PT  - JOURNAL ARTICLE
AU  - Rukia Swaleh
AU  - Taylor McGuckin
AU  - Tyler W. Myroniuk
AU  - Donna Manca
AU  - Karen Lee
AU  - Arya M. Sharma
AU  - Denise Campbell-Scherer
AU  - Roseanne O. Yeung
TI  - Using the Edmonton Obesity Staging System in the real world: a feasibility study based on cross-sectional data
AID  - 10.9778/cmajo.20200231
DP  - 2021 Oct 01
TA  - CMAJ Open
PG  - E1141--E1148
VI  - 9
IP  - 4
4099  - http://www.cmajopen.ca/content/9/4/E1141.short
4100  - http://www.cmajopen.ca/content/9/4/E1141.full
SO  - CMAJ2021 Oct 01; 9
AB  - Background: The Edmonton Obesity Staging System (EOSS) combined with body mass index (BMI) enables improved functional and prognostic assessment for patients. To facilitate application of the EOSS in practice, we aimed to create tools for capturing comorbidity assessments in electronic medical records and for automating the calculation of a patient’s EOSS stage.Methods: In this feasibility study, we used cross-sectional data to create a clinical dashboard to calculate and display the relation between BMI and EOSS and the prevalence of related comorbidities. We obtained data from the Northern Alberta Primary Care Research Network and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). We included patients at least 18 years of age with BMI between 30 and 60 who visited a network clinic between July 2016 and July 2019. We calculated descriptive statistics and used stepwise ordinary least squares regression to assess the contributions of age, sex and BMI to EOSS variation.Results: We created a clinical dashboard using the CPCSSN data presentation tool. Of the total 31 496 patients included in the study, 23 460 had a BMI of at least 30; BMI was unavailable for 8036 patients. Within each EOSS disease severity stage, there were similar proportions of patients from each BMI class (e.g., patients with EOSS stage 2 included 51.8% of those with BMI class I, 55.3% of those with BMI class II and 58.8% of those with BMI class III).Interpretation: Using data from primary care electronic medical records, it was feasible to create a clinical dashboard for obesity that highlighted the severity and stage of obesity. Making this information easily accessible for individual clinical care and practice-level quality improvement may advance obesity care.