Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19.

Individual testing of samples is time- and cost-intensive, particularly during an ongoing pandemic. Better practical alternatives to individual testing can significantly decrease the burden of disease on the healthcare system. Herein, we presented the clinical validation of Segtnan™ on 3929 patients. Segtnan™ is available as a mobile application entailing an AI-integrated personalized risk assessment approach with a novel data-driven equation for pooling of biological samples. The AI was selected from a comparison between 15 machine learning classifiers (highest accuracy = 80.14%) and a feed-forward neural network with an accuracy of 81.38% in predicting the rRT-PCR test results based on a designed survey with minimal clinical questions. Furthermore, we derived a novel pool-size equation from the pooling data of 54 published original studies. The results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively. Compared to Dorfman's method, our novel equation saved more tests significantly at high prevalence, i.e., 28% (p = 0.006), 40% (p = 0.00001), and 66% (p = 0.02). Lastly, we illustrated the feasibility of the Segtnan™ usage in clinically complex settings like emergency and psychiatric departments.

Plasma levels of multiple cardiovascular- and inflammation-related proteins analysed for associations with disease activity and anti-cyclic citrullinated peptide status in active early rheumatoid arthritis.

OBJECTIVES: To compare plasma levels of 92 cardiovascular- and inflammation-related proteins (CIRPs) and to analyse for associations with anti-cyclic citrullinated peptide (anti-CCP) status and disease activity in early and treatment-naive rheumatoid arthritis (RA). METHODS: Olink CVD-III-panel was used to measure 92 CIRP plasma levels in 180 early, treatment-naive, and highly inflamed RA patients from the OPERA trial. CIRP plasma levels as well as correlation between CIRP plasma levels and RA disease activity were compared between anti-CCP groups. CIRP level-based hierarchical cluster analysis was performed in each anti-CCP group separately. RESULTS: The study included 117 anti-CCP-positive and 63 anti-CCP-negative RA patients. Among the 92 CIRPs measured, the levels of chitotriosidase-1 (CHIT1) and tyrosine-protein-phosphatase non-receptor-type substrate-1 (SHPS-1) were increased and those of metalloproteinase inhibitor-4 (TIMP-4) decreased in the anti-CCP-negative group compared to anti-CCP-positive group. The strongest associations with RA disease activity were found for interleukin-2 receptor-subunit-alpha (IL2-RA) and E-selectin levels in the anti-CCP-negative group and for C-C-motif chemokine-16 levels (CCL16) in the anti-CCP-positive group. None of the differences passed the Hochberg sequential multiplicity test, however, the CIPRs were interacting and thus the prerequisites of the Hochberg procedure were not fulfilled. CIRP level-based cluster analysis identified two patient clusters in both anti-CCP groups. Demographic and clinical characteristics were similar in the two clusters for each anti-CCP group. CONCLUSIONS: In active and early RA, the findings regarding CHIT1, SHPS-1 TIMP-4, IL2-RA, E-selectin, and CCL16 differed between the two anti-CCP groups. In addition, we identified two patient clusters that were independent of the anti-CCP status.