Journal
SCIENTIFIC REPORTS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-85217-2
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Funding
- Projekt DEAL
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This study aimed to evaluate the role of rheumatic symptoms in guiding the differential diagnosis of Whipple's disease in patients with rheumatic disorders. The findings suggest that different types of rheumatoid arthritis have distinct pain characteristics, which can be used to predict the probability of diagnosing Whipple's disease.
Most patients with Whipple's disease have rheumatic symptoms. The aim of our prospective, questionnaire-based, non-interventional clinical study was to assess whether these symptoms are useful in guiding the differential diagnosis to the rheumatic disorders. Forty patients with Whipple's disease, followed by 20 patients for validation and 30 patients with rheumatoid-, 21 with psoriatic-, 15 with palindromic- and 25 with axial spondyloarthritis were recruited for the present investigation. Patients with Whipple's disease and patients with rheumatic disorders were asked to record rheumatic symptoms on pseudonymized questionnaires. The data obtained were subjected to multiple logistic regression analysis. Episodic pain with rapid onset, springing from joint to joint was most common in patients with palindromic arthritis and second most common and somewhat less conspicuous in Whipple's disease. Continuous pain in the same joints predominated in patients with rheumatoid-, psoriatic-, and axial spondyloarthritis. Multiple logistic equations resulted in a predicted probability for the diagnosis of Whipple's disease of 43.4 +/- 0.19% (M +/- SD) versus a significantly lower probability of 23.8 +/- 0.19% (M +/- SD) in the aggregate of patients with rheumatic disorders. Mean area under the curve (AUC)+/- SD was 0.781 +/- 0.044, 95% CI 0.695-0.867, asymptotic significance p<0.001. The logistic equations predicted probability for the diagnosis of Whipple's disease in the initial series of 40 patients of 43.4 +/- 0.19% was not significantly different in the subsequent 20 patients of 38.2 +/- 0.28% (M +/- SD) (p=0.376). The data may be useful in a predictive algorithm for diagnosing Whipple's disease. The project is registered as clinical study DRK S0001566.
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