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

E. M.D. Smith, A. Eleuteri, Beatrice Goilav, L. Lewandowski, A. Phuti, Tamar Rubinstein, D. Wahezi, C. A. Jones, S. D. Marks, R. Corkhill, C. Pilkington, K. Tullus, Chaim Putterman, C. Scott, A. C. Fisher, M. W. Beresford

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
JournalClinical Immunology
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Orosomucoid
Lupus Nephritis
Ceruloplasmin
Biomarkers
Urine
prostaglandin R2 D-isomerase
Confidence Intervals
Space Simulation
Lipocalins
Vascular Cell Adhesion Molecule-1
Chemokine CCL2
Transferrin
Systemic Lupus Erythematosus
Longitudinal Studies
Cohort Studies
Demography
Clinical Trials
Pediatrics
Enzymes

Keywords

  • Juvenile systemic lupus erythematosus
  • Lupus Nephritis
  • Markov Multi-State model
  • Urine biomarker panel

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

Cite this

A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children : Predicting changes in disease activity. / Smith, E. M.D.; Eleuteri, A.; Goilav, Beatrice; Lewandowski, L.; Phuti, A.; Rubinstein, Tamar; Wahezi, D.; Jones, C. A.; Marks, S. D.; Corkhill, R.; Pilkington, C.; Tullus, K.; Putterman, Chaim; Scott, C.; Fisher, A. C.; Beresford, M. W.

In: Clinical Immunology, 01.01.2018.

Research output: Contribution to journalArticle

Smith, EMD, Eleuteri, A, Goilav, B, Lewandowski, L, Phuti, A, Rubinstein, T, Wahezi, D, Jones, CA, Marks, SD, Corkhill, R, Pilkington, C, Tullus, K, Putterman, C, Scott, C, Fisher, AC & Beresford, MW 2018, 'A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity', Clinical Immunology. https://doi.org/10.1016/j.clim.2018.10.021
Smith, E. M.D. ; Eleuteri, A. ; Goilav, Beatrice ; Lewandowski, L. ; Phuti, A. ; Rubinstein, Tamar ; Wahezi, D. ; Jones, C. A. ; Marks, S. D. ; Corkhill, R. ; Pilkington, C. ; Tullus, K. ; Putterman, Chaim ; Scott, C. ; Fisher, A. C. ; Beresford, M. W. / A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children : Predicting changes in disease activity. In: Clinical Immunology. 2018.
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abstract = "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.",
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T1 - A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children

T2 - Predicting changes in disease activity

AU - Smith, E. M.D.

AU - Eleuteri, A.

AU - Goilav, Beatrice

AU - Lewandowski, L.

AU - Phuti, A.

AU - Rubinstein, Tamar

AU - Wahezi, D.

AU - Jones, C. A.

AU - Marks, S. D.

AU - Corkhill, R.

AU - Pilkington, C.

AU - Tullus, K.

AU - Putterman, Chaim

AU - Scott, C.

AU - Fisher, A. C.

AU - Beresford, M. W.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Juvenile systemic lupus erythematosus

KW - Lupus Nephritis

KW - Markov Multi-State model

KW - Urine biomarker panel

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