Dillon, John

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.

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.