Citations

ABSTRACT Background Cognitive impairment (CI) is a critical complication in peritoneal dialysis (PD) patients, associated with decreased quality of life and increased hospitalization. Despite its significant impact, the prevalence, risk factors, and consequences of CI in PD patients are not well understood. We aimed to determine the prevalence, risk factors, and outcomes of CI in PD patients. Methods We performed systematic reviews in OVID Medline, Embase, and Cochrane databases until February 2024 to identify cross-sectional and cohort studies on prevalence of CI (identified by cognitive assessment scales) in PD patients. The Newcastle–Ottawa Scale was used to assess risk of bias. A pooled meta-analysis of CI prevalence in PD and a subgroup analysis comparing the risk of CI between PD and non-PD settings were performed using a random-effects model. Results A total of 19 studies were identified, involving 2882 PD patients. The pooled prevalence of CI in PD patients was 47.7% (95%CI: 35.8–59.9%). CI in patients undergoing PD appears to be associated with older age, female gender, lower levels of education, and is linked to higher rates of hospitalization and peritonitis, compared to those without CI. However, it is not associated with increased mortality. Compared to hemodialysis, PD showed a trend toward a lower risk of CI (OR 0.64, 95%CI 0.39–1.03; P = .068). Conclusion CI is highly prevalent and associated with several adverse clinical outcomes in PD patients. These findings could contribute to facilitate the development of screening and early intervention strategies to reduce the burden of disease in this population.

ABSTRACTBackgroundDespite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.MethodsUsing Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.ResultsThe median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re‐transplant patients characterized Cluster 1. Cluster 2 was characterized by first‐time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3‐year patient death, and graft failure. Cluster 3 had a higher risk of death‐censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient‐, graft‐, and death‐censored graft survival at 1 and 3 years. Compared to non‐AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non‐AKI: 25.5%); however, there were comparable long‐term outcomes specific to death‐censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non‐AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non‐AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non‐AKI: 86.0%).ConclusionsIn this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.

Abstract: The integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging represents a transformative approach to glaucoma diagnosis and management. This article explores various AS imaging techniques, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases. The review focuses on advancements in AI, including machine learning and deep learning, which enhance image analysis and automate complex processes in glaucoma care, and provides current evidence on the performance and clinical applications of these technologies. In addition, the article discusses the integration of big data, detailing its potential to revolutionize medical imaging by enabling comprehensive data analysis, fostering enhanced clinical decision-making, and facilitating personalized treatment strategies. In this article, we address the challenges of standardizing and integrating diverse data sets and suggest that future collaborations and technological advancements could substantially improve the management and research of glaucoma. This synthesis of current evidence and new technologies emphasizes their clinical relevance, offering insights into their potential to change traditional approaches to glaucoma evaluation and care.