Immunology Archives | Page 4 of 4 | OM1 outcomes & advanced analytics

We would like to define treatment patterns for GCA patients with tocilizumab or glucocorticoids. For glucocorticoids, we need dose and days of supply information in order to track dose changes (tapering) over time. The medication dose/days of supply information can be inconsistent from my previous experience with EMR and open claims? Can you share if OM1 has used a tapering algorithm in published studies?

July 24, 2023

Our research team has not used a tapering algorithm to look at dose over time.To calculate dose for oral medications at single points in time, the team uses quantity dispensed divided by days supply multiplied by strength. Note, the Steroid Dose calculated in the dataset is an average daily dose suitable for oral and injectable[…]

How does OM1 identify Non-Radiographic Axial Spondyloarthritis (nr-axSpA)?

July 24, 2023

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[…]

Why are there more females than males in the PremiOM AxSpA dataset? AS (r-AxSpA) is more frequently diagnosed in males than females.

July 24, 2023

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[…]

Can phenotypes be identified in the SLE patient population?

July 20, 2023July 20, 2023

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.

Is the methodology behind the estimated SLEDAI scores validated?

July 20, 2023July 20, 2023

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/)