A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children Predicting changes in disease activity

A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children : Predicting changes in disease activity

Copyright © 2018 Elsevier Inc. All rights reserved.

Bibliographische Detailangaben
Veröffentlicht in:Clinical immunology (Orlando, Fla.). - 1999. - 198(2019) vom: 20. Jan., Seite 71-78
1. Verfasser: Smith, E M D (VerfasserIn)
Weitere Verfasser: Eleuteri, A, Goilav, B, Lewandowski, L, Phuti, A, Rubinstein, T, Wahezi, D, Jones, C A, Marks, S D, Corkhill, R, Pilkington, C, Tullus, K, Putterman, C, Scott, C, Fisher, A C, Beresford, M W
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:Clinical immunology (Orlando, Fla.)
Schlagworte:Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Juvenile systemic lupus erythematosus Lupus Nephritis Markov Multi-State model Urine biomarker panel Biomarkers Orosomucoid Ceruloplasmin EC 1.16.3.1
Beschreibung
Zusammenfassung:Copyright © 2018 Elsevier Inc. All rights reserved.
BACKGROUND: A urine 'biomarker panel' comprising alpha-1-acid-glycoprotein, ceruloplasmin, transferrin and lipocalin-like-prostaglandin-D synthase performs to an 'excellent' level for lupus nephritis identification in children cross-sectionally. The aim of this study was to assess if this biomarker panel predicts lupus nephritis flare/remission longitudinally
METHODS: The novel urinary biomarker panel was quantified by enzyme linked immunoabsorbant assay in participants of the United Kingdom Juvenile Systemic Lupus Erythematosus (UK JSLE) Cohort Study, the Einstein Lupus Cohort, and the South African Paediatric Lupus Cohort. Monocyte chemoattractant protein-1 and vascular cell adhesion molecule-1 were also quantified in view of evidence from other longitudinal studies. Serial urine samples were collected during routine care with detailed clinical and demographic data. A Markov Multi-State model of state transitions was fitted, with predictive clinical/biomarker factors assessed by a corrected Akaike Information Criterion (AICc) score (the better the model, the lower the AICc score)
RESULTS: The study included 184 longitudinal observations from 80 patients. The homogeneous multi-state Markov model of lupus nephritis activity AICc score was 147.85. Alpha-1-acid-glycoprotein and ceruloplasmin were identified to be the best predictive factors, reducing the AICc score to 139.81 and 141.40 respectively. Ceruloplasmin was associated with the active-to-inactive transition (hazard ratio 0.60 (95% confidence interval [0.39, 0.93])), and alpha-1-acid-glycoprotein with the inactive-to-active transition (hazard ratio 1.49 (95% confidence interval [1.10, 2.02])). Inputting individual alpha-1-acid-glycoprotein/ceruloplasmin values provides 3, 6 and 12 months probabilities of state transition
CONCLUSIONS: Alpha-1-acid-glycoprotein was predictive of active lupus nephritis flare, whereas ceruloplasmin was predictive of remission. The Markov state-space model warrants testing in a prospective clinical trial of lupus nephritis biomarker led monitoring
Beschreibung:Date Completed 28.10.2019
Date Revised 01.11.2024
published: Print-Electronic
Citation Status MEDLINE
ISSN:1521-7035
DOI:10.1016/j.clim.2018.10.021