA-AKI cohort.

CONCLUSIONS

Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.Key messagesUnsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction.Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor.This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.

Personalized acute kidney injury treatment

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a complex syndrome that might be induced by different causes and is associated with an increased morbidity and mortality. Therefore, it is a very heterogeneous syndrome and establishing a "one size fits all" treatment approach might not work. This review aims to examine the potential of personalized treatment strategies for AKI.

RECENT FINDINGS

The traditional diagnosis of AKI is based on changes of serum creatinine and urine output, but these two functional biomarkers have several limitations. Recent research identified different AKI phenotypes based on clinical features, biomarkers, and pathophysiological pathways. Biomarkers, such as Cystatin C, NGAL, TIMP2∗IGFBP7, CCL14, and DKK-3, have shown promise in predicting AKI development, renal recovery, and prognosis. Biomarker-guided interventions, such as the implementation of the KDIGO bundle, have demonstrated an improvement in renal outcomes in specific patient groups.

SUMMARY

A personalized approach to AKI treatment as well as research is becoming increasingly important as it allows the identification of distinct AKI phenotypes and the potential for targeted interventions. By utilizing biomarkers and clinical features, physicians might be able to stratify patients into subphenotypes, enabling more individualized treatment strategies. This review highlights the potential of personalized AKI treatment, emphasizing the need for further research and large-scale clinical trials to validate the efficacy of these approaches.

Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit

BACKGROUND

Sepsis-associated acute kidney injury (AKI) is a leading comorbidity in admissions to the intensive care unit. While a gold standard definition exists, it remains imperfect and does not allow for the timely identification of patients in the setting of critical illness. This review will discuss the use of biochemical and electronic biomarkers to allow for prognostic and predictive enrichment of patients with sepsis-associated AKI over and above the use of serum creatinine and urine output.

SUMMARY

Current data suggest that several biomarkers are capable of identifying patients with sepsis at risk for the development of severe AKI and other associated morbidity. This review discusses these data and these biomarkers in the setting of sub-phenotyping and endotyping sepsis-associated AKI. While not all these tests are widely available and some require further validation, in the near future we anticipate several new tools to help nephrologists and other providers better care for patients with sepsis-associated AKI.

KEY MESSAGES

Predictive and prognostic enrichment using both traditional biomarkers and novel biomarkers in the setting of sepsis can identify subsets of patients with either similar outcomes or similar pathophysiology, respectively. Novel biomarkers can identify kidney injury in patients without consensus definition AKI (e.g., changes in creatinine or urine output) and can predict other adverse outcomes (e.g., severe consensus definition AKI, inpatient mortality). Finally, emerging artificial intelligence and machine learning-derived risk models are able to predict sepsis-associated AKI in critically ill patients using advanced learning techniques and several laboratory and vital sign measurements.

Sepsis and acute respiratory failure in patients with cancer: how can we improve care and outcomes even further?

PURPOSE OF REVIEW

Care and outcomes of critically ill patients with cancer have improved over the past decade. This selective review will discuss recent updates in sepsis and acute respiratory failure among patients with cancer, with particular focus on important opportunities to improve outcomes further through attention to phenotyping, predictive analytics, and improved outcome measures.

RECENT FINDINGS

The prevalence of cancer diagnoses in intensive care units (ICUs) is nontrivial and increasing. Sepsis and acute respiratory failure remain the most common critical illness syndromes affecting these patients, although other complications are also frequent. Recent research in oncologic sepsis has described outcome variation - including ICU, hospital, and 28-day mortality - across different types of cancer (e.g., solid vs. hematologic malignancies) and different sepsis definitions (e.g., Sepsis-3 vs. prior definitions). Research in acute respiratory failure in oncology patients has highlighted continued uncertainty in the value of diagnostic bronchoscopy for some patients and in the optimal respiratory support strategy. For both of these syndromes, specific challenges include multifactorial heterogeneity (e.g. in etiology and/or underlying cancer), delayed recognition of clinical deterioration, and complex outcomes measurement.

SUMMARY

Improving outcomes in oncologic critical care requires attention to the heterogeneity of cancer diagnoses, timely recognition and management of critical illness, and defining appropriate ICU outcomes.

Trans-Omics analysis of post injury thrombo-inflammation identifies endotypes and trajectories in trauma patients

Understanding and managing the complexity of trauma-induced thrombo-inflammation necessitates an innovative, data-driven approach. This study leveraged a trans-omics analysis of longitudinal samples from trauma patients to illuminate molecular endotypes and trajectories that underpin patient outcomes, transcending traditional demographic and physiological characterizations. We hypothesize that trans-omics profiling reveals underlying clinical differences in severely injured patients that may present with similar clinical characteristics but ultimately have very different responses to treatment and clinical outcomes. Here we used proteomics and metabolomics to profile 759 of longitudinal plasma samples from 118 patients at 11 time points and 97 control subjects. Results were used to define distinct patient states through data reduction techniques. The patient groups were stratified based on their shock severity and injury severity score, revealing a spectrum of responses to trauma and treatment that are fundamentally tied to their unique underlying biology. Ensemble models were then employed, demonstrating the predictive power of these molecular signatures with area under the receiver operating curves of 80 to 94% for key outcomes such as INR, ICU-free days, ventilator-free days, acute lung injury, massive transfusion, and death. The molecularly defined endotypes and trajectories provide an unprecedented lens to understand and potentially guide trauma patient management, opening a path towards precision medicine. This strategy presents a transformative framework that aligns with our understanding that trauma patients, despite similar clinical presentations, might harbor vastly different biological responses and outcomes.

Acute kidney injury in neurocritical care

Approximately 20% of patients with acute brain injury (ABI) also experience acute kidney injury (AKI), which worsens their outcomes. The metabolic and inflammatory changes associated with AKI likely contribute to prolonged brain injury and edema. As a result, recognizing its presence is important for effectively managing ABI and its sequelae. This review discusses the occurrence and effects of AKI in critically ill adults with neurological conditions, outlines potential mechanisms connecting AKI and ABI progression, and highlights AKI management principles. Tailored approaches include optimizing blood pressure, managing intracranial pressure, adjusting medication dosages, and assessing the type of administered fluids. Preventive measures include avoiding nephrotoxic drugs, improving hemodynamic and fluid balance, and addressing coexisting AKI syndromes. ABI patients undergoing renal replacement therapy (RRT) are more susceptible to neurological complications. RRT can negatively impact cerebral blood flow, intracranial pressure, and brain tissue oxygenation, with effects tied to specific RRT methods. Continuous RRT is favored for better hemodynamic stability and lower risk of dialysis disequilibrium syndrome. Potential RRT modifications for ABI patients include adjusted dialysate and blood flow rates, osmotherapy, and alternate anticoagulation methods. Future research should explore whether these strategies enhance outcomes and if using novel AKI biomarkers can mitigate AKI-related complications in ABI patients.

Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes

RATIONALE & OBJECTIVE

Acute kidney injury (AKI) is a heterogeneous clinical syndrome with varying causes, pathophysiology, and outcomes. We incorporated plasma and urine biomarker measurements to identify AKI subgroups (subphenotypes) more tightly linked to underlying pathophysiology and long-term clinical outcomes.

STUDY DESIGN

Multicenter cohort study.

SETTING & PARTICIPANTS

769 hospitalized adults with AKI matched with 769 without AKI, enrolled from December 2009 to February 2015 in the ASSESS-AKI Study.

PREDICTORS

29 clinical, plasma, and urinary biomarker parameters used to identify AKI subphenotypes.

OUTCOME

Composite of major adverse kidney events (MAKE) with a median follow-up period of 4.7 years.

ANALYTICAL APPROACH

Latent class analysis (LCA) and k-means clustering were applied to 29 clinical, plasma, and urinary biomarker parameters. Associations between AKI subphenotypes and MAKE were analyzed using Kaplan-Meier curves and Cox proportional hazard models.

RESULTS

Among 769 AKI patients both LCA and k-means identified 2 distinct AKI subphenotypes (classes 1 and 2). The long-term risk for MAKE was higher with class 2 (adjusted HR, 1.41 [95% CI, 1.08-1.84]; P=0.01) compared with class 1, adjusting for demographics, hospital level factors, and KDIGO stage of AKI. The higher risk of MAKE among class 2 was explained by a higher risk of long-term chronic kidney disease progression and dialysis. The top variables that were different between classes 1 and 2 included plasma and urinary biomarkers of inflammation and epithelial cell injury; serum creatinine ranked 20th out of the 29 variables for differentiating classes.

LIMITATIONS

A replication cohort with simultaneously collected blood and urine sampling in hospitalized adults with AKI and long-term outcomes was unavailable.

CONCLUSIONS

We identify 2 molecularly distinct AKI subphenotypes with differing risk of long-term outcomes, independent of the current criteria to risk stratify AKI. Future identification of AKI subphenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI.

PLAIN-LANGUAGE SUMMARY

Acute kidney injury (AKI) occurs commonly in hospitalized patients and is associated with high morbidity and mortality. The AKI definition lumps many different types of AKI together, but subgroups of AKI may be more tightly linked to the underlying biology and clinical outcomes. We used 29 different clinical, blood, and urinary biomarkers and applied 2 different statistical algorithms to identify AKI subtypes and their association with long-term outcomes. Both clustering algorithms identified 2 AKI subtypes with different risk of chronic kidney disease, independent of the serum creatinine concentrations (the current gold standard to determine severity of AKI). Identification of AKI subtypes may facilitate linking therapies to underlying biology to prevent long-term consequences after AKI.

Precision management of acute kidney injury in the intensive care unit: current state of the art

Acute kidney injury (AKI) is a prototypical example of a common syndrome in critical illness defined by consensus. The consensus definition for AKI, traditionally defined using only serum creatinine and urine output, was needed to standardize the description for epidemiology and to harmonize eligibility for clinical trials. However, AKI is not a simple disease, but rather a complex and multi-factorial syndrome characterized by a wide spectrum of pathobiology. AKI is now recognized to be comprised of numerous sub-phenotypes that can be discriminated through shared features such as etiology, prognosis, or common pathobiological mechanisms of injury and damage. The characterization of sub-phenotypes can serve to enable prognostic enrichment (i.e., identify subsets of patients more likely to share an outcome of interest) and predictive enrichment (identify subsets of patients more likely to respond favorably to a given therapy). Existing and emerging biomarkers will aid in discriminating sub-phenotypes of AKI, facilitate expansion of diagnostic criteria, and be leveraged to realize personalized approaches to management, particularly for recognizing treatment-responsive mechanisms (i.e., endotypes) and targets for intervention (i.e., treatable traits). Specific biomarkers (e.g., serum renin; olfactomedin 4 (OLFM4); interleukin (IL)-9) may further enable identification of pathobiological mechanisms to serve as treatment targets. However, even non-specific biomarkers of kidney injury (e.g., neutrophil gelatinase-associated lipocalin, NGAL; [tissue inhibitor of metalloproteinases 2, TIMP2]·[insulin like growth factor binding protein 7, IGFBP7]; kidney injury molecule 1, KIM-1) can direct greater precision management for specific sub-phenotypes of AKI. This review will summarize these evolving concepts and recent innovations in precision medicine approaches to the syndrome of AKI in critical illness, along with providing examples of how they can be leveraged to guide patient care.

Subphenotypes of Pediatric Acute Kidney Injury

Acute kidney injury (AKI) is seen frequently in hospitalized patients and is associated with increased risk of mortality and adverse short- and long-term renal and systemic complications. Emerging data suggest that AKI is a heterogenous syndrome with a variety of underlying causes, predisposing illnesses, and range of clinical trajectories and outcomes. This mini-review aims to discuss emerging AKI subphenotype classifications as our understanding of the heterogeneity and underlying pathophysiology has improved.

Prognostic and predictive value of endothelial dysfunction biomarkers in sepsis-associated acute kidney injury: risk-stratified analysis from a prospective observational cohort of pediatric septic shock

BACKGROUND

Sepsis-associated acute kidney injury (SA-AKI) is associated with high morbidity, with no current therapies available beyond continuous renal replacement therapy (CRRT). Systemic inflammation and endothelial dysfunction are key drivers of SA-AKI. We sought to measure differences between endothelial dysfunction markers among children with and without SA-AKI, test whether this association varied across inflammatory biomarker-based risk strata, and develop prediction models to identify those at highest risk of SA-AKI.

METHODS

Secondary analyses of prospective observational cohort of pediatric septic shock. Primary outcome of interest was the presence of ≥ Stage II KDIGO SA-AKI on day 3 based on serum creatinine (D3 SA-AKI SCr). Biomarkers including those prospectively validated to predict pediatric sepsis mortality (PERSEVERE-II) were measured in Day 1 (D1) serum. Multivariable regression was used to test the independent association between endothelial markers and D3 SA-AKI SCr. We conducted risk-stratified analyses and developed prediction models using Classification and Regression Tree (CART), to estimate risk of D3 SA-AKI among prespecified subgroups based on PERSEVERE-II risk.

RESULTS

A total of 414 patients were included in the derivation cohort. Patients with D3 SA-AKI SCr had worse clinical outcomes including 28-day mortality and need for CRRT. Serum soluble thrombomodulin (sTM), Angiopoietin-2 (Angpt-2), and Tie-2 were independently associated with D3 SA-AKI SCr. Further, Tie-2 and Angpt-2/Tie-2 ratios were influenced by the interaction between D3 SA-AKI SCr and risk strata. Logistic regression demonstrated models predictive of D3 SA-AKI risk performed optimally among patients with high- or intermediate-PERSEVERE-II risk strata. A 6 terminal node CART model restricted to this subgroup of patients had an area under the receiver operating characteristic curve (AUROC) 0.90 and 0.77 upon tenfold cross-validation in the derivation cohort to distinguish those with and without D3 SA-AKI SCr and high specificity. The newly derived model performed modestly in a unique set of patients (n = 224), 84 of whom were deemed high- or intermediate-PERSEVERE-II risk, to distinguish those patients with high versus low risk of D3 SA-AKI SCr.

CONCLUSIONS

Endothelial dysfunction biomarkers are independently associated with risk of severe SA-AKI. Pending validation, incorporation of endothelial biomarkers may facilitate prognostic and predictive enrichment for selection of therapeutics in future clinical trials among critically ill children.

Genome-wide Association Study for AKI

Key Points

Two genetic variants in the DISP1-TLR5 gene locus were associated with risk of AKI. DISP1 and TLR5 were differentially regulated in kidney biopsy tissue from patients with AKI compared with no AKI.

Background

Although common genetic risks for CKD are well established, genetic factors influencing risk for AKI in hospitalized patients are poorly understood.

Methods

We conducted a genome-wide association study in 1369 participants in the Assessment, Serial Evaluation, and Subsequent Sequelae of AKI Study; a multiethnic population of hospitalized participants with and without AKI matched on demographics, comorbidities, and kidney function before hospitalization. We then completed functional annotation of top-performing variants for AKI using single-cell RNA sequencing data from kidney biopsies in 12 patients with AKI and 18 healthy living donors from the Kidney Precision Medicine Project.

Results

No genome-wide significant associations with AKI risk were found in Assessment, Serial Evaluation, and Subsequent Sequelae of AKI (P < 5×10 −8 ). The top two variants with the strongest association with AKI mapped to the dispatched resistance-nodulation-division (RND) transporter family member 1 (DISP1) gene and toll-like receptor 5 (TLR5) gene locus, rs17538288 (odds ratio, 1.55; 95% confidence interval, 1.32 to 182; P = 9.47×10 −8 ) and rs7546189 (odds ratio, 1.53; 95% confidence interval, 1.30 to 1.81; P = 4.60×10 −7 ). In comparison with kidney tissue from healthy living donors, kidney biopsies in patients with AKI showed differential DISP1 expression in proximal tubular epithelial cells (adjusted P = 3.9× 10−2) and thick ascending limb of the loop of Henle (adjusted P = 8.7× 10−3) and differential TLR5 gene expression in thick ascending limb of the loop of Henle (adjusted P = 4.9× 10−30).

Conclusions

AKI is a heterogeneous clinical syndrome with various underlying risk factors, etiologies, and pathophysiology that may limit the identification of genetic variants. Although no variants reached genome-wide significance, we report two variants in the intergenic region between DISP1 and TLR5 , suggesting this region as a novel risk for AKI susceptibility.

Subgroups of Patients with Distinct Health Utility Profiles after AKI

Key Points

Health utility profiles can be identified at 60 days after AKI. Patient subgroups with distinct health utility profiles have different characteristics at index hospitalization and outcomes at 1 year. These profiles may be useful when considering resources to improve the physical and emotional health of patients after AKI.

Background

A large amount of interindividual variability exists in health-related quality of life outcomes after AKI. This study aimed to determine whether subgroups of early AKI survivors could be identified on the basis of distinct health utility impairment profiles ascertained at 60 days after AKI and whether these subgroups differed in clinical and biomarker characteristics at index hospitalization and outcomes at 1-year follow-up.

Methods

This retrospective analysis used data from the Biologic Markers of Renal Recovery for the Kidney study, an observational subcohort of the Acute Renal Failure Trial Network study. Of 402 patients who survived to 60 days after AKI, 338 completed the Health Utility Index 3 survey, which measures impairments in eight health attributes. Latent class analysis was used to identify subgroups of patients with distinct health utility profiles.

Results

Three subgroups with distinct health utility impairment profiles were identified: Low (28% of participants), Moderate (58%), and High (14%) with a median of one, four, and six impairments across the eight health attributes at 60 days after AKI, respectively. Patient subgroups differed in weight, history of cerebrovascular disease, intensity of dialysis, hospital length of stay, and dialysis dependence. Serum creatinine and blood urea nitrogen at index hospitalization did not differ among the three subgroups. The High impairment subgroup had higher levels of IL-6 and soluble TNF receptor 2 at study day 1. The three subgroups had different 1-year mortality rates: 5% in the Low, 21% in the Moderate, and 52% in the High impairment subgroup.

Conclusion

Patient subgroups with distinct health utility impairment profiles can be identified 60 days after AKI. These subgroups have different characteristics at index hospitalization. A higher level of impairment at 60 days was associated with decreased survival.

Association of Trauma Molecular Endotypes With Differential Response to Transfusion Resuscitation Strategies

Importance

It is not clear which severely injured patients with hemorrhagic shock may benefit most from a 1:1:1 vs 1:1:2 (plasma:platelets:red blood cells) resuscitation strategy. Identification of trauma molecular endotypes may reveal subgroups of patients with differential treatment response to various resuscitation strategies.

Objective

To derive trauma endotypes (TEs) from molecular data and determine whether these endotypes are associated with mortality and differential treatment response to 1:1:1 vs 1:1:2 resuscitation strategies.

Design, Setting, and Participants

This was a secondary analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) randomized clinical trial. The study cohort included individuals with severe injury from 12 North American trauma centers. The cohort was taken from the participants in the PROPPR trial who had complete plasma biomarker data available. Study data were analyzed on August 2, 2021, to October 25, 2022.

Exposures

TEs identified by K-means clustering of plasma biomarkers collected at hospital arrival.

Main Outcomes and Measures

An association between TEs and 30-day mortality was tested using multivariable relative risk (RR) regression adjusting for age, sex, trauma center, mechanism of injury, and injury severity score (ISS). Differential treatment response to transfusion strategy was assessed using an RR regression model for 30-day mortality by incorporating an interaction term for the product of endotype and treatment group adjusting for age, sex, trauma center, mechanism of injury, and ISS.

Results

A total of 478 participants (median [IQR] age, 34.5 [25-51] years; 384 male [80%]) of the 680 participants in the PROPPR trial were included in this study analysis. A 2-class model that had optimal performance in K-means clustering was found. TE-1 (n = 270) was characterized by higher plasma concentrations of inflammatory biomarkers (eg, interleukin 8 and tumor necrosis factor α) and significantly higher 30-day mortality compared with TE-2 (n = 208). There was a significant interaction between treatment arm and TE for 30-day mortality. Mortality in TE-1 was 28.6% with 1:1:2 treatment vs 32.6% with 1:1:1 treatment, whereas mortality in TE-2 was 24.5% with 1:1:2 treatment vs 7.3% with 1:1:1 treatment (P for interaction = .001).

Conclusions and Relevance

Results of this secondary analysis suggest that endotypes derived from plasma biomarkers in trauma patients at hospital arrival were associated with a differential response to 1:1:1 vs 1:1:2 resuscitation strategies in trauma patients with severe injury. These findings support the concept of molecular heterogeneity in critically ill trauma populations and have implications for tailoring therapy for patients at high risk for adverse outcomes.

Latent class analysis-derived classification improves the cancer-specific death stratification of molecular subtyping in colorectal cancer

The molecular subtypes of colorectal cancer (CRC) represent a comprehensive dissection of CRC heterogeneity. However, molecular feature-based classification systems have limitations in accurately prognosticating stratification due to the inability to distinguish cancer-specific deaths. This study aims to establish a classification system that bridges clinical characteristics, cause-specific deaths, and molecular features. We adopted latent class analysis (LCA) on 491,107 first primary CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database to reveal hidden profiles of CRC. The LCA-derived classification scheme was further applied to The Cancer Genome Atlas (TCGA) to assess its effectiveness in improving the accurate stratification of molecular-based subtypes of CRC. Four classes were identified based on latent class analysis integrating demographic and clinicopathological information of CRC patients. The LCA-derived Class 1 (LCAC1) and the LCAC2 showed a high risk of dying from non-CRC, while patients in LCAC3 had a risk of dying from CRC 1.41 times that of LCAC1 (95% confidence interval [CI] = 1.39-1.43). LCAC4 had the lowest probability to die from non-CRC (hazard ratio [HR] = 0.22, 95% CI = 0.21-0.24) compared with LCAC1. Since the LCA-derived classification can identify patients susceptible to CRC-specific death, adjusting for this classification allows molecular-based subtypes to achieve more accurate survival stratification. We provided a classification system capable of distinguish CRC-specific death, which will improve the accuracy of consensus molecular subtypes for CRC patients' survival stratification. Further studies are warranted to confirm the molecular features of LCA-derived classification to inform potential therapeutic strategies and treatment recommendations.

Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes

BACKGROUND

AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition.

METHODS

We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models.

RESULTS

We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events.

CONCLUSIONS

We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.

Cardiovascular Subphenotypes in ARDS: Diagnostic and Therapeutic Implications and Overlap with Other ARDS Subphenotypes

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous clinical condition. Shock is a poor prognostic sign in ARDS, and heterogeneity in its pathophysiology may be a barrier to its effective treatment. Although right ventricular dysfunction is commonly implicated, there is no consensus definition for its diagnosis, and left ventricular function is neglected. There is a need to identify the homogenous subgroups within ARDS, that have a similar pathobiology, which can then be treated with targeted therapies. Haemodynamic clustering analyses in patients with ARDS have identified two subphenotypes of increasingly severe right ventricular injury, and a further subphenotype of hyperdynamic left ventricular function. In this review, we discuss how phenotyping the cardiovascular system in ARDS may align with haemodynamic pathophysiology, can aid in optimally defining right ventricular dysfunction and can identify tailored therapeutic targets for shock in ARDS. Additionally, clustering analyses of inflammatory, clinical and radiographic data describe other subphenotypes in ARDS. We detail the potential overlap between these and the cardiovascular phenotypes.

Overcoming barriers in the design and implementation of clinical trials for acute kidney injury: a report from the 2020 Kidney Disease Clinical Trialists meeting

Acute kidney injury (AKI) is a growing epidemic and is independently associated with increased risk of death, chronic kidney disease (CKD) and cardiovascular events. Randomized-controlled trials (RCTs) in this domain are notoriously challenging and many clinical studies in AKI have yielded inconclusive findings. Underlying this conundrum is the inherent heterogeneity of AKI in its etiology, presentation and course. AKI is best understood as a syndrome and identification of AKI subphenotypes is needed to elucidate the disease's myriad etiologies and to tailor effective prevention and treatment strategies. Conventional RCTs are logistically cumbersome and often feature highly selected patient populations that limit external generalizability and thus alternative trial designs should be considered when appropriate. In this narrative review of recent developments in AKI trials based on the Kidney Disease Clinical Trialists (KDCT) 2020 meeting, we discuss barriers to and strategies for improved design and implementation of clinical trials for AKI patients, including predictive and prognostic enrichment techniques, the use of pragmatic trials and adaptive trials.

The outcome of acute kidney injury substages based on urinary cystatin C in critically ill children

BACKGROUND

The concept of acute kidney injury (AKI) substages has been recommended to better phenotype AKI and identify high-risk patient groups and therefore improve the diagnostic accuracy of AKI. However, there remains a gap between the recommendation and the clinical application. The study aimed to explore the incidence of AKI substages based on a sensitive AKI biomarker of urinary cystatin C (uCysC), and to determine whether AKI substages were relevant with respect to outcome in critically ill children.

RESULTS

The multicenter cohort study enrolled 793 children in pediatric intensive care unit (PICU) of four tertiary hospitals in China. Children were classified as non-AKI, sub-AKI and AKI substages A and B according to uCysC level at PICU admission. Sub-AKI was defined by admission uCysC level ≥ 1.26 mg/g uCr in children not meeting the KDIGO criteria of AKI. In children who fulfilled KDIGO criteria, those with uCysC < 1.26 was defined as AKI substage A, and with ≥ 1.26 defined as AKI substage B. The associations of AKI substages with 30-day PICU mortality were assessed. 15.6% (124/793) of patients met the definition of sub-AKI. Of 180 (22.7%) patients with AKI, 90 (50%) had uCysC-positive AKI substage B and were more likely to have classical AKI stage 3, compared to substage A. Compared to non-AKI, sub-AKI and AKI substages A and B were risk factors significantly associated with mortality, and the association of sub-AKI (adjusted hazard ratio HR = 2.42) and AKI substage B (adjusted HR = 2.83) with mortality remained significant after adjustment for confounders. Moreover, AKI substage B had increased risks of death as compared with sub-AKI (HR = 3.10) and AKI substage A (HR = 3.19).

CONCLUSIONS

Sub-AKI defined/based on uCysC occurred in 20.2% of patients without AKI and was associated with a risk of death close to patients with AKI substage A. Urinary CysC-positive AKI substage B occurred in 50% of AKI patients and was more likely to have classical AKI stage 3 and was associated with the highest risk of mortality.