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- Labinsky, H, et al.
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An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study
- 2023
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In: Diagnostics (Basel, Switzerland). - : MDPI AG. - 2075-4418. ; 13:1
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Journal article (peer-reviewed)abstract
- Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS—Rheuma Care Manager (RCM)—including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients’ flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence.
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- Haschka, J, et al.
(author)
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Identification of circulating microRNA patterns in patients in psoriasis and psoriatic arthritis
- 2023
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In: Rheumatology (Oxford, England). - : Oxford University Press (OUP). - 1462-0332 .- 1462-0324. ; 62:10, s. 3448-3458
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Journal article (peer-reviewed)abstract
- ObjectivemiRNAs are small non-coding RNAs that control gene expression. Specific intra- and extracellular miRNA signatures have been identified in various diseases. Whether certain miRNA signatures are associated with psoriasis (PsO) and PsA is currently unknown. We aimed to search for circulating miRNA signatures associated with PsO and PsA patients.MethodsExpression of miRNAs was analysed by reverse transcription quantitative real-time PCR (RT-qPCR) in the serum of PsA, PsO patients and healthy controls. Demographic and disease-specific characteristics and imaging data from hand MRI were recorded. In the discovery phase, 192 miRNA assays were analysed in 48 samples (PsA, PsO, controls: each N = 16). For validation, 17 selected miRNAs were measured in the total population.ResultsA total of 141 patients and controls were analysed (51 PsA, 40 PsO, 50 controls). In the discovery phase 51 miRNAs in PsO and 64 miRNAs in PsA were down- or upregulated compared with controls, with 33 miRNAs being changed in both (adj. P < 0.05). The 17 top candidates from discovery were assessed in the validation phase, 9 of them discriminated PsA and PsO from controls [area under the curve (AUC) ≥0.70, all P < 0.05]. Four miRNAs (miR-19b-3p, miR-21-5p, miR-92a-3p and let-7b-5p) were significantly differently regulated between PsO and PsA. A combination of these miRNAs increased the AUC to 0.92 in multivariate regression model to discriminate PsO and PsA.ConclusionmiRNA signatures in PsA and PsO patients differ from controls. Nine miRNAs were differentially regulated in PsA and PsO patients, five of them previously reported to be involved in bone and cartilage metabolism, indicating an intimate association of psoriatic inflammation and bone/cartilage changes.
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