Rheumatic ?-A Digital Diagnostic Decision Support Tool for Individuals Suspecting Rheumatic Diseases: A Multicenter Pilot Validation Study

• Published in: Frontiers in medicine Vol. 9; p. 774945
• Main Authors: Knevel, Rachel, Knitza, Johannes, Hensvold, Aase, Circiumaru, Alexandra, Bruce, Tor, Evans, Sebastian, Maarseveen, Tjardo, Maurits, Marc, Beaart-van de Voorde, Liesbeth, Simon, David, Kleyer, Arnd, Johannesson, Martina, Schett, Georg, Huizinga, Tom, Svanteson, Sofia, Lindfors, Alexandra, Klareskog, Lars, Catrina, Anca
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 25.04.2022
• Subjects: diagnostics; eHealth; Medicin och hälsovetenskap; Medicine; mHealth (mobile health); rheumatology; symptom assessment; rheumatology; mHealth (mobile health); symptom assessment; diagnostics; eHealth
• ISSN: 2296-858X; 2296-858X
• DOI: 10.3389/fmed.2022.774945

Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. is an online tool developed by specialists in rheumatology and general medicine together with patients and patient organizations. It calculates a risk score for several rheumatic diseases. We ran a pilot study retrospectively testing for its ability to differentiate symptoms from existing or emerging immune-mediated rheumatic diseases from other rheumatic and musculoskeletal complaints and disorders in patients visiting rheumatology clinics. The performance of was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) = 73]. In dataset A we tested whether could predict the development of arthritis. In dataset B and C we tested whether could predict the development of an immune-mediated rheumatic diseases. We examined the discriminative power of the total score with the Wilcoxon rank test and the area-under-the-receiver-operating-characteristic curve (AUC-ROC). Next, we calculated the test characteristics for these patients passing the first or second expert-based scoring threshold. The total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, < 0.0001, AUC-ROC = 79.0; but less patients with an immune-mediated arthritic disease or not amongst those where the clinician already considered an immune mediated disease most likely (median 262 vs. 212, < 0.0001, AUC-ROC = 53.6). Threshold-1 (advising to visit primary care doctor) was highly specific in dataset A and B (0.72, 0.87, and 0.23, respectively) and sensitive (0.67, 0.61, and 0.67). Threshold-2 (advising to visit rheumatologic care) was very specific in all three centers but not very sensitive: specificity of 1.0, 0.96, and 0.91, sensitivity 0.05, 0.07, 0.14 in dataset A, B, and C, respectively. is a web-based patient-centered multilingual diagnostic tool capable of differentiating immune-mediated rheumatic conditions from other musculoskeletal problems. The current scoring system needs to be further optimized.