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; B. Goilav; L. Lewandowski; A. Phuti; T. Rubinstein; D. Wahezi; C. A. Jones; S. D. Marks; R. Corkhill; C. Pilkington; K. Tullus; C. Putterman; C. Scott; A. C. Fisher; M. W. Beresford

doi:10.1016/j.clim.2018.10.021

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, B. Goilav, L. Lewandowski, A. Phuti, T. Rubinstein, D. Wahezi, C. A. Jones, S. D. Marks, R. Corkhill, C. Pilkington, K. Tullus, C. Putterman, C. Scott, A. C. Fisher, M. W. Beresford

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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.

Original language	English (US)
Pages (from-to)	71-78
Number of pages	8
Journal	Clinical Immunology
Volume	198
DOIs	https://doi.org/10.1016/j.clim.2018.10.021
State	Published - Jan 2019

Keywords

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

ASJC Scopus subject areas

Immunology and Allergy
Immunology

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10.1016/j.clim.2018.10.021

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Smith, E. M. D., 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. (2019). A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity. Clinical Immunology, 198, 71-78. https://doi.org/10.1016/j.clim.2018.10.021

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 2019, 'A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity', Clinical Immunology, vol. 198, pp. 71-78. https://doi.org/10.1016/j.clim.2018.10.021

@article{fb51125eca1343508ddcc5de30f429b0,

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

abstract = "Background: A urine {\textquoteleft}biomarker panel{\textquoteright} comprising alpha-1-acid-glycoprotein, ceruloplasmin, transferrin and lipocalin-like-prostaglandin-D synthase performs to an {\textquoteleft}excellent{\textquoteright} 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.",

keywords = "Juvenile systemic lupus erythematosus, Lupus Nephritis, Markov Multi-State model, Urine biomarker panel",

author = "Smith, {E. M.D.} and A. Eleuteri and B. Goilav and L. Lewandowski and A. Phuti and T. Rubinstein and D. Wahezi and Jones, {C. A.} and Marks, {S. D.} and R. Corkhill and C. Pilkington and K. Tullus and C. Putterman and C. Scott and Fisher, {A. C.} and Beresford, {M. W.}",

note = "Publisher Copyright: {\textcopyright} 2018 Elsevier Inc.",

year = "2019",

month = jan,

doi = "10.1016/j.clim.2018.10.021",

language = "English (US)",

volume = "198",

pages = "71--78",

journal = "Clinical Immunology",

issn = "1521-6616",

publisher = "Academic Press Inc.",

}

TY - JOUR

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, B.

AU - Lewandowski, L.

AU - Phuti, A.

AU - Rubinstein, T.

AU - Wahezi, D.

AU - Jones, C. A.

AU - Marks, S. D.

AU - Corkhill, R.

AU - Pilkington, C.

AU - Tullus, K.

AU - Putterman, C.

AU - Scott, C.

AU - Fisher, A. C.

AU - Beresford, M. W.

PY - 2019/1

Y1 - 2019/1

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

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