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Hyponatremia is the most common electrolyte abnormality in hospitalized patients. Treatment of hyponatremia is associated with improved outcomes, but more than one in three cases of new onset hyponatremia is not corrected by the time of hospital discharge. Nephrologist input may improve the diagnosis and treatment of hyponatremia, but specialist resources are limited. Targeted automatic electronic consultations (TACos) may be one approach to provide expert nephrologist guidance to the workup and management of hyponatremia using a scalable model. Evaluate the feasibility and acceptability of a TACo intervention for hospitalized patients with hyponatremia. Single-site, parallel-group cluster randomized trial. Adult inpatients with hyponatremia on the hospital medicine service. A nephrologist conducted TACos on intervention patients, making diagnostic and therapeutic recommendations daily (if warranted) until discharge or resolution of hyponatremia. Measures of feasibility included the number of eligible participants, percentage receiving TACos, number of TACos per participant, and percentage of formal nephrology consults. Acceptability was assessed by a post-intervention survey. Clinical outcomes, including the percentage of hyponatremia cases that resolved by discharge, were also assessed. We identified 62 patients who met inclusion criteria: 38 in the intervention group and 24 in the control group. A nephrologist determined that 26 of 38 intervention patients (68%) would likely benefit from diagnostic and management recommendations; 67 TACos were performed (mean 2.6 per patient). Fourteen of 18 primary team physicians (78%) reported that the e-consults changed their management, and 15 of 18 (83%) wanted TACOs to continue. Resolution of hyponatremia, length of stay, 30-day readmissions, and costs were similar in the intervention and control groups. Inpatient TACos for hyponatremia were feasible and acceptable to primary teams, and frequently led to changes in diagnosis and management. Further studies are needed to determine the impact of the TACo model on clinical outcomes and cost-effectiveness.

No abstract available Copyright © 2012 S. Karger AG, Basel [PUBLICATION ABSTRACT]

Dietary management in pediatric chronic kidney disease (CKD) is an area fraught with uncertainties and wide variations in practice. Even in tertiary pediatric nephrology centers, expert dietetic input is often lacking. The Pediatric Renal Nutrition Taskforce (PRNT), an international team of pediatric renal dietitians and pediatric nephrologists, was established to develop clinical practice recommendations (CPRs) to address these challenges and to serve as a resource for nutritional care. We present CPRs for energy and protein requirements for children with CKD stages 2–5 and those on dialysis (CKD2–5D). We address energy requirements in the context of poor growth, obesity, and different levels of physical activity, together with the additional protein needs to compensate for dialysate losses. We describe how to achieve the dietary prescription for energy and protein using breastmilk, formulas, food, and dietary supplements, which can be incorporated into everyday practice. Statements with a low grade of evidence, or based on opinion, must be considered and adapted for the individual patient by the treating physician and dietitian according to their clinical judgment. Research recommendations have been suggested. The CPRs will be regularly audited and updated by the PRNT.

Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-related conditions with the aging global population. This study evaluates the utility of ChatGPT, a large language model, in triaging nephrology cases through simulated real-world scenarios. Two nephrologists created 100 patient cases that encompassed various aspects of nephrology. ChatGPT’s performance in determining the appropriateness of nephrology consultations and identifying suitable nephrology subspecialties was assessed. The results demonstrated high accuracy; ChatGPT correctly determined the need for nephrology in 99–100% of cases, and it accurately identified the most suitable nephrology subspecialty triage in 96–99% of cases across two evaluation rounds. The agreement between the two rounds was 97%. While ChatGPT showed promise in improving medical triage efficiency and accuracy, the study also identified areas for refinement. This included the need for better integration of multidisciplinary care for patients with complex, intersecting medical conditions. This study’s findings highlight the potential of AI in enhancing decision-making processes in clinical workflow, and it can inform the development of AI-assisted triage systems tailored to institution-specific practices including multidisciplinary approaches.

Acute kidney injury (AKI) is an increasingly frequent complication among hospitalized children. It is associated with high morbidity and mortality, especially in neonates and children requiring dialysis. The different renal replacement therapy (RRT) options for AKI have expanded from peritoneal dialysis (PD) and intermittent hemodialysis (HD) to continuous RRT (CRRT) and hybrid modalities. Recent advances in the provision of RRT in children allow a higher standard of care for increasingly ill and young patients. In the absence of evidence indicating better survival with any dialysis method, the most appropriate dialysis choice for children with AKI is based on the patient’s characteristics, on dialytic modality performance, and on the institutional resources and local practice. In this review, the available dialysis modalities for pediatric AKI will be discussed, focusing on indications, advantages, and limitations of each of them.

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods. In this Review, the authors discuss mass spectrometry (MS) imaging and spatially resolved MS approaches that are being employed in nephrology applications. They also highlight emerging MS methods and applications, as well as the integration of MS data with data from other omics approaches. Key points Mass spectrometry (MS) is a versatile technology that enables the analysis of various classes of molecules, including proteins, metabolites, lipids and drugs; the resulting molecular data complement data from other omics approaches such as transcriptomics and genomics. Bulk analysis of kidney samples with MS can obfuscate localized molecular changes at the functional tissue unit and cellular level; however, the results of these assays can complement spatially resolved MS data. Spatially resolved MS methods, including MS imaging, have been used to identify key molecular signatures related to kidney function and disease progression at the cellular level. Various spatially resolved and MS imaging approaches have been used to perform spatial and single-cell metabolomics and proteomics analyses of kidney samples; these approaches can be targeted or untargeted and can be used in a multimodal fashion. Numerous emerging MS domains can be applied to nephrology studies, including highly multiplexed immunohistochemistry, ion mobility and three-dimensional imaging; these approaches can provide additional insights into the complex molecular mechanisms that occur within and between cells and functional tissue units. Artificial intelligence and machine-learning tools are poised to have a substantial impact owing to their advantages in simplifying complex MS data, image processing and data integration, as well as aiding identification of disease markers, elucidation of connections between omics findings and rapid data interpretation.

Acute kidney injury (AKI) in critically ill patients is clinically complex and heterogeneous, limiting the development of structured simulation models. Digital twin approaches require clearly defined causal relationships grounded in expert consensus. We aimed to establish an international Delphi consensus to inform the development of an interpretable, AI-driven digital twin framework for AKI in critical care. We conducted a modified Delphi study involving up to three survey rounds. Experts in nephrology and critical care were invited to evaluate statements addressing AKI etiology, biomarker integration, biopsy indications, contrast use, and ICU management. Consensus was predefined as ≥75% agreement. Fifty-six experts were invited, and 49 completed the first round. Consensus was achieved after two rounds. Hemodynamic instability and nephrotoxicity were identified as leading contributors to AKI. Experts supported integrating biomarkers beyond serum creatinine and urine output, although agreement varied for specific assays. Daily biomarker assessment reached consensus, whereas contrast use in advanced AKI stages did not. Desmopressin use prior to kidney biopsy in patients with markedly elevated blood urea nitrogen achieved consensus. A structured Directed Acyclic Graph was developed to represent the expert-derived causal framework. This Delphi study established a clinically grounded framework for AKI in critical care while highlighting areas of practice variability. The resulting causal structure provides a transparent foundation for future AI-driven digital twin development and prospective validation.

Trial emulation, also known as target trial emulation, has significantly advanced epidemiology and causal inference by providing a robust framework for deriving causal relationships from observational data. This approach aims to reduce biases and confounding factors inherent in observational studies, thereby improving the validity of causal inferences. By designing observational studies to mimic randomized controlled trials (RCTs) as closely as possible, researchers can better control for confounding and bias. Key components of trial emulation include defining a clear time-zero, simulating random assignment using techniques like propensity score matching and inverse probability treatment weighting, assessing group comparability by standardized mean differences and establishing a clear comparison strategy. The increasing availability of large-scale real-world databases, such as research cohorts, patient registries, and hospital records, has driven the popularity of target trial emulation. These data sources offer information on patient outcomes, treatment patterns, and disease progression in real-world settings. By applying the principles of target trial emulation to these rich data sources, researchers can design studies that provide robust causal inferences about the effects of interventions, informing clinical guidelines and regulatory decisions. Despite its advantages, trial emulation faces challenges like data quality, unmeasured confounding, and implementation complexity. Future directions include integrating trial emulation with machine learning techniques and developing methods to address unmeasured confounding. Overall, trial emulation represents a significant advancement in epidemiology, offering a valuable tool for deriving accurate and reliable causal inferences from observational data, ultimately improving public health outcomes. Graphical abstract

Slow low-efficiency dialysis (SLED) is viewed historically a 'hybrid' technique of kidney replacement therapy, representing a transition between intermittent and continuous kidney replacement therapy. It can be performed with a mobile single-pass batch dialysis system or multifunctional hemodialysis machines, using reduced dialysate flow and an extended procedure duration. We summarized the technical aspects, as well as the practical advantages of SLED. Moreover, we present our experience of using SLED in selected difficult clinical conditions, such as intraoperative renal replacement therapy during liver transplantation, in sepsis, and in heart failure patients with recalcitrant volume overload. In this paper, we aimed to describe the unique advantages of SLED in terms of efficacy, safety, and, more importantly, sustainability in kidney care in the intensive care units.

IgA nephropathy (IgAN) is the most prevalent primary glomerular disease worldwide and a leading cause of end-stage kidney disease (ESKD). Its clinical heterogeneity results in divergent renal outcomes, making early identification of high-risk patients essential. Prognostic models are crucial for stratifying ESKD risk, guiding treatment intensity, optimizing timing of interventions such as immunosuppressive therapy, and informing clinical trial enrollment. Over recent decades, multiple prognostic approaches have emerged, ranging from traditional clinical and histopathological scoring systems to advanced machine learning (ML) and deep learning (DL) models designed to capture complex nonlinear interactions and improve predictive precision. Among them, the International IgA Nephropathy Prediction Tool (IIgAN-PT), endorsed by the 2021 KDIGO guidelines, represents a landmark in globally validated risk assessment and has set the foundation for standardized clinical decision support. However, classical models often rely on static baseline parameters and may not adequately reflect dynamic disease trajectories, limiting their utility in real-time clinical management. To overcome these limitations, ML- and DL-based models increasingly integrate multi-omics data, serial clinical measurements, and digital pathology features, offering enhanced accuracy, dynamic risk tracking, and potential for personalized response prediction. These data-driven approaches are progressively bridging the gap between prognostic research and precision nephrology. This review provides a comprehensive overview of the evolution of IgAN prognostic models, summarizes their strengths and limitations, and discusses considerations for clinical translation. By highlighting emerging trends toward explainable AI, dynamic time-series modeling, and multimodal prognostication, we outline how next-generation prediction tools may enable real-time, AI-driven decision support for individualized IgAN management.