The Kidneys in Autoimmune Disease: From Basic Mechanisms to Clinical Outcomes

A special issue of Rheumato (ISSN 2674-0621).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3311

Special Issue Editors


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Guest Editor
Section Head of Rheumatology, Department of Nephrology and Rheumatology, University Medical Center Göttingen, 37075 Göttingen, Germany
Interests: systemic lupus erythematosus; lupus nephritis; sarcoidosis; systemic sclerosis; interstitial lung disease; myositis; vasculitis; emergency medicine
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Guest Editor
Section Head of Critical Care Medicine, Department of Nephrology and Rheumatology, University Medical Center Göttingen, 37075 Göttingen, Germany
Interests: kidney injury; kidney fibrosis; ANCA-associated vasculitis; inflammation; Covid-19; cytokine storm; hyper inflammation; Intensive Care Medicine; dialysis; vascular access

Special Issue Information

Dear Colleagues,

The kidneys are major organs affected by systemic autoimmune diseases, and acute kidney injury in these conditions has increased mortality. With this Special Issue, we aim to give an overview of systemic autoimmune diseases with a preference for kidney involvement. These include systemic vasculitis, systemic lupus erythematosus, and underrecognized conditions, such as primary Sjögren’s syndrome or sarcoidosis. In the latter condition, affection of the kidneys is rare, but newer studies show a higher prevalence of renal sarcoidosis than previously reported.

The recognition of renal manifestations in these conditions is crucial to improve the overall survival of patients. However, with the advent of novel therapeutic agents for many of these conditions, it is still unclear how they fit in the current therapeutic approaches considering costs, risks, benefits, and availability. Nevertheless, these are exciting times for clinicians facing complex systemic diseases with severe and often refractory organ manifestations.

This Special Issue aims to provide a broad overview of current and new knowledge on renal manifestations in systemic autoimmune diseases, including original studies and reviews on histopathology, biomarkers, clinical outcomes, and novel therapeutic strategies, especially using immunomodulating therapies.

Dr. Peter Korsten
Dr. Björn Tampe
Guest Editors

Manuscript Submission Information

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Keywords

  • systemic lupus erythematosus
  • lupus nephritis belimumab
  • ANCA-associated vasculitis
  • glomerulonephritis
  • acute kidney injury
  • histopathology
  • sarcoidosis

Published Papers (1 paper)

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Research

12 pages, 1553 KiB  
Article
A Preliminary Predictive Model for Proliferative Lupus Nephritis in Juvenile Systemic Lupus Erythematosus
by Sern Chin Lim, Elaine Wan Ling Chan, Shikriti Suprakash Mandal and Swee Ping Tang
Rheumato 2023, 3(1), 86-97; https://doi.org/10.3390/rheumato3010007 - 22 Feb 2023
Cited by 1 | Viewed by 1814
Abstract
Proliferative lupus nephritis, which is diagnosed by renal biopsy, has significant impact on the treatment choices and long-term prognosis of juvenile SLE (jSLE). Renal biopsies are however not always possible or available, thus leading to an ongoing search for alternative biomarkers. This study [...] Read more.
Proliferative lupus nephritis, which is diagnosed by renal biopsy, has significant impact on the treatment choices and long-term prognosis of juvenile SLE (jSLE). Renal biopsies are however not always possible or available, thus leading to an ongoing search for alternative biomarkers. This study aimed to develop a clinical predictive machine learning model using routine standard parameters as an alternative tool to evaluate the probability of proliferative lupus nephritis (ISN/RPS Class III or IV). Data were collected retrospectively from jSLE patients seen at Selayang Hospital from 2004 to 2021. A total of 22 variables including demographic, clinical and laboratory features were analyzed. A recursive feature elimination technique was used to identify factors to predict pediatric proliferative lupus nephritis. Various models were then used to build predictive machine learning models and assessed for sensitivity, specificity and accuracy. There were 194 jSLE patients (165 females), of which 111 had lupus nephritis (54 proliferative pattern). A combination of 11 variables consisting of gender, ethnicity, fever, nephrotic state, hypertension, urine red blood cells (RBC), C3, C4, duration of illness, serum albumin, and proteinuria demonstrated the highest accuracy of 79.4% in predicting proliferative lupus nephritis. A decision-tree model performed the best with an AROC of 69.9%, accuracy of 73.85%, sensitivity of 78.72% and specificity of 61.11%. A potential clinically useful predictive model using a combination of 11 non-invasive variables to collectively predict pediatric proliferative lupus nephritis in daily practice was developed. Full article
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