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Ayoub I, Wolf BJ, Geng L, et al. Prediction models of treatment response in lupus nephritis. Kidney Int. 2021;doi: 10.1016/j.kint.2021.11.014.
Ayoub, I., Wolf, B. J., Geng, L., Song, H., Khatiwada, A., Tsao, B. P., Oates, J. C., & Rovin, B. H. (2021). Prediction models of treatment response in lupus nephritis. Kidney international, . https://doi.org/10.1016/j.kint.2021.11.014
Ayoub, Isabelle, et al. "Prediction models of treatment response in lupus nephritis." Kidney international vol. (2021). doi: https://doi.org/10.1016/j.kint.2021.11.014
Ayoub I, Wolf BJ, Geng L, Song H, Khatiwada A, Tsao BP, Oates JC, Rovin BH. Prediction models of treatment response in lupus nephritis. Kidney Int. 2021 Dec 03; doi: 10.1016/j.kint.2021.11.014. Epub 2021 Dec 03. PMID: 34871620.
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Kidney Int. 2021 Dec 03; doi: 10.1016/j.kint.2021.11.014. Epub 2021 Dec 03.

Prediction models of treatment response in lupus nephritis.

Kidney international

Isabelle Ayoub, Bethany J Wolf, Linyu Geng, Huijuan Song, Aastha Khatiwada, Betty P Tsao, Jim C Oates, Brad H Rovin

Affiliations

  1. Division of Nephrology, Department of Medicine, The Ohio State University, Columbus, Ohio, USA.
  2. Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
  3. Division of Rheumatology and Immunology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.
  4. Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado, USA.
  5. Division of Rheumatology and Immunology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA. Electronic address: [email protected].
  6. Division of Nephrology, Department of Medicine, The Ohio State University, Columbus, Ohio, USA. Electronic address: [email protected].

PMID: 34871620 DOI: 10.1016/j.kint.2021.11.014

Abstract

In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three-quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.

Copyright © 2021 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Keywords: lupus nephritis; outcomes in lupus nephritis; prediction models in lupus nephritis; urine biomarkers

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