Prediction of inactive disease in juvenile idiopathic arthritis: a multicentre observational cohort study

Evert H Pieter van Dijkhuizen, Orfeas Aidonopoulos, Nienke M Ter Haar, Denise Pires Marafon, Silvia Magni-Manzoni, Yannis E Ioannidis, Lorenza Putignani, Sebastiaan J Vastert, Clara Malattia, Fabrizio De Benedetti, Alberto Martini

Research output: Contribution to journalArticle

Abstract

Objectives: To predict the occurrence of inactive disease in JIA in the first 2 years of disease.

Methods: An inception cohort of 152 treatment-naïve JIA patients with disease duration <6 months was analysed. Potential predictors were baseline clinical variables, joint US, gut microbiota composition and a panel of inflammation-related compounds in blood plasma. Various algorithms were employed to predict inactive disease according to Wallace criteria at 6-month intervals in the first 2 years. Performance of the models was evaluated using the split-cohort technique. The cohort was analysed in its entirety, and separate models were developed for oligoarticular patients, polyarticular RF negative patients and ANA positive patients.

Results: All models analysing the cohort as a whole showed poor performance in test data [area under the curve (AUC): <0.65]. The subgroup models performed better. Inactive disease was predicted by lower baseline juvenile arthritis DAS (JADAS)-71 and lower relative abundance of the operational taxonomic unit Mogibacteriaceae for oligoarticular patients (AUC in test data: 0.69); shorter duration of morning stiffness, higher haemoglobin and lower CXCL-9 levels at baseline for polyarticular RF negative patients (AUC in test data: 0.69); and shorter duration of morning stiffness and higher baseline haemoglobin for ANA positive patients (AUC in test data: 0.72).

Conclusion: Inactive disease could not be predicted with satisfactory accuracy in the whole cohort, likely due to disease heterogeneity. Interesting predictors were found in more homogeneous subgroups. These need to be validated in future studies.

Original languageEnglish
Pages (from-to)1752-1760
Number of pages9
JournalRheumatology (Oxford, England)
Volume57
Issue number10
DOIs
Publication statusPublished - Oct 1 2018

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Juvenile Arthritis
Observational Studies
Cohort Studies
Area Under Curve
amsonic acid
Hemoglobins
Inflammation

Keywords

  • Algorithms
  • Arthritis, Juvenile/pathology
  • Child
  • Child, Preschool
  • Female
  • Humans
  • Male
  • Predictive Value of Tests
  • Prognosis
  • Prospective Studies
  • Severity of Illness Index

Cite this

van Dijkhuizen, E. H. P., Aidonopoulos, O., Ter Haar, N. M., Pires Marafon, D., Magni-Manzoni, S., Ioannidis, Y. E., ... Martini, A. (2018). Prediction of inactive disease in juvenile idiopathic arthritis: a multicentre observational cohort study. Rheumatology (Oxford, England), 57(10), 1752-1760. https://doi.org/10.1093/rheumatology/key148

Prediction of inactive disease in juvenile idiopathic arthritis : a multicentre observational cohort study. / van Dijkhuizen, Evert H Pieter; Aidonopoulos, Orfeas; Ter Haar, Nienke M; Pires Marafon, Denise; Magni-Manzoni, Silvia; Ioannidis, Yannis E; Putignani, Lorenza; Vastert, Sebastiaan J; Malattia, Clara; De Benedetti, Fabrizio; Martini, Alberto.

In: Rheumatology (Oxford, England), Vol. 57, No. 10, 01.10.2018, p. 1752-1760.

Research output: Contribution to journalArticle

van Dijkhuizen, Evert H Pieter ; Aidonopoulos, Orfeas ; Ter Haar, Nienke M ; Pires Marafon, Denise ; Magni-Manzoni, Silvia ; Ioannidis, Yannis E ; Putignani, Lorenza ; Vastert, Sebastiaan J ; Malattia, Clara ; De Benedetti, Fabrizio ; Martini, Alberto. / Prediction of inactive disease in juvenile idiopathic arthritis : a multicentre observational cohort study. In: Rheumatology (Oxford, England). 2018 ; Vol. 57, No. 10. pp. 1752-1760.
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T2 - a multicentre observational cohort study

AU - van Dijkhuizen, Evert H Pieter

AU - Aidonopoulos, Orfeas

AU - Ter Haar, Nienke M

AU - Pires Marafon, Denise

AU - Magni-Manzoni, Silvia

AU - Ioannidis, Yannis E

AU - Putignani, Lorenza

AU - Vastert, Sebastiaan J

AU - Malattia, Clara

AU - De Benedetti, Fabrizio

AU - Martini, Alberto

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N2 - Objectives: To predict the occurrence of inactive disease in JIA in the first 2 years of disease.Methods: An inception cohort of 152 treatment-naïve JIA patients with disease duration <6 months was analysed. Potential predictors were baseline clinical variables, joint US, gut microbiota composition and a panel of inflammation-related compounds in blood plasma. Various algorithms were employed to predict inactive disease according to Wallace criteria at 6-month intervals in the first 2 years. Performance of the models was evaluated using the split-cohort technique. The cohort was analysed in its entirety, and separate models were developed for oligoarticular patients, polyarticular RF negative patients and ANA positive patients.Results: All models analysing the cohort as a whole showed poor performance in test data [area under the curve (AUC): <0.65]. The subgroup models performed better. Inactive disease was predicted by lower baseline juvenile arthritis DAS (JADAS)-71 and lower relative abundance of the operational taxonomic unit Mogibacteriaceae for oligoarticular patients (AUC in test data: 0.69); shorter duration of morning stiffness, higher haemoglobin and lower CXCL-9 levels at baseline for polyarticular RF negative patients (AUC in test data: 0.69); and shorter duration of morning stiffness and higher baseline haemoglobin for ANA positive patients (AUC in test data: 0.72).Conclusion: Inactive disease could not be predicted with satisfactory accuracy in the whole cohort, likely due to disease heterogeneity. Interesting predictors were found in more homogeneous subgroups. These need to be validated in future studies.

AB - Objectives: To predict the occurrence of inactive disease in JIA in the first 2 years of disease.Methods: An inception cohort of 152 treatment-naïve JIA patients with disease duration <6 months was analysed. Potential predictors were baseline clinical variables, joint US, gut microbiota composition and a panel of inflammation-related compounds in blood plasma. Various algorithms were employed to predict inactive disease according to Wallace criteria at 6-month intervals in the first 2 years. Performance of the models was evaluated using the split-cohort technique. The cohort was analysed in its entirety, and separate models were developed for oligoarticular patients, polyarticular RF negative patients and ANA positive patients.Results: All models analysing the cohort as a whole showed poor performance in test data [area under the curve (AUC): <0.65]. The subgroup models performed better. Inactive disease was predicted by lower baseline juvenile arthritis DAS (JADAS)-71 and lower relative abundance of the operational taxonomic unit Mogibacteriaceae for oligoarticular patients (AUC in test data: 0.69); shorter duration of morning stiffness, higher haemoglobin and lower CXCL-9 levels at baseline for polyarticular RF negative patients (AUC in test data: 0.69); and shorter duration of morning stiffness and higher baseline haemoglobin for ANA positive patients (AUC in test data: 0.72).Conclusion: Inactive disease could not be predicted with satisfactory accuracy in the whole cohort, likely due to disease heterogeneity. Interesting predictors were found in more homogeneous subgroups. These need to be validated in future studies.

KW - Algorithms

KW - Arthritis, Juvenile/pathology

KW - Child

KW - Child, Preschool

KW - Female

KW - Humans

KW - Male

KW - Predictive Value of Tests

KW - Prognosis

KW - Prospective Studies

KW - Severity of Illness Index

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DO - 10.1093/rheumatology/key148

M3 - Article

C2 - 29931340

VL - 57

SP - 1752

EP - 1760

JO - Rheumatology

JF - Rheumatology

SN - 1462-0324

IS - 10

ER -