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Ann Intern Med. 1994 Jan 1;120(1):26-34.

Prediction of long-term mortality in patients with rheumatoid arthritis according to simple questionnaire and joint count measures.

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1
Vanderbilt University School of Medicine, Department of Medicine, Nashville, TN 37232.

Abstract

OBJECTIVE:

To describe mortality over 15 years in a cohort of patients with rheumatoid arthritis, according to a simple questionnaire and joint count.

DESIGN:

A cohort study with 15 years of follow-up.

SETTING:

University hospital outpatient clinic.

PATIENTS:

A cohort of 75 patients with rheumatoid arthritis.

MEASUREMENTS:

Quantitative baseline measures: demographic, articular (joint counts), clinical, questionnaire, and physical measures, including modified questionnaire and joint count measures with substantially fewer items.

RESULTS:

Although few deaths were seen in the first 3 years after baseline, the standard mortality ratio over 15 years was 1.62, similar to findings in other series. Significant predictors of mortality included age, formal education level, joint count, activities-of-daily-living questionnaire scores, disease adjustment scores, morning stiffness, comorbid cardiovascular disease, grip strength, modified walking time, and button test. Five-year survival in patients with the poorest status according to these quantitative measures was 40% to 60%, comparable to expected survival at that time of patients with three-vessel coronary artery disease or with stage 4 Hodgkin disease. Simplified measures, including a count using only 28 joints and a questionnaire using only 8 activities of daily living, were similar to the more elaborate traditional measures for predicting mortality.

CONCLUSION:

Higher mortality rates in patients with rheumatoid arthritis are predicted by more severe clinical disease, as in other chronic diseases. Severe rheumatoid arthritis may be identified using quantitative functional status questionnaires and joint counts, which can be ascertained in about 10 to 15 minutes in any clinical setting.

PMID:
8250453
[Indexed for MEDLINE]

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