Neyra, Javier A

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.

Key Points Pooled discrimination metrics were acceptable (area under the receiver operating characteristic curve >0.70) for all AKI risk classification categories in both internal and external validation. Better performance was observed in most recently published studies and those with a low or unclear risk of bias. Significant heterogeneity in patient populations, definitions, clinical predictors, and methods limit implementation in real-world clinical scenarios. Background Artificial intelligence through machine learning models seems to provide accurate and precise AKI risk classification in some clinical settings, but their performance and implementation in real-world settings has not been established. Methods PubMed, Excerpta Medica (EMBASE) database, Web of Science, and Scopus were searched until August 2023. Articles reporting on externally validated models for prediction of AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric patients were searched using text words related to AKI, artificial intelligence, and machine learning. Two independent reviewers screened article titles, abstracts, and full texts. Areas under the receiver operating characteristic curves (AUCs) were used to compare model discrimination and pooled using a random-effects model. Results Of the 4816 articles initially identified and screened, 95 were included, representing 3.8 million admissions. The Kidney Disease Improving Global Outcomes (KDIGO)-AKI criteria were most frequently used to define AKI (72%). We identified 302 models, with the most common being logistic regression (37%), neural networks (10%), random forest (9%), and eXtreme gradient boosting (9%). The most frequently reported predictors of hospitalized incident AKI were age, sex, diabetes, serum creatinine, and hemoglobin. The pooled AUCs for AKI onset were 0.82 (95% confidence interval, 0.80 to 0.84) and 0.78 (95% confidence interval, 0.76 to 0.80) for internal and external validation, respectively. Pooled AUCs across multiple clinical settings, AKI severities, and post-AKI complications ranged from 0.78 to 0.87 for internal validation and 0.73 to 0.84 for external validation. Although data were limited, results in the pediatric population aligned with those observed in adults. Between-study heterogeneity was high for all outcomes (I 2 >90%), and most studies presented high risk of bias (86%) according to the Prediction Model Risk of Bias Assessment Tool. Conclusions Most externally validated models performed well in predicting AKI onset, AKI severity, and post-AKI complications in hospitalized adult and pediatric populations. However, heterogeneity in clinical settings, study populations, and predictors limits their generalizability and implementation at the bedside.

Abstract High-dose methotrexate (MTX) results in high rates of acute kidney injury (AKI), neutropenia, and hepatotoxicity. Glucarpidase is a recombinant enzyme that cleaves MTX, but clinical data supporting its use are scarce. We examined the association between glucarpidase administration and outcomes in adults with MTX-AKI from 28 cancer centers across the United States using a sequential target trial emulation framework. The primary end point was kidney recovery at hospital discharge, defined as survival to discharge with serum creatinine <1.5-fold baseline and without dialysis dependence. Key secondary end points were time to kidney recovery, neutropenia, and transaminitis on day 7, and time to death. Using multivariable logistic and Cox regression models, we compared outcomes in patients who received glucarpidase within 4 days following MTX initiation with those in patients who did not. Among 708 patients with MTX-AKI, 209 (29.5%) received glucarpidase. Overall, 183 (25.8%) had a primary end point event. Glucarpidase receipt was associated with a 2.70-fold higher adjusted odds of kidney recovery (95% confidence interval [CI], 1.69–4.31) compared with no glucarpidase receipt. Patients treated with glucarpidase also had faster time to kidney recovery (adjusted hazard ratio [aHR], 1.88; 95% CI, 1.18–3.33) and lower risks of grade ≥2 neutropenia (adjusted odds ratio [aOR], 0.50; 95% CI, 0.28–0.91) and grade ≥2 transaminitis (aOR, 0.50; 95% CI, 0.28–0.91) on day 7. There was no difference in time to death (aHR, 0.76; 95% CI, 0.49–1.18). These data suggest glucarpidase may improve both renal and extrarenal outcomes in patients with MTX-AKI.

Key Points In patients with cirrhosis and AKI requiring renal replacement therapy (RRT), hepatorenal syndrome-AKI was not associated with an increased 90-day mortality when compared with other AKI etiologies. Etiology of AKI may not be a critical factor regarding decisions to trial RRT in acutely ill patients with cirrhosis and AKI. Although elevated, mortality rates in this study are comparable with those reported in general hospitalized patients with AKI requiring RRT. Background While AKI requiring renal replacement therapy (AKI-RRT) is associated with increased mortality in heterogeneous inpatient populations, the epidemiology of AKI-RRT in hospitalized patients with cirrhosis is not fully known. Herein, we evaluated the association of etiology of AKI with mortality in hospitalized patients with cirrhosis and AKI-RRT in a multicentric contemporary cohort. Methods This is a multicenter retrospective cohort study using data from the HRS-HARMONY consortium, which included 11 US hospital network systems. Consecutive adult patients admitted in 2019 with cirrhosis and AKI-RRT were included. The primary outcome was 90-day mortality, and the main independent variable was AKI etiology, classified as hepatorenal syndrome (HRS-AKI) versus other (non–HRS-AKI). AKI etiology was determined by at least two independent adjudicators. We performed Fine and Gray subdistribution hazard analyses adjusting for relevant clinical variables. Results Of 2063 hospitalized patients with cirrhosis and AKI, 374 (18.1%) had AKI-RRT. Among them, 65 (17.4%) had HRS-AKI and 309 (82.6%) had non–HRS-AKI, which included acute tubular necrosis in most cases (62.6%). Continuous renal replacement therapy was used as the initial modality in 264 (71%) of patients, while intermittent hemodialysis was used in 108 (29%). The HRS-AKI (versus non–HRS-AKI) group received more vasoconstrictors for HRS management (81.5% versus 67.9%), whereas the non–HRS-AKI group received more mechanical ventilation (64.3% versus 50.8%) and more continuous renal replacement therapy (versus intermittent hemodialysis) as the initial RRT modality (73.9% versus 56.9%). In the adjusted model, HRS-AKI (versus non–HRS-AKI) was not independently associated with increased 90-day mortality (subdistribution hazard ratio, 1.36; 95% confidence interval, 0.95 to 1.94). Conclusions In this multicenter contemporary cohort of hospitalized adult patients with cirrhosis and AKI-RRT, HRS-AKI was not independently associated with an increased risk of 90-day mortality when compared with other AKI etiologies. The etiology of AKI appears less relevant than previously considered when evaluating the prognosis of hospitalized adult patients with cirrhosis and AKI requiring RRT.

Key Points AKI survivors experience gaps in care that contribute to worse outcomes, experience, and cost. Challenges to optimal care include issues with information transfer, education, collaborative care, and use of digital health tools. Research is needed to study these challenges and inform optimal use of diagnostic and therapeutic interventions to promote recovery AKI affects one in five hospitalized patients and is associated with poor short-term and long-term clinical and patient-centered outcomes. Among those who survive to discharge, significant gaps in documentation, education, communication, and follow-up have been observed. The American Society of Nephrology established the AKINow taskforce to address these gaps and improve AKI care. The AKINow Recovery workgroup convened two focus groups, one each focused on dialysis-independent and dialysis-requiring AKI, to summarize the key considerations, challenges, and opportunities in the care of AKI survivors. This article highlights the discussion surrounding care of AKI survivors discharged without the need for dialysis. On May 3, 2022, 48 patients and multidisciplinary clinicians from diverse settings were gathered virtually. The agenda included a patient testimonial, plenary sessions, facilitated small group discussions, and debriefing. Core challenges and opportunities for AKI care identified were in the domains of transitions of care, education, collaborative care delivery, diagnostic and therapeutic interventions, and digital health applications. Integrated multispecialty care delivery was identified as one of the greatest challenges to AKI survivor care. Adequate templates for communication and documentation; education of patients, care partners, and clinicians about AKI; and a well-coordinated multidisciplinary posthospital follow-up plan form the basis for a successful care transition at hospital discharge. The AKINow Recovery workgroup concluded that advancements in evidence-based, patient-centered care of AKI survivors are needed to improve health outcomes, care quality, and patient and provider experience. Tools are being developed by the AKINow Recovery workgroup for use at the hospital discharge to facilitate care continuity.