Kashani, Kianoush B

Abstract Background Lower extremity compression therapy has been proposed as an adjunct to mitigate diuretic resistance during acute decompensation of heart failure (ADHF). We evaluated the effect of lower leg compression with elastic bandages on diuretic efficiency in patients with diuretic-resistant ADHF. Methods Historical cohort study enrolling adult cardiac ICU admissions with ADHF who used elastic bandages (EB) at Mayo Clinic in Minnesota from 2007 to 2017. The diuretic efficiency before and after applying EB was the primary outcome. Results A total of 176 patients with heart failure who received elastic bandages were included. The in-hospital mortality rate, the median hospital length of stay, and 30-day hospital readmission were 9.7%, 11.4 days (IQR 7.1–19.0), and 11%, respectively. When EB was applied, the average daily loop diuretic dosage was decreased by a mean difference of 1.19 mg per 40 mg furosemide equivalent (95% CI: 0.22 to 2.2; p = 0.016). The average post-EB diuretic efficiency was 1,355 mL (IQR 1,119, 1,590) per 40-mg furosemide, which was significantly higher than before EB application, with a mean difference of 340 mL per 40-mg furosemide equivalent (95% CI: -554, -125; p = 0.002). A higher diuretic efficiency led to lower diuretic doses. Average daily urine output before and after EB application was not different (2,642 pre-EB vs. 2,792 post-EB mL/day; 95% CI: -455 to 154; p = 0.33). Conclusion EB use enhances diuretic efficacy during ADHF. Further studies are needed to determine the potential clinical benefits and risks of applying EB compression treatment in ADHF.

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 Pregnancy-associated acute kidney injury (PrAKI) remains a major contributor to maternal and foetal morbidity worldwide, with an estimated incidence of 40–100 per 10 000 pregnancies. PrAKI is substantially more prevalent in low- and middle-income countries. Unlike acute kidney injury episodes unrelated to pregnancy, PrAKI arises in a unique physiological context characterized by haemodynamic adaptation, immune tolerance, hormonal modulation, and the presence of the foetal–placental unit. These pregnancy-specific factors alter renal reserve, endothelial stability, and inflammatory responsiveness. The pathogenesis of PrAKI is multifactorial. Haemodynamic vasodilation and increased glomerular filtration rates during pregnancy reduce renal functional reserve, rendering the kidney vulnerable to hypovolemia and sepsis. Placental ischaemia drives the release of antiangiogenic factors, particularly soluble Fms-like tyrosine kinase-1 (sFlt-1) and soluble endoglin, causing systemic endothelial dysfunction and glomerular endotheliosis. Disruption of immune tolerance and complement regulation further contributes to microangiopathy and autoimmune-mediated renal injury. Structural urinary tract changes, metabolic stressors, and maternal comorbidities amplify susceptibility. Beyond the index event, PrAKI has lasting consequences. Maladaptive renal repair promotes chronic kidney disease and long-term cardiovascular risk, while foetal exposure to uremic toxins and placental dysfunction contribute to intrauterine growth restriction, prematurity, and reduced nephron mass with lifelong cardio-renal implications. Understanding the pathophysiology of PrAKI, particularly endothelial, immunologic, and placental interactions, has direct diagnostic and therapeutic implications, including the use of angiogenic biomarkers and targeted haemodynamic strategies. Future research must prioritize molecular predictors, global registries, and mechanistic studies to reduce intergenerational kidney and cardiovascular disease.

Abstract Background Pregnancy-related acute kidney injury (PrAKI) is an uncommon but serious complication associated with adverse maternal and neonatal outcomes, with limited data in high-resource settings. Methods We conducted a retrospective cohort study of 29 586 deliveries at Mayo Clinic hospitals between 2017 and 2022. PrAKI was defined using Kidney Disease Improving Global Outcomes (KDIGO) creatinine criteria. Maternal characteristics, nephrotoxin exposure, clinical interventions, and neonatal outcomes were compared between patients with and without PrAKI. Results PrAKI occurred in 341 patients (1.15%). Compared with controls, PrAKI was associated with higher rates of chronic hypertension (28.7% vs 5.2%), cesarean delivery (68.5% vs 35.1%), intensive care unit (ICU) admission (59.1% vs 6.2%), and nephrotoxin exposure (58.7% vs 31.6%) (all P < .0001). Dialysis was required in 7.1% of patients with PrAKI, with 1.17% remaining dependent at discharge and 0.88% at 30 and 90 days; no control patients required dialysis. Neonates of mothers with PrAKI had higher rates of prematurity and neonatal ICU (NICU) admission (48.7% vs 9.8%; P < .0001). Maternal mortality was rare. Conclusions PrAKI is a marker of severe maternal illness and is associated with adverse maternal and neonatal outcomes. Although most patients recover kidney function, a subset develop persistent dysfunction. Early identification, cautious use of nephrotoxic medications, and structured postpartum follow-up are essential.

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 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.

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: ( 1 ) rigorous multicenter validation focused on clinical outcomes, ( 2 ) integration of uncertainty quantification and causal modeling into AI tools, ( 3 ) development of clinician-centered interfaces that minimize cognitive load, and ( 4 ) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.