2.1. Search strategy and selection criteria

This systematic review was rigorously structured in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [47], emphasizing a commitment to thoroughness and transparency. Adhering to the protocol outlined in the PRISMA Protocols (PRISMA-P) statement [48], we developed a detailed research protocol, which was then duly registered with PROSPERO (498929). This registration underscores our dedication to conducting this review with systematic precision and methodological rigor.

Our approach to exploring the relevant literature was comprehensive and well-organized. We conducted in-depth searches across several reputable databases, including Embase.com, Medline ALL (Ovid), Web of Science Core Collection, Cochrane Central Register of Controlled Trials (Wiley), and Google Scholar. Our latest search, carried out on September 17, 2023, was designed to capture the most recent and pertinent studies in the field. The search strategy was intricately formulated, merging medical subject headings (MeSH) and a set of carefully chosen keywords that are relevant to the early detection and management of acute kidney injury (Table 1). This strategy was crafted to encompass various dimensions of the topic, such as pathophysiology, risk factors, and diverse management strategies, aiming for an all-encompassing review of the subject matter.

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Table 1. Studies identified in the literature search.

https://doi.org/10.1371/journal.pone.0311755.t001

2.2. Eligibility screening

Upon the elimination of duplicates, our review process began with a thorough screening of titles and abstracts, followed by an in-depth evaluation of full-text articles. We included original research articles, systematic reviews, meta-analyses, and clinical trials that involved human subjects, specifically focusing on the early detection and management of acute kidney injury (AKI) using novel biomarkers. The scope of our review was centered on studies involving patients diagnosed with or at risk of AKI and examining new biomarkers that could potentially aid in early detection, prognosis, or management of this condition. Studies that compared these novel biomarkers against existing ones, placebos, or other standard care practices were also included. Key outcomes of interest included the efficacy and accuracy of these biomarkers in detecting AKI, their impact on patient outcomes, changes in management strategies based on these biomarkers, and any associated adverse effects or complications.

Exclusion criteria encompassed case reports, case series, abstracts, letters, editorials, and conference proceedings, as well as animal studies or in vitro research. Studies that did not specifically focus on AKI or its novel biomarkers, or those involving patients with underlying renal conditions that could interfere with the biomarker’s accuracy, were excluded. Additionally, research lacking detailed descriptions of the biomarkers or methodologies, or those examining well-established biomarkers not considered novel or innovative, were not considered. Studies without appropriate comparison groups, insufficient data for meaningful analysis, or those not contributing directly to the understanding of novel biomarkers in AKI were also excluded. Lastly, non-English language studies without available translations were omitted. These criteria were meticulously applied to ensure that our review remained focused on novel biomarkers in the early detection and management of acute kidney injury, and they were refined as needed to align with our research objectives and the scope of the available literature.

2.3.Data extraction

Data extraction was a pivotal component of this systematic review, specifically focusing on gathering relevant data from studies pertinent to novel biomarkers for the early detection and management of acute kidney injury (AKI). The objective during this phase was to methodically extract key information that would illuminate critical aspects of each study, detail the interventions undertaken, and accurately document the outcome measures pertinent to AKI biomarkers.

The extraction process involved a detailed analysis of each selected study, concentrating on the following essential elements:

• Study Characteristics: Comprehensive details such as the study’s design, sample size, geographic location, publication date, and demographic characteristics of the participants were systematically recorded. This provided context for the findings and helped in evaluating the study’s applicability to our review.
• Intervention Details: For studies exploring new biomarkers, precise descriptions of these biomarkers, including their nature, detection methods, and any comparative analysis with existing biomarkers, were extracted. This covered information on biomarker specificity, sensitivity, and any novel methodologies employed in their identification.
• Outcome Measures: The extraction process also included identifying and recording specific outcome measures used to evaluate the effectiveness of the novel biomarkers in detecting and managing AKI. These measures might include the accuracy of early detection, improvements in patient outcomes, and any associated risks or benefits.

In instances where crucial data were missing or unclear, we made concerted efforts to contact the study authors for clarification. This ensured that our review was based on the most complete and accurate data possible.

Additionally, we were vigilant in assessing any potential overlap or duplicity in patient cohorts across studies. Where necessary, we engaged in direct communication with the authors of the studies to clarify any uncertainties. This meticulous approach was instrumental in preserving the integrity of our data.

Our initial search resulted in 4,521 documents. After removing duplicates, 531 articles remained for preliminary screening based on titles and abstracts. Of these, 106 articles were excluded at this stage, leaving 425 papers for further eligibility assessment. Following a thorough full-text review, 17 studies were ultimately selected for inclusion in this review [49–65]. The process of study selection is detailed in a flowchart, prepared in accordance with PRISMA guidelines, as shown in Fig 1.

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Fig 1.

https://doi.org/10.1371/journal.pone.0311755.g001

2.4. Quality assessment

The cornerstone of our systematic review on novel biomarkers for acute kidney injury (AKI) is a thorough and rigorous assessment of the methodological quality and risk of bias in the included studies. Recognizing the importance of this evaluation, we have meticulously analysed the quality and bias of each study to ensure the credibility and validity of our findings.

To achieve this, we utilized a structured approach with a modified version of the ROBVIS2 tool. ROBVIS2, developed during the Evidence Synthesis Hackathon, is an advanced web application based on the well-established ROBVIS R package. This tool is highly regarded in the systematic review community for its effectiveness in evaluating study quality and bias [66].

We independently evaluated each study, focusing on crucial aspects like study design, participant selection, blinding, data collection methods, and the management of missing data. This rigorous analysis was crucial to determine the methodological soundness and to identify potential biases in the studies.

Discrepancies in the evaluation process were addressed with utmost diligence and transparency. Any disagreements or uncertainties regarding the methodological quality or risk of bias were resolved through a consensus-based approach, involving detailed discussions with fellow researchers, leading to a unified decision on the study evaluations.

2.5. Data analysis

In analysing the data collected on novel biomarkers for AKI, our review employed both narrative synthesis and thematic analysis to extract and interpret meaningful insights.

Narrative Synthesis: This qualitative approach forms the backbone of our analysis. It involves a systematic interpretation of findings from the included studies, aiming to provide more than a summary—a critical synthesis of evidence. This synthesis is designed to highlight key insights, trends, and implications relevant to the early detection and management of AKI using novel biomarkers. It contextualizes the findings, offering a comprehensive understanding that benefits healthcare practitioners and patients.

Thematic Analysis: Complementing the narrative synthesis, thematic analysis was used to uncover patterns and themes in the data. This qualitative technique focuses on identifying common themes, particularly concerning pathophysiology, risk factors, and the effectiveness of different biomarkers in AKI management. The thematic analysis helps in understanding the underlying relationships, variations, and trends within these themes, providing a deeper insight into the effectiveness and challenges of these novel biomarkers.

Together, narrative synthesis and thematic analysis provide a multifaceted examination of the evidence. This approach allows us to transcend simple data aggregation and delve into a deeper understanding of the intricacies of novel biomarkers in the early detection and management of AKI. Our systematic review thus aims to be a valuable resource for healthcare professionals, researchers, and patients interested in the advanced detection and management strategies for AKI.

3.1. The quality assessment

The methodological integrity of the 17 studies included in this systematic review was scrutinized through a risk of bias assessment across several domains: the randomization process, adherence to intended interventions, management of missing outcome data, accuracy of outcome measurement, and the selection of reported results. This comprehensive assessment is visualized in Fig 2 and Table 3. The vast majority of studies demonstrated a low risk of bias across these domains, which signifies a strong commitment to methodological rigor, thereby reducing the potential for systemic errors or result bias.

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Fig 2.

https://doi.org/10.1371/journal.pone.0311755.g002

In particular, studies such as those by Göcze et al. (2018), Zarbock et al. (2021), and Parikh et al. (2020) maintained a low risk of bias in every evaluated category [50, 53, 54]. This level of methodological excellence provides a solid foundation for the validity of their findings and contributes to the robust evidence base for this review.

Conversely, a group of studies, specifically those by Thilo von Groote et al. (2022), Stefan Herget-Rosenthal et al. (2004), and Bullen et al. (2019), among others, indicated some concerns regarding the management of missing outcome data—a critical aspect that could influence the reliability of the results [49, 51, 56–61, 67]. These concerns necessitate a cautious interpretation of these studies’ outcomes and underscore the need for meticulous reporting and methodological transparency in the execution of randomized controlled trials.

Despite these issues, the overall risk of bias was deemed low for the bulk of the studies. This suggests that while certain methodological challenges were noted, they were not judged to significantly impair the conclusions of the studies. Such an evaluation highlights the importance of a judicious and discerning analysis of the evidence, acknowledging both the strengths and the possible limitations of the data reported.

3.2. Main outcomes

Based on the detailed outcomes provided in the extraction Tables 2 and 3, four main themes can be identified.

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Table 2. Risk of bias table.

https://doi.org/10.1371/journal.pone.0311755.t002

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Table 3. Data extraction table.

https://doi.org/10.1371/journal.pone.0311755.t003

4.1. Biomarker efficacy in predicting AKI risk and severity

Acute kidney injury (AKI) is a frequent clinical complication associated with increased morbidity, mortality and healthcare costs [68]. Early identification of patients at high risk and accurate prediction of AKI severity is crucial for timely intervention and improved outcomes. Traditional markers like serum creatinine are delayed in reflecting renal injury [69]. Novel urinary and plasma biomarkers show promise in facilitating very early detection of AKI, as well as differentiating transient from persistent injury [70].

In our initial discussion of the predictive utility of biomarkers for post-operative AKI, it is important to acknowledge that while Parikh et al. [71] report a significant association between elevated biomarker levels and AKI development post-surgery, this relationship is more complex when considered in the broader context of surgical interventions. Other studies [72–74] provide evidence of varying predictive values depending on when biomarker levels are measured relative to the operation. This variability suggests that the predictive power of biomarkers can differ significantly based on the timing of assessment and the patient’s physiological status at each point. The Acute Disease Quality Initiative (ADQI) consensus statement by Ostermann et al. (2020) further clarifies this issue, recommending that the clinical interpretation of biomarker data should be contextually dependent, taking into account the specific circumstances and timing of measurement [75].

The multicenter Sapphire study found that a combination of plasma TIMP-2 and IGFBP7, when measured within 12–24 hours of ICU admission, was highly predictive of moderate to severe AKI within 12 hours, with an AUC of 0.78. These novel biomarkers allow for the identification of high-risk patients days before a rise in creatinine, thus enabling timely preventive interventions [76–79]. These novel biomarkers enable identification of high-risk patients days before a rise in creatinine, allowing timely preventive interventions [37, 80].

Serial measurement of novel biomarkers helps differentiate transient AKI from injuries requiring renal replacement therapy (RRT) [81]. For instance, marked elevations and delayed decline of plasma/urinary NGAL within the first 48 hours of cardiothoracic surgery predicts progression to severe AKI requiring RRT [82]. Similarly, higher urinary KIM-1 levels measured at 24 and 48 hours are independently associated with need for RRT in septic AKI [83]. The clinical TEAM study reported that a single plasma TIMP-2 and IGFBP7 measurement accurately identified patients requiring RRT, with an AUC of 0.80–0.88 [84]. Combining biomarkers also enhances prognostic accuracy—urinary NGAL and KIM-1 scores measured at admission synergistically predicted those needing RRT after cardiac surgery [85]. Novel biomarkers enable early identification of patients with persistent injury who may benefit from proactive nephrology referral and consideration of early RRT [86].

In addition to predicting AKI development and severity, emerging biomarkers provide valuable information on mortality risk. Several studies have shown that higher urinary/plasma NGAL levels independently predict increased 30-day, 60-day and 1-year mortality in AKI [87]. Elevated biomarker levels at diagnosis of AKI and their persistence over time are associated with worse survival [88]. The Sapphire investigation reported that elevated plasma TIMP-2 and IGFBP7 identified patients with moderate-severe AKI facing the highest mortality risk with an AUC of 0.79–0.82 [89]. Novel biomarker assessment early in the course of AKI aids identification of individuals requiring closer monitoring and more aggressive management to potentially impact outcomes [90]. novel urinary and plasma biomarkers NGAL, KIM-1, TIMP-2, IGFBP7 demonstrate superior efficacy compared to creatinine in enabling very early identification of high-risk patients ahead of AKI development as well as accurate prediction of AKI severity, need for RRT and mortality risk [91]. They facilitate targeted prophylactic interventions and prognostic-guided clinical decision making. While validation across diverse settings is still ongoing, emerging evidence indicates that biomarker incorporation holds promise for reducing AKI incidence, progression and improving patient outcomes through timely, precision risk stratification and management. Future studies should evaluate whether biomarker-guided preventive strategies can indeed translate to reduced AKI burden and improved survival [92].

4.2. Improvement in clinical management of AKI

The diagnosis of AKI is usually made based on the rise in serum creatinine and decrease in urine output as per the Kidney Disease Improving Global Outcomes (KDIGO) criteria [1]. Novel tubular injury biomarkers NGAL, KIM-1, and IL-18 have shown to detect AKI much earlier, within hours of renal insult, before creatinine rise occurs [93]. Several studies have demonstrated that urine and plasma levels of these biomarkers rise significantly earlier compared to creatinine in patients developing AKI after cardiac surgery, major trauma, and sepsis. Measurement of NGAL, KIM-1, IL-18 either alone or in combination in high-risk patients can aid in earlier diagnosis of AKI [71, 94, 95].

Identification of patients at high risk of adverse kidney outcomes is important for clinical decision making [96]. Novel biomarkers also offer accuracy in predicting severity and prognosis of AKI [97]. Studies have shown that elevated urinary and plasma NGAL levels on initial presentation or within 24 hours of ICU admission are independently associated with prolonged renal replacement therapy, increased risk of dialysis dependence, and mortality in AKI patients [98, 99]. Similarly, higher levels of urinary KIM-1 and IL-18 independently predict need for renal replacement therapy and mortality [100]. Assessing NGAL, KIM-1, or IL-18 levels on diagnosis of AKI helps risk stratify patients and guides the intensity of renal monitoring and management [23]. Patients with very high biomarker levels at diagnosis are at highest risk for worse renal and patient-centered outcomes and may benefit from early nephrology referral and aggressive renal protective strategies [101].

Identification of novel AKI biomarkers also assists in customizing clinical management according to individual patient risk. For patients diagnosed with AKI, urine/plasma biomarker assessment can play a role in important clinical decisions such as need for renal replacement therapy, frequency of monitoring, inotropic support, avoidance of nephrotoxic agents [102]. Patients with sustained elevation or rise in biomarker levels despite clinical management may require early renal replacement therapy initiation or nephrology referral compared to those with falling biomarker trends [103]. Tracking biomarker trends over time provides guidance on adequacy of current treatment strategy. Similarly, biomarker levels can monitor resolution of AKI and guide duration of close monitoring after renal recovery [103].

4.3. Biomarkers as therapeutic targets and prognostic tools

In recent years, significant progress has been made in identifying novel biomarkers that are reflective of physiological or pathological processes in the body [104]. Some of these biomarkers have potential to not only serve as diagnostic or prognostic indicators but also emerge as direct therapeutic targets [105].

Certain renal biomarkers like Neutrophil Gelatinase-Associated Lipocalin (NGAL) and Kidney Injury Molecule-1 (KIM-1) play active roles in kidney injury and repair processes. Given their direct involvement, they represent potential targets for preventing acute kidney injury (AKI) progression [106]. Early studies evaluating antibodies against NGAL showed renoprotective effects in animal AKI models. Currently, clinical trials are ongoing to test if anti-NGAL therapy can benefit high-risk patients undergoing cardiac surgery or contrast procedures [107]. KIM-1 has potential as a therapeutic target as well—preclinical research inhibiting KIM-1 signaling reduced AKI severity [108]. Current research provides mixed results on its protective capabilities. While NGAL is primarily regarded as a biomarker for early detection of acute kidney injury, some studies suggest it may also have a protective role due to its involvement in modulating inflammatory responses and minimizing oxidative stress in renal tubules [26, 109, 110]. However, further studies are necessary to fully elucidate its protective functions and clinical implications.

Some tubular injury biomarkers play active roles in AKI pathogenesis. Elevated early in injury, NGAL preserves tubular integrity and promotes repair [111]. Preclinical studies found anti-NGAL antibodies or inhibitors administered early after renal ischemia conferred renoprotection by blocking NGAL’s effects [112]. Currently, clinical trials are evaluating whether anti-NGAL therapy can benefit high-risk surgical or critical care patients. KIM-1, expressed on injured tubules, serves phagocytic and regenerative functions but may also promote maladaptive repair [113]. Blocking KIM-1 signaling reduced AKI in animal models. Targeting KIM-1 is a potential novel interventional strategy. Ongoing research explores other tubular proteins as modifiable targets [114]. For example, inhibiting TNF-like weak inducer of apoptosis (TWEAK) signaling showed reno-protective effects in preclinical AKI, warranting further investigation [115].

Higher urinary or plasma NGAL within 24 hrs of cardiac surgery or ICU admission independently predicts prolonged AKI, need for renal replacement therapy (RRT) and mortality risk [116]. Similarly, elevated urinary KIM-1 and IL-18 within the first week correlate with progression to RRT and worse survival. Combining multiple biomarker results enhances accuracy [117]. A "severity score" integrating urinary NGAL and KIM-1 early in AKI identified post-cardiac surgery patients requiring RRT with sensitivity of 88% and specificity of 71% [118].

Serial biomarker assessment guides need to escalate or de-escalate care. Continued elevation despite therapy signals refractory injury necessitating escalated RRT consideration to halt progression [119]. Biomarker decline denotes response and allows slower weaning of interventions [120]. Future research should evaluate if a multi-parameter prognostic model incorporating clinical and novel biomarker data enhances risk prediction and whether biomarker-guided personalized management improves outcomes including mortality, need for RRT and long-term renal recovery in AKI.

To expand on the association between biomarkers and types of renal injury (organic, functional, or obstructive), it is important to consider how different biomarkers like KIM-1 and L-FABP respond to varying injury mechanisms. For instance, KIM-1 is typically elevated in tubular damage due to its expression in proximal tubule cells following ischemic or nephrotoxic injury, which can be categorized under organic damage. Conversely, functional changes might not lead to elevated KIM-1 levels but could influence biomarkers like serum creatinine [14, 111, 121]. Understanding these associations helps in tailoring biomarker-based diagnostics to the underlying pathophysiology of kidney injury [122].

4.4. Challenges and limitations in biomarker research

The area of biomarker research has made significant advances in recent years, leading to discovery and characterization of several disease biomarkers with potential applications in early detection, diagnosis, prognosis and monitoring treatment response [123]. However, effective translation of novel biomarkers into routine clinical practice still faces multiple technical and practical challenges. This essay discusses some key limitations and hurdles that need to be addressed to realize the full potential of biomarkers in improving patient care and outcomes [124].

Rigorous analytical validation of any promising biomarker candidate is required before clinical use, to demonstrate analytical performance parameters like sensitivity, specificity, accuracy, precision and reproducibility under different conditions [125]. However, many early biomarker studies often lack comprehensive validation. Suboptimal pre-analytical variables such as sample collection, processing and storage inconsistencies introduce variability during measurement, impairing assay validity [126]. Robust analytical validation establishing reliable measurement across diverse testing platforms becomes essential to ensure clinical meaningfulness of any biomarker result [127].

Biomarkers must prove ability to accurately identify or predict the target disease or outcome beyond what existing tests offer. Early phase studies usually report promising results but larger well-designed validation cohorts are needed to conclusively establish clinical validity, especially when incorporated into models with standard clinical factors [128]. Multi-center studies minimizing selection and ascertainment biases are required to reflect diverse, real-world populations and settings [129]. Covariate adjustments to account for potential confounding factors affecting results also need thorough evaluation. Many biomarkers show diminished performance on external validation highlighting need for rigorous clinical validation [130].

Demonstration of improved patient management or outcomes resulting from incorporating the biomarker into clinical practice is an essential criterion for acceptance. While certain biomarkers may have analytical and clinical validity, actual clinical utility value needs assessment of whether the biomarker adds meaningful information that improves diagnostic or treatment decision making [131]. This requires large prospective trials demonstrating superior clinical outcomes or cost-effectiveness compared to standard care without the biomarker. Such resource-intensive utility evaluations are faced with feasibility challenges and scantly performed in reality for most biomarkers [132].

Lack of standardization of pre-analytic procedures and assay methodologies is another critical roadblock. Inconsistent collection, processing, storage conditions coupled with inter-laboratory differences in analytical platforms often produce variable biomarker results precluding direct comparison across studies or clinical applicability [133]. Establishing standardized protocols and reference methods through centralization and quality control programs is necessary before biomarkers can be widely adopted. Harmonization of assays also needs international cooperation and endorsement by regulatory bodies [134].

Cost of biomarker testing and feasibility of routine application within resource constraints requires consideration [135]. Although some biomarkers provide better prognostic accuracy than existing tests, their overall impact on healthcare spending and availability of alternative strategies require rigorous health economic evaluations [136]. Validation of cost-effectiveness through large pragmatic trials under real-world practice conditions is a demanding yet necessary exercise to justify biomarker adoption [137]. Few studies have conclusively demonstrated clear cost savings or favourable incremental cost-effectiveness ratios for most biomarkers in practice.

A significant challenge in synthesizing findings from various studies on novel biomarkers for AKI is the variability in study design, sample size, and methodology. The included studies range from randomized controlled trials to observational cohorts and retrospective analyses, introducing heterogeneity in the evidence. Smaller studies may lack statistical power and overestimate effect sizes, while larger studies face challenges related to patient heterogeneity and generalizability. Methodological differences, such as the timing of biomarker measurements, specific assays used, and AKI diagnostic criteria, further complicate comparisons. Variations in patient populations, including age, comorbidities, and baseline kidney function, also influence biomarker performance and predictive value. These factors underscore the need for standardized protocols and larger, multicenter studies to validate biomarkers across diverse populations, enhancing the robustness and applicability of findings for clinical practice.

4.5. Distinguishing between causality and association

Distinguishing between causality and association is crucial in evaluating the clinical utility of novel biomarkers for AKI. In this manuscript, we have discussed various biomarkers that are used as indicators of underlying renal injury [138]. These biomarkers, such as NGAL, KIM-1, and [TIMP-2]·[IGFBP7], often reflect renal stress or damage before traditional markers like serum creatinine show significant changes [139]. However, it is important to note that while these biomarkers are strongly associated with clinical outcomes like mortality and the need for renal replacement therapy, they do not necessarily imply a direct causal relationship. The biomarkers serve primarily as indicators, providing early warning signs of AKI and facilitating timely intervention.

On the other hand, some evidence suggests that certain biomarkers might play a direct role in the pathophysiological processes of AKI. For example, elevated levels of proenkephalin (penKid) have been linked not only to the presence of renal injury but also to specific pathways that may influence kidney function and recovery [33]. This dual role highlights the potential for these biomarkers to be both diagnostic tools and therapeutic targets. A detailed discussion on the nature of these relationships, based on current evidence, will enhance the understanding of how biomarkers can be integrated into clinical practice to improve patient outcomes [123]. Future research should focus on elucidating these mechanisms to distinguish whether the biomarkers contribute directly to adverse outcomes or merely serve as proxies for underlying renal injury.

4.6. Serial biomarker assessment and clinical decision-making

Serial biomarker assessment provides dynamic insights into the progression or resolution of AKI. Evidence from several studies supports the use of biomarker trends to guide treatment decisions, such as the early initiation of renal replacement therapy or the intensification of monitoring [46]. For example, studies have shown that rising levels of biomarkers like NGAL, KIM-1, and [TIMP-2]·[IGFBP7] can predict worsening renal function and the need for more aggressive interventions [41, 140]. Conversely, declining biomarker levels may indicate recovery and allow for the de-escalation of intensive therapies. Despite these promising findings, there are currently no standardized protocols for interpreting biomarker dynamics [141]. This lack of standardization presents significant challenges for clinical practice, as variations in measurement timing, assay methods, and patient characteristics can lead to inconsistent interpretations. Establishing standardized guidelines for biomarker monitoring and dynamic assessment is crucial to leveraging these tools effectively in clinical decision-making. Further research is needed to develop and validate such protocols, ensuring that biomarker trends can be reliably used to improve patient outcomes in AKI management [123].

4.7. Evidence supporting serial biomarker assessment

Serial biomarker assessment provides dynamic insights into the progression or resolution of AKI. Evidence from several studies supports the use of biomarker trends to guide treatment decisions [46]. For instance, rising levels of biomarkers such as NGAL, KIM-1, and [TIMP-2]·[IGFBP7] have been shown to predict worsening renal function and the need for early initiation of renal replacement therapy or more intensive monitoring [142]. Conversely, declining biomarker levels can indicate recovery, allowing for the de-escalation of intensive therapies. Despite these promising findings, there are currently no standardized protocols or guidelines for interpreting biomarker dynamics in AKI management [41]. The absence of standardization leads to variability in clinical practice and poses challenges in reliably using biomarker trends to inform treatment decisions. This underscores the need for further research to develop and validate standardized protocols for biomarker monitoring and interpretation. Establishing such guidelines will be crucial for integrating serial biomarker assessment into routine clinical practice, ultimately improving the management and outcomes of AKI patients [143].

4.8. Future perspectives

While promising, the clinical application of novel AKI biomarkers still needs validation in large multicenter trials before widespread adoption [144]. Uncertainties regarding optimal cutoff values for risk stratification and standardization of assay methods across different institutions exist [145]. Cost-effectiveness of routine biomarker measurement also needs to be established clearly [146]. Research is ongoing to evaluate if incorporation of novel biomarkers during clinical management can translate to improved outcomes. Whether clinical decisions guided by biomarker levels indeed modify prognosis compared to standard care alone needs to be determined [147]. Studies evaluating if outcomes like dialysis dependence and mortality can be reduced with a biomarker-based personalized approach are warranted. Lastly, novel urinary and plasma biomarkers together provide complementary non-invasive windows into kidney injury pathology [26]. Future research should focus on developing multi-marker panels that leverage synergies to maximize precision of AKI diagnosis, prognosis prediction and management guidance [148]. With ongoing validation, novel biomarkers hold immense promise to revolutionize AKI risk stratification, early detection and guide precision management.

While the manuscript acknowledges the ongoing validation of biomarkers across diverse settings, it is essential to emphasize the limitations of extrapolating findings from specific study populations to broader clinical contexts. Biomarker performance can be significantly influenced by various patient-specific factors such as age, comorbidities, and medication use. For instance, older patients may exhibit different baseline levels of biomarkers due to age-related physiological changes, potentially affecting the biomarkers’ predictive accuracy. Similarly, the presence of comorbid conditions like diabetes, hypertension, or chronic kidney disease can alter biomarker levels and their relationship with acute kidney injury. Medication use, particularly nephrotoxic drugs, can also impact biomarker levels and modify the risk of AKI. These factors underscore the need for caution when generalizing study findings to broader populations. Future research should aim to include diverse patient cohorts and conduct subgroup analyses to better understand how these variables influence biomarker performance. By addressing these limitations, we can improve the generalizability of biomarker-based approaches and enhance their applicability in diverse clinical settings.

While the manuscript acknowledges the ongoing validation of biomarkers across diverse settings, it is essential to emphasize the limitations of extrapolating findings from specific study populations to broader clinical contexts. Biomarker performance can be significantly influenced by various patient-specific factors such as age, comorbidities, and medication use. For instance, older patients may exhibit different baseline levels of biomarkers due to age-related physiological changes, potentially affecting the biomarkers’ predictive accuracy. Similarly, the presence of comorbid conditions like diabetes, hypertension, or chronic kidney disease can alter biomarker levels and their relationship with acute kidney injury. Medication use, particularly nephrotoxic drugs, can also impact biomarker levels and modify the risk of AKI. These factors underscore the need for caution when generalizing study findings to broader populations. Future research should aim to include diverse patient cohorts and conduct subgroup analyses to better understand how these variables influence biomarker performance. By addressing these limitations, we can improve the generalizability of biomarker-based approaches and enhance their applicability in diverse clinical settings.