Koyner, Jay L.

Scientific evidence regarding AKI and acute kidney care has advanced in recent years. However, a knowledge gap remains on the implementation of these evidence-based practices (EBPs) into clinical care. Implementation science (IS) is focused on ensuring that this knowledge is translated in an effective, efficient and sustainable fashion. Objectives: 1) Define the current status of IS; 2) Define a roadmap for accelerating IS with a focus on patient/care partner advocacy, digital tool application, social determinants of health, and resource-limited settings; and 3) Develop a robust and broad research agenda incorporating IS methodology into the programs. Evidence Review: ADQI XXXV was conducted through a modified Delphi process with virtual meetings preceding an in-person meeting. Five workgroups were determined a priori to focus on 1) IS definitions and methods applicable to AKI and acute kidney care; 2) IS literature in AKI and acute kidney care; 3) Innovations in IS to accelerate the adoption, adherence to, and sustainment of EBPs in AKI and acute kidney care; 4) IS methods in resource-limited settings; and 5) Recommendations to identify and evaluate EBPs that are ready for implementation or de-implementation. Prior to the in-person meeting, each workgroup met virtually to review the literature and develop framework questions to address their objectives. During the two-day in person meeting, through iterative discussions, questions and supporting statements were finalized. These questions and statements were agreed upon through voting to achieve consensus, defined as agreement of ≥80%. Conclusions and Relevance: We report a structured multidisciplinary consensus for defining the role of IS in AKI and acute kidney care. Future programs should address these consensus questions and apply these statements along with IS methodology in the translation of science into clinical practice and the implementation/de-implementation of EBPs in clinical care.

ABSTRACT Artificial intelligence (AI), encompassing machine learning, deep learning and generative AI, is poised to redefine nephrology by enabling earlier detection, more precise risk stratification and workflow-integrated clinical decision support across the spectrum of kidney disease. This state-of-the-art review synthesizes emerging applications of AI in acute kidney injury (AKI), chronic kidney disease (CKD), dialysis and kidney transplantation, with attention to clinical integration, real-world validation, workflow implementation and translational challenges. In AKI, predictive models trained on high-frequency electronic health record data and intensive care unit telemetry have demonstrated strong performance in forecasting critical events, yet translation into routine clinical workflows remains limited. In CKD, machine learning tools support progression risk stratification and phenotype clustering, with the potential to inform individualized surveillance and therapy. AI-enabled dialysis management systems optimize ultrafiltration, anemia control and vascular access surveillance, while generative AI and large language models offer novel capabilities for clinical documentation, triage and patient education. In transplantation, AI applications span organ allocation, dynamic graft monitoring and digital pathology-assisted rejection classification, with validated tools such as the iBox system gaining regulatory recognition. Implementation challenges include data heterogeneity, bias, interpretability, regulatory uncertainty and workflow integration. Looking ahead, multimodal integration of imaging, pathology and multi-omics data may support biologically informed precision nephrology. Reinforcement learning, digital twins and ambient intelligence are emerging as adaptive decision-support paradigms rather than autonomous care systems. Regulatory frameworks are evolving to accommodate adaptive algorithms, underscoring the need for clinician engagement in model development, validation and deployment. As AI matures from pilot to practice, nephrologists who embrace and help shape these tools will lead the transition toward a more personalized, efficient and equitable future for kidney care.

Purpose of review Acute kidney injury (AKI) is a highly prevalent clinical syndrome that substantially impacts patient outcomes. It is accepted by the clinical communities that the management of AKI is time-sensitive. Unfortunately, despite growing proof of its preventability, AKI management remains suboptimal in community, acute care, and postacute care settings. Digital health solutions comprise various tools and models to improve care processes and patient outcomes in multiple medical fields. AKI development, progression, recovery, or lack thereof, offers tremendous opportunities for developing, validating, and implementing digital health solutions in multiple settings. This article will review the definitions and components of digital health, the characteristics of AKI that allow digital health solutions to be considered, and the opportunities and threats in implementing these solutions. Recent findings Over the past two decades, the academic output related to the use of digital health solutions in AKI has exponentially grown. While this indicates the growing interest in the topic, most topics are primarily related to clinical decision support by detecting AKI within hospitals or using artificial intelligence or machine learning technologies to predict AKI within acute care settings. However, recently, projects to assess the impact of digital health solutions in more complex scenarios, for example, managing nephrotoxins among adults of pediatric patients who already have AKI, is increasing. Depending on the type of patients, chosen digital health solution intervention, comparator groups, and selected outcomes, some of these studies showed benefits, while some did not indicate additional gain in care processes or clinical outcomes. Summary Careful needs assessment, selection of the correct digital health solution, and appropriate clinical validation of the benefits while avoiding additional health disparities are moral, professional, and ethical obligations for all individuals using these healthcare tools, including clinicians, data scientists, and administrators.

Objectives: Decreased urine output and/or increased serum creatinine may herald the development of acute kidney injury or reflect normal physiology. In this secondary analysis of the Sapphire study, we examined biomarkers of cell cycle arrest in the settings of oliguria and/or azotemia to improve risk assessment when used with conventional indices in predicting severe acute kidney injury (Kidney Disease: Improving Global Outcomes 3 defined by the need for renal replacement therapy or changes in urine output, serum creatinine or both) or death. Design: Prospective, international, Sapphire study. Setting: Academic Medical Center. Patients: Patients without acute kidney injury Kidney Disease: Improving Global Outcomes stage 2 or 3. Interventions: None. Measurements and Main Results: The primary endpoint being development of severe acute kidney injury or death within 1 week. Secondary analysis examined the relationship between tissue inhibitor of metalloproteinases-2 ([TIMP-2]) and insulin growth factor binding protein 7 ([IGFBP7]) and 9-month death or dialysis conditioned on progression to stage 2–3 acute kidney injury within 1 week. Seventy-nine patients reached the primary endpoint and were more likely to be surgical, with higher nonrenal Acute Physiology and Chronic Health Evaluation III scores and more chronic kidney disease. Stage 1 urine output, serum creatinine, and urinary [TIMP-2]•[IGFBP7] greater than 2.0 were all predictive of progression to the primary endpoint independent from nonrenal Acute Physiology and Chronic Health Evaluation III score. Combinations of predictors increased the hazard ratios considerably (from 2.17 to 4.14 to 10.05, respectively). In the presence of acute kidney injury (stage 1), [TIMP-2]•[IGFBP7] greater than 2.0 leads to an increased risk of death or dialysis at 9 months even in the absence of progression of acute kidney injury (stage 2–3) within 7 days. Conclusions: Cell cycle arrest biomarkers, TIMP-2 and IGFBP7, improve risk stratification for severe outcomes in patients with stage 1 acute kidney injury by urine output, serum creatinine or both, with risk increasing with each acute kidney injury indicator. Longer term outcomes demonstrate that the associated risks of a [TIMP-2]•[IGFBP7] greater than 2.0 is equivalent to acute kidney injury progression even where no progression from stage 1 acute kidney injury is observed.

AKI is a global concern with a high incidence among patients across acute care settings. AKI is associated with significant clinical consequences and increased health care costs. Preventive measures, as well as rapid identification of AKI, have been shown to improve outcomes in small studies. Providing high-quality care for patients with AKI or those at risk of AKI occurs across a continuum that starts at the community level and continues in the emergency department, hospital setting, and after discharge from inpatient care. Improving the quality of care provided to these patients, plausibly mitigating the cost of care and improving short- and long-term outcomes, are goals that have not been universally achieved. Therefore, understanding how the management of AKI may be amenable to quality improvement programs is needed. Recognizing this gap in knowledge, the 22nd Acute Disease Quality Initiative meeting was convened to discuss the evidence, provide recommendations, and highlight future directions for AKI-related quality measures and care processes. Using a modified Delphi process, an international group of experts including physicians, a nurse practitioner, and pharmacists provided a framework for current and future quality improvement projects in the area of AKI. Where possible, best practices in the prevention, identification, and care of the patient with AKI were identified and highlighted. This article provides a summary of the key messages and recommendations of the group, with an aim to equip and encourage health care providers to establish quality care delivery for patients with AKI and to measure key quality indicators.

Abstract Background The assessment of kidney function and detection of acute kidney injury (AKI) remain cumbersome. On the one hand, because of limited accuracy of established tests: The most widely used methods are creatinine based, which lack in sensitivity, as creatinine is not purely filtrated by the kidney and rises relatively late after onset of AKI. On the other hand, because of labor-intensiveness: Gold standard inulin clearance and comparable methods involve intravenous compound infusion, blood sampling at several time points, and have error-sensitive determination methods. In recent years, several biomarkers have been put forward (e.g., NGAL, KIM-1, TIMP-2*IGFBP-7), but clinical implementation is limited up to now. Content Proenkephalin (PENK) represents a new candidate to determine kidney function. This peptide is cleaved from the precursor peptide preproenkephalin A alongside enkephalins (endogenous opioids) and is filtrated in the glomerulus. PENK plasma concentration appears to accurately represent glomerular filtration rate in patients diagnosed with sepsis or cardiac diseases. Moreover, increased PENK concentration is found to be associated with longer-term outcome concerning AKI and cardiac diseases. Lastly, the predominant receptor of enkephalins, the δ-opioid receptor, is expressed with the highest density in the kidney, suggesting that enkephalins could also exert a direct effect on kidney function. Summary In this review, we present an overview of enkephalins and the assessment of kidney function using this possible new functional biomarker PENK and compare it with established and novel biomarkers.