ABSTRACT
Chronic kidney disease (CKD) incidence is growing worldwide, with a significant percentage of CKD patients reaching end-stage renal disease (ESRD) and requiring kidney replacement therapies (KRT). Peritoneal dialysis (PD) is a convenient KRT presenting benefices as home therapy. In PD patients, the peritoneum is chronically exposed to PD fluids containing supraphysiologic concentrations of glucose or other osmotic agents, leading to the activation of cellular and molecular processes of damage, including inflammation and fibrosis. Importantly, peritonitis episodes enhance peritoneum inflammation status and accelerate peritoneal injury. Here, we review the role of immune cells in the damage of the peritoneal membrane (PM) by repeated exposure to PD fluids during KRT as well as by bacterial or viral infections. We also discuss the anti-inflammatory properties of current clinical treatments of CKD patients in KRT and their potential effect on preserving PM integrity. Finally, given the current importance of coronavirus disease 2019 (COVID-19) disease, we also analyze here the implications of this disease in CKD and KRT.
Subject(s)
COVID-19 , Kidney Failure, Chronic , Peritonitis , Renal Insufficiency, Chronic , Humans , Peritoneum , Renal Dialysis/adverse effects , COVID-19/complications , Dialysis Solutions/adverse effects , Peritonitis/chemically induced , Renal Insufficiency, Chronic/complications , Inflammation/complications , Kidney Failure, Chronic/therapy , Kidney Failure, Chronic/complications , ImmunityABSTRACT
The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.