While assessments like EASI and the SCORAD index are commonly used in clinical trials, they are not practical for clinical practice because they are time-consuming and were not developed and validated for routine care. The dataset is a reflection of assessments conducted in routine care.
OM1 flags a cohort of nr-AxSpA patients within the OM1 AxSpA dataset via clinical abstraction and machine learning (ML).Patients can also be identified using ICD-10 diagnosis code M45.A, Non-radiographic axial spondyloarthritis, effective October 2020. The nr-AxSpA observation identifies patients who have been flagged with a non-radiographic status (True/False) and the qualifying date on which they[…]
While a male predominance is found in radiographic AxSpA (r-AxSpA), a more balanced male to female distribution has repeatedly been reported for nr-AxSpA. In addition, to provide a broad swath of patients with potential nr-AxSpA who may not have been coded as AxSpA prior to the nr-AxSpA ICD-10 code becoming available, the full dataset includes[…]
Yes. The PremiOM SLE dataset includes deep clinical data, including laboratory assessments, observed and estimated disease activity measures, and treatments in patients with systemic disease. These data can be used in a range of methods to determine either a priori defined or machine-learning identified phenotypes. Please find our Lupus ebook for additional information.
Yes. The machine-learning based methodology for estimating SLEDAI scores for patients is fully validated and published (Alves P, Bandaria J, Leavy MB, et al. Validation of a machine learning approach to estimate Systemic Lupus Erythematosus Disease Activity Index score categories and application in a real-world dataset. RMD Open. 2021 May;7(2):e001586. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141448/)
