Citations

ABSTRACT Sodium glucose co‐transporter 2 inhibitors (SGLT2i) and glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) are nephroprotective and their use is recommended for adults with chronic kidney disease (CKD). The role of SGLT2i and GLP‐1 RAs in patients with acute kidney injury (AKI) is unknown. This systematic review aimed to estimate the effect of SGLT2i or GLP‐1 RAs on clinical outcomes in AKI. We systematically searched Embase, MEDLINE, Scopus, Web of Science Core Collection, and clinical trials registries for observational studies and clinical trials from database inception to July 3, 2025. Included studies evaluated adults (≥ 18 years) with AKI during a hospitalization and compared patients subsequently exposed to either SGLT2i or GLP‐1 RAs to a non‐exposed control group. Random effects meta‐analyses were performed. Six studies related to SGLT2i (five observational, one randomized trial) and one related to GLP‐1 RAs (one observational) met eligibility criteria ( N = 432,048 patients). SGLT2i/GLP‐1 RA exposure was associated with lower odds of major adverse kidney events (MAKE) (OR 0.63, 95% CI 0.46–0.86) and all‐cause mortality [odds ratio (OR) 0.36, 95% confidence interval (CI) 0.21–0.62] compared to controls. The odds for kidney replacement therapy were lower with SGLT2i therapy in the three studies where it was evaluated (OR 0.61, 95% CI 0.40–0.93). Sensitivity analyses restricted to SGLT2i data were consistent with the overall findings [MAKE OR 0.63 (95% CI 0.42–0.95); Mortality OR 0.34 (95% CI 0.18, 0.65)]. Exposure to SGLT2i or GLP‐1 RAs after AKI, examined primarily in observational studies, was associated with improved clinical outcomes. These findings are promising and warrant evaluation in randomized clinical trials.

Background The prognostic significance of an adverse acid–base milieu despite venoarterial extracorporeal membrane oxygenation support in patients with cardiogenic shock remains poorly defined. We examined metabolic and respiratory disturbances in the ELSO (Extracorporeal Life Support Organization) Registry among patients who received venoarterial extracorporeal membrane oxygenation support for refractory cardiogenic shock. Methods Arterial blood gas acid–base values at 24 hours following venoarterial extracorporeal membrane oxygenation initiation were analyzed, with patients classified by pH, arterial partial pressure of carbon dioxide, and bicarbonate. Multivariable logistic regression and exploratory machine learning analyses were used to assess predictors of in‐hospital mortality. Results Among 15 144 adult patients, 74.6% (10 860) had neither respiratory nor metabolic acidosis, 13.1% (1909) had isolated respiratory acidosis, 11.6% (1684) had isolated metabolic acidosis, and 0.7% (100) had combined acidosis. In‐hospital mortality occurred in 7719 (51.0%) patients and increased incrementally across patients with lower pH (adjusted odds ratio [OR], 2.03 with each 0.1‐unit decrease in pH, using 7.4 as a reference point [95% CI, 1.82–2.26]), bicarbonate (adjusted OR, 1.93 per each 5‐mEq/L <24 mEq/L [95% CI, 1.76–2.12]) or base excess (adjusted OR, 1.90 per each 5‐mEq/L <0 mEq/L [95% CI, 1.75–2.07]) (all P <0.01). Mortality varied significantly, being highest for the low‐pH metabolic acidosis cluster (adjusted OR, 1.90 [95% CI, 1.70–2.12], P <0.01). Conclusions Survival decreased progressively with worse acidemia or metabolic acidosis despite 24 hours of hemodynamic support with venoarterial extracorporeal membrane oxygenation among patients with cardiogenic shock. Our findings emphasize the prognostic significance of metabolic parameters as potentially modifiable risk factors and underscore the need for early recognition and interventions to mitigate severe metabolic derangements.

The glomerular filtration rate (GFR) is a key indicator of kidney function, yet no ideal method exists for its direct measurement. This two-part review examines the development of GFR measurement techniques, focusing on their physiologic basis, performance characteristics, and clinical applications. Part 1 reviews endogenous markers used to estimate GFR (eGFR), including creatinine- and cystatin C-based equations, with emphasis on their derivation, limitations, and clinical use. Part 2 addresses exogenous filtration markers and measured GFR (mGFR) methodologies, highlighting comparative performance and specific uses. Emphasis is placed on innovation in marker development, highlighting the need for continued development and standardization to enhance accuracy and clinical utility.

Measured glomerular filtration rate (mGFR) remains the reference standard for precise assessment of kidney function when estimated approaches are insufficient. This second part of a two-part series reviews exogenous filtration markers used to obtain mGFR, including inulin, iothalamate, iohexol, and radionuclide-based tracers, with emphasis on their physiologic handling, analytic methods, accuracy, and clinical applicability. The review further examines emerging fluorescent and near-infrared tracers coupled with transcutaneous monitoring technologies that enable rapid or continuous assessment of GFR. Together, these approaches illustrate the evolution of mGFR measurement toward methods that are more practical, scalable, and responsive to dynamic changes in kidney function, while maintaining analytical accuracy.

Abstract Background Cardiac dysfunction is a common complication in septic population. Though traditionally considered an acute and reversible phenomenon, the true prevalence of reversible cardiac dysfunction and its long-term prognostic implications remain unclear. Purpose Using serial echocardiographic assessments, this study aimed to investigate the prevalence and reversibility of cardiac dysfunction in septic patients, and to determine whether the recovery of cardiac function is associated with improved 5-year mortality. Methods We retrospectively screened 16,041 septic patients who underwent transthoracic echocardiography during their ICU stay. After excluding patients with a history of heart failure, pre-existing cardiac dysfunction, or lacking follow-up echocardiographic assessments post-ICU discharge, 3,112 patients were enrolled for analysis. To assess the reversibility of cardiac dysfunction, paired-sample t-tests were used to evaluate changes in echocardiographic parameters between in-ICU and post-ICU measurements. Furthermore, 5-year mortality were compared among controls and patients with or without recovery of echocardiographic abnormalities. Results Totally 1,604 patients (51.5%) were found to have newly recognized cardiac dysfunction during ICU stay. The mean interval between ICU admission and follow-up echocardiography was 425 days. Most in-ICU echocardiographic abnormalities demonstrated partial recovery over time, with the exception of left atrial volume index (LAVi) and right ventricular fractional area change (RV FAC) (Figure 1). Recovery of left ventricular ejection fraction (LVEF), average E/e', and tricuspid regurgitation (TR) velocity was significantly associated with reduced 5-year mortality (Figure 2). Older age [adjusted hazard ratio (aHR) 0.99, p=0.034], male gender (aHR 0.54, p=0.003), and elevated troponins (aHR 0.87, p=0.006) were negative predictors for LVEF recovery; while older age (aHR 0.099, p=0.043), higher APACHE III scores (aHR 0.99, p=0.018), atrial fibrillation (aHR 0.59, p=0.046), and chronic pulmonary disease (aHR 0.68, p=0.042) were negative predictors for TR velocity recovery. Conclusions Both the presence and recovery of newly recognized echocardiographic abnormalities during ICU stay were significantly associated with long-term mortality. Certain abnormalities, such as increased LAVi, persisted despite resolution of sepsis.Figure 1 Figure 2

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.

Background: Adequate medication dosing in the intensive care unit (ICU) remains challenging due to rapid physiological variability, narrow therapeutic windows, and evolving standards of care. Artificial intelligence (AI) offers promise for personalized dosing, but its accuracy and clinical utility remain uncertain. Methods: We conducted a systematic scoping review (2011-2025) of PubMed, Embase, and Cochrane, following the Joanna Briggs Institute (JBI) and preferred reporting Items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines (open science framework [OSF]-registered). Eligible studies applied AI methods ( e.g. , machine learning [ML], deep learning [DL], reinforcement learning [RL], and natural language processing [NLP]) to ICU medication dosing or monitoring. Results: Data extraction included study design, drug class, model inputs and outputs, algorithms, validation approach, and reported outcomes. Forty-seven studies met the inclusion criteria, which were mostly retrospective and focused on adult ICU populations over the past five years. Antibiotics and anticoagulants were the most studied classes, with vancomycin being the most common. Models primarily used labs, vitals, and demographics, with limited incorporation of time series, pharmacokinetic data, or clinical notes. Outputs spanned dose prediction, serum concentration, risk stratification, adverse events, and decision support. Algorithms evolved from classical ML and ensembles to RL and long short-term memory (LSTM)-based models, although a few advanced beyond retrospective validation. Discussion: AI systems showed strong predictive performance and some clinical benefits ( e.g. , heparin target attainment, sedative reduction, and adverse event detection), but the field remains fragmented and clinically immature. Newer architectures and systematic explainability are lacking, and generalizability is uncertain. AI-enabled dosing holds promise for precision pharmacotherapy in critical illness but requires prospective multicenter validation, inclusion of population pharmacokinetic (PK) parameters, diverse cohorts, explainable design, and clinical integration.