One year followup variables predict disability 5 years after presentation with inflammatory polyarthritis with greater accuracy than at baseline.

OBJECTIVE

To compare the accuracy of simple demographic and clinical variables recorded at baseline with those recorded after one year followup, in predicting self-reported functional disability recorded 5 years after initial assessment in patients with early inflammatory polyarthritis (IP).

METHODS

We followed annually for 5 years 528 patients registered by the Norfolk Arthritis Register (a primary care based cohort of patients with early IP) using the Health Assessment Questionnaire (HAQ). Backward stepwise logistic regression was used to determine the clinical and demographic variables, collected at either baseline or first followup, that were associated with disability (HAQ > or = 1.00/> or =1.50) at 5 years.

RESULTS

At the 5th anniversary assessment, the prevalence of moderate disability (HAQ > or = 1.00) was 47%. Twenty-nine percent reported more severe disability (HAQ > or = 1.50). Variables recorded at first anniversary assessment were better able to predict patients at risk of developing a poor outcome than baseline variables. Multivariate methods identified age at symptom onset, HAQ score, presence of nodules, and a statistically derived factor describing joint tenderness recorded at first year as important predictors of both moderate disability (HAQ > or =1.00) and a higher level of disability (HAQ > or = 1.50). When tested in an independent validation sample, the accuracy of the models generated from data recorded at the first year was 76% (HAQ > or = 1.00) and 83% (HAQ > or = 1.50).

CONCLUSION

It was possible to predict disability at 5 years with high accuracy using simple clinical variables and demographic data collected 4 or 5 years previously. First year HAQ score was the strongest predictor of future disability. HAQ score at 5 years could be predicted more accurately using data collected at first anniversary visit than using data recorded at baseline.