Citations

Worldwide, more than 4 million patients with end-stage kidney disease require hemodialysis through an arteriovenous fistula (AVF). AVFs fail because of venous neointimal hyperplasia (VNH) resulting in venous stenosis formation. A phase 1 randomized trial in patients undergoing upper extremity AVF placement was performed to evaluate the safety and efficacy of autologous adipose–derived mesenchymal stem cells (MSCs) in improving AVF function. The mechanism of action by which MSCs exert their beneficial effects was investigated using AVFs created in mice and pigs treated with allogenic MSCs and xenotransplant using patient MSCs in CD1- Foxn1nu mice. At a median follow-up of 42 months, patients with MSC-treated AVFs had reduced time to maturation with an increase in the vein diameter compared with controls. AVFs treated with allogenic MSCs in mice and pigs had an increase in M2- and M1-like macrophages with a decrease in VNH. Transcriptomic analysis of AVFs treated with allogenic MSCs in mouse and MSC xenotransplants of patients with successful AVFs showed decreased peroxisome proliferator–activated receptor gamma ( Ppar γ) and leptin receptor ( Lepr ) and increased latent transforming growth factor–beta–binding protein 2 ( Ltbp2 ). PPARγ was reduced in CD68 (+) cells from successful AVFs and allogenic MSC-treated mouse AVFs. Venous stenosis and VNH formation were mitigated in AVFs of mice with Ppar γ ablation in immune cells. Conditioned medium from MSCs of responders versus nonresponders had decreased proinflammatory genes, senescence-associated proteins, and monocyte-to-macrophage differentiation induced by phorbol 12-myristate-13-acetate. Periadventitial delivery of MSCs to AVFs promoted positive vascular remodeling through decreased inflammatory responses.

Key Points This study demonstrates that peritoneal dialysis is a safe and feasible option for patients with autosomal dominant polycystic kidney disease, even with high cystic organ volumes. Larger organ volumes, including height-adjusted cumulative kidney and liver volume, were not associated with increased peritoneal dialysis–related complications. Background Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent genetic kidney disorder and the fourth leading cause of kidney failure. Peritoneal dialysis (PD), preferred for its home-based convenience and cost-effectiveness, is often underutilized in ADPKD because of concerns over enlarged kidneys and heightened risk of complications. Methods This retrospective cohort study used data from the Mayo Clinic Polycystic Kidney Disease Database to evaluate individuals with ADPKD undergoing PD. We analyzed demographics, clinical parameters, and PD-related parameters. Complications were correlated with kidney and liver volumes derived from pre-kidney failure imaging. Results A total of 155 individuals with ADPKD on PD were included, of whom 45.1% were male. The mean age at PD initiation was 54.3±12.8 years, and the mean body mass index was 28.0±7.0 kg/m 2 . The median duration of PD was 24.3 months (interquartile range [IQR], 10.7–43.1), with 21.9% transitioning to hemodialysis. The most common complications were abdominal hernias (30.3%) and peritonitis (23.9%), with a peritonitis rate of 0.11 episodes per patient-year. Imaging analyses performed on a subset of 50 patients showed a median height-adjusted total kidney liver volume of 2731.9 ml/m (IQR, 2102.5–3131.1) and a median height-adjusted total kidney volume of 1303.5 ml/m (IQR, 733.1–1829.4). Kaplan–Meier analysis demonstrated no differences in complications rates on the basis of height-adjusted total kidney liver volume or height-adjusted total kidney volume (above versus below median values) or body mass index categories. Multivariate Cox regression analysis revealed that higher height-adjusted cumulative organ volume was associated with a lower risk of PD-related complications (hazard ratio=0.56, P = 0.026). Conclusions PD is a safe and feasible treatment option for patients with ADPKD, with no increased risk of PD-related complications associated with larger height-adjusted cumulative organ volumes. Infectious complication rates in this cohort were within International Society of Peritoneal Dialysis guideline thresholds, further supporting the safety of PD in this population.

Background and Aims: Metabolic dysfunction–associated steatotic liver disease management guidelines have been published worldwide; we aimed to summarize, categorize, and compare their lifestyle intervention recommendations. Approach and Results: We searched metabolic dysfunction–associated steatotic liver disease/NAFLD management guidelines published between January 1, 2013, and June 31, 2024, through databases including PubMed/MEDLINE, Cochrane, and CINAHL. In total, 35 qualifying guidelines were included in the final analysis. Guideline recommendations were categorized into 5 domains (ie, weight reduction goals, physical activity, nutrition, alcohol, and tobacco smoking) and were ranked based on how frequently they appeared. A recommendation was defined as widely adopted if recommended in ≥24 (≥66.6%) of the guidelines. These included increasing physical activity; reducing body weight by 7%–10% to improve steatohepatitis and/or fibrosis; restricting caloric intake; undertaking 150–300 or 75–150 minutes/week of moderate or vigorous-intensity physical activity, respectively; and decreasing consumption of commercially produced fructose. The least mentioned topics, in ≤9 of the guidelines, evaluated environmental determinants of health, mental health, referring patients for psychological or cognitive behavioral therapy, using digital health interventions, and assessing patients’ social determinants of health. Conclusions: Most guidelines recommend weight reduction through physical activity and improving nutrition, as these have proven positive effects on health outcomes when sustained. However, gaps regarding mental health and the social and environmental determinants of metabolic dysfunction–associated steatotic liver disease were found. To optimize behavioral modifications and treatment, we recommend carrying out studies that will provide further evidence on social support, environmental factors, and mental health, as well as further exploring digital health interventions.

Key Points Measuring blood potassium has always required access to blood. The surface electrocardiogram, analyzed using an artificial intelligence algorithm, can detect hyperkalemia bloodlessly. The artificial intelligence-analyzed electrocardiogram exhibited a high negative predictive value but substantially lower positive predictive value. Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort ( N =40,128) had a mean age of 60 years, 48% were male, and 1% ( N =351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort ( N =2636) had a mean age of 65 years, 60% were male, and 3% ( N =87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Introduction: Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) augmented electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. Hypothesis: We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond the serum potassium measurement. Methods: We included 11 234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who had both a 12 lead ECG and blood potassium (K) level obtained at admission with K>=5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for probability of hyperkalemia (probability >=0.5 defined as positive). Hospital mortality was analyzed using logistic regression and survival to one year was estimated using Kaplan-Meier and Cox analysis. Results: The mean age was 70 years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20%. The mean K value was 4.2 mEq/L, and 13% (n=1460) had hyperkalemia. AI-ECG predicted hyperkalemia in 34% (n=3820). In-hospital mortality was higher in patients with laboratory and/or AI-ECG hyperkalemia, and these patients had lower survival out to 1 year. Mortality was lowest in patients with normal K and negative AI-ECG and highest in patients with both laboratory-confirmed hyperkalemia and AI-ECG predicted hyperkalemia. Patients with isolated laboratory hyperkalemia (false-negative AI-ECG) had higher mortality than those with isolated AI-ECG hyperkalemia (false-positive AI-ECG). Conclusions: AI-ECG probability of hyperkalemia, even with a normal laboratory K, was associated with higher short-term and long-term mortality in CICU patients. The laboratory K value and AI-ECG probability of hyperkalemia are complementary risk factors. The AI algorithm can detect prognostically important ECG abnormalities which may reflect the effects of K levels on the myocardium.