Abstract
Objective. To identify baseline prognostic factors of one year disability within a contemporary early inflammatory arthritis inception cohort and then develop a clinically useful tool which may support early patient education and decision making.
Methods. The Scottish Early Rheumatoid Arthritis (SERA) inception cohort is a prospective multicentre study of newly presenting RA and undifferentiated arthritis patients. SERA data were analysed to determine baseline predictors of disability (defined as a Health Assessment Questionnaire [HAQ] score ≥ 1) at one year. Clinical and psychosocial baseline exposures were submitted to a forward stepwise logistic regression model. The model was externally validated using newly accrued SERA data and subsequently converted into a prediction tool.
Results. Of the 578 participants (64.5% female), 36.7% (n=212) reported functional disability at one year. These were independently predicted by baseline disability (OR 2.67; 95%CI 1.98 – 3.59), depression (2.52; 1.18 – 5.37), anxiety (2.37; 1.33 – 4.21), in paid employment with absenteeism during the last week (1.19; 0.63 – 2.23), not in paid employment (2.36; 1.38 – 4.03) and being overweight (1.61; 1.04 – 2.50). External validation (using 113 newly acquired patients) evidenced good discriminative performance with a c-statistic of 0.74 and the calibration slope showed no evidence of model over-fit (p=0.31).
Conclusion. In the context of modern early inflammatory arthritis treatment paradigms, predictors of one year disability appear to be dominated by psychosocial rather than more traditional clinical measures. This alludes to the potential benefit of early access to non-pharmacological interventions targeting key psychosocial factors such as mental health and work disability.
Methods. The Scottish Early Rheumatoid Arthritis (SERA) inception cohort is a prospective multicentre study of newly presenting RA and undifferentiated arthritis patients. SERA data were analysed to determine baseline predictors of disability (defined as a Health Assessment Questionnaire [HAQ] score ≥ 1) at one year. Clinical and psychosocial baseline exposures were submitted to a forward stepwise logistic regression model. The model was externally validated using newly accrued SERA data and subsequently converted into a prediction tool.
Results. Of the 578 participants (64.5% female), 36.7% (n=212) reported functional disability at one year. These were independently predicted by baseline disability (OR 2.67; 95%CI 1.98 – 3.59), depression (2.52; 1.18 – 5.37), anxiety (2.37; 1.33 – 4.21), in paid employment with absenteeism during the last week (1.19; 0.63 – 2.23), not in paid employment (2.36; 1.38 – 4.03) and being overweight (1.61; 1.04 – 2.50). External validation (using 113 newly acquired patients) evidenced good discriminative performance with a c-statistic of 0.74 and the calibration slope showed no evidence of model over-fit (p=0.31).
Conclusion. In the context of modern early inflammatory arthritis treatment paradigms, predictors of one year disability appear to be dominated by psychosocial rather than more traditional clinical measures. This alludes to the potential benefit of early access to non-pharmacological interventions targeting key psychosocial factors such as mental health and work disability.
Original language | English |
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Pages (from-to) | 1596-1602 |
Number of pages | 7 |
Journal | Arthritis & Rheumatology |
Volume | 68 |
Issue number | 7 |
Early online date | 24 Jun 2016 |
DOIs | |
Publication status | Published - Jul 2016 |
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Gary Macfarlane
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Clinical Chair in Epidemiology
- Aberdeen Centre for Arthritis and Musculoskeletal Health (ACAMH)
- School of Medicine, Medical Sciences & Nutrition, Epidemiology Group
Person: Clinical Academic
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David McLernon
- School of Medicine, Medical Sciences & Nutrition, Applied Health Sciences - Senior Research Fellow
- School of Medicine, Medical Sciences & Nutrition, Centre for Health Data Science
- School of Medicine, Medical Sciences & Nutrition, Grampian Data Safe Haven (DaSH)
- School of Medicine, Medical Sciences & Nutrition, Medical Statistics
Person: Academic Related - Research