期刊
FRONTIERS IN MEDICINE
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2022.774945
关键词
eHealth; symptom assessment; diagnostics; rheumatology; mHealth (mobile health)
资金
- EIT Health [19436]
- JPAST
- European Institute of Innovation and Technology (EIT)
- European Union
- Innovative Medicines Initiative 2 Joint Undertaking [777357]
- Deutsche Forschungsgemeinschaft [DFG-FOR2886 PANDORA, CRC1181]
- Bundesministerium fur Bildung und Forschung (BMBF)
- ERC Synergy grant 4D Nanoscope
- HIPPOCRATES and RTCure
- Emerging Fields Initiative MIRACLE of the Friedrich-Alexander-Universitat Erlangen-Nurnberg
- Else Kroner-Memorial Scholarship [2019_EKMS.27]
Rheumatic? is an online tool developed for rheumatology diagnosis, and its ability to differentiate rheumatic symptoms was validated through a retrospective study. The study results showed that Rheumatic? performed well in risk scoring.
Introduction: Digital diagnostic decision support tools promise to accelerate diagnosis and increase health care efficiency in rheumatology. Rheumatic? 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 Rheumatic? 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. Materials and Methods: The performance of Rheumatic? was tested using in three university rheumatology centers: (A) patients at Risk for RA (Karolinska Institutet, n = 50 individuals with musculoskeletal complaints and anti-citrullinated protein antibody positivity) (B) patients with early joint swelling [dataset B (Erlangen) n = 52]. (C) Patients with early arthritis where the clinician considered it likely to be of auto-immune origin [dataset C (Leiden) n = 73]. In dataset A we tested whether Rheumatic? could predict the development of arthritis. In dataset B and C we tested whether Rheumatic? 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 Rheumatic? scoring threshold. Results: The total test scores differentiated between: (A) Individuals developing arthritis or not, median 245 vs. 163, P < 0.0001, AUC-ROC = 75.3; (B) patients with an immune-mediated arthritic disease or not median 191 vs. 107, P < 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, P < 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. Conclusion: Rheumatic? 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.
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