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Abstract Number: 2180

Differences in Longitudinal Disease Activity Measures between Research Cohort and Non-Cohort Participants with Rheumatoid Arthritis Using Electronic Health Record Data

Milena Gianfrancesco1, Laura Trupin1, Charles McCulloch2, Stephen Shiboski3, Jonathan Graf4, Gabriela Schmajuk5 and Jinoos Yazdany6, 1Medicine/Rheumatology, University of California, San Francisco, San Francisco, CA, 2Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 3Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 4Department of Medicine, Division of Rheumatology Zuckerberg San Francisco General Hospital, University of California, San Francisco, San Francisco, CA, 5San Francisco VA Medical Center, San Francisco, CA, 6University of California, San Francisco, San Francisco, CA

Meeting: 2018 ACR/ARHP Annual Meeting

Keywords: Disease Activity, Electronic Health Record, epidemiologic methods and rheumatoid arthritis (RA)

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Session Information

Date: Tuesday, October 23, 2018

Session Title: Health Services Research Poster III – ACR/ARHP

Session Type: ACR/ARHP Combined Abstract Session

Session Time: 9:00AM-11:00AM

Background/Purpose: Research using electronic health records (EHR) may offer advantages over traditional observational studies, including lower costs and greater generalizability to a broader patient population; however, EHR data may be more biased as a result of the high prevalence of missing data. Little research is available that directly evaluates the potential benefits and weaknesses of each study design within a single population. We examined differences in baseline demographics and disease outcomes between RA patients enrolled in a cohort study with more standardized data collection and patients whose data comes purely from the EHR within the same health system. We also compared the availability of measures and prevalence of missing data between the two groups and explored differences in longitudinal predictors of RA disease activity.

Methods: We included individuals with an RA diagnosis (ICD-9 code 714.0) and at least 2 rheumatology clinic visits within 12 months between 2013-2017 from the EHR of a public hospital (n=377 patients, n=2,269 visits). Approximately half were also enrolled in an RA cohort study. In order to examine if longitudinal differences in disease activity were present between cohort and non-cohort groups, mixed effects models were used to evaluate the association between sex, race/ethnicity, age, body mass index (BMI), smoking status and medication on Clinical Disease Activity Index (CDAI) score. Interaction between covariates and participant status (cohort vs. non-cohort) was also assessed.

Results: No significant baseline differences between cohort (n=187) and non-cohort (n=190) participants were found with respect to sex, age, race/ethnicity, smoking status, or disease activity measures (Table). Variables with a higher prevalence of missing data in non-cohort individuals compared to cohort individuals included language (14% vs. 0%), BMI (14% vs. 4%), smoking status (18% vs. 6%), and certain disease activity measures (21-22% vs. 3-6%). Black, non-Hispanic race/ethnicity was associated with a higher CDAI score over the study period compared to white, non-Hispanic individuals in non-cohort participants, while no association was found in cohort participants (p-interaction = 0.07).

Conclusion: Non-cohort participants from the EHR were comparable to a research cohort drawn from the same health system across some variables, but demonstrated more severe disease trajectories in racial/ethnic minorities. While challenges remain given the prevalence of missing data for specific variables in the EHR, utilizing EHR data repositories may inform our understanding of disease trajectories for RA patients who are not adequately captured in research cohorts.


Table 1. Demographic and disease characteristics of RA cohort and non-cohort participants from the EHR of a public hospital in California, 2013-2017.

Cohort

Participants

(n=187)

Non-Cohort Participants

(n=190)

P-value

Sex (female)

151 (81)

159 (84)

0.46

Age

57.49 (12.56)

56.74 (11.67)

0.55

Race/Ethnicity

0.93

White, non-Hispanic

16 (9)

13 (7)

Asian

69 (37)

65 (37)

Black, non-Hispanic

12 (6)

10 (6)

Hispanic

89 (48)

90 (50)

Language

<0.001

English

64 (34)

76 (40)

Spanish

69 (37)

53 (28)

Chinese – Cantonese

38 (20)

19 (10)

Other

16 (9)

15 (8)

Unknown

0 (0)

27 (14)

Body Mass Index

27.60 (5.40)

28.95 (7.00)

0.05

Current Smoker

16 (10)

13 (8)

0.48

Biologic/ Small Molecule DMARD

61 (33)

35 (19)

0.002

Synthetic DMARD

142 (76)

132 (70)

0.21

Clinical Disease Activity Score (0-76)

13.94 (11.25)

15.05 (13.06)

0.42

Patient Global Score (0-10)

4.52 (2.47)

4.98 (2.92)

0.11

Physician Global Score (0-10)

2.58 (2.08)

2.71 (2.57)

0.63

Swollen Joint Count (0-28)

4.10 (5.10)

4.39 (5.41)

0.62

Tender Joint Count (0-28)

3.02 (4.58)

3.45 (5.34)

0.43

Number of Visits / Person

5.25 (3.26)

3.75 (2.70)

<0.001

Table values represent: N (%) or Mean (SD)


Disclosure: M. Gianfrancesco, None; L. Trupin, None; C. McCulloch, None; S. Shiboski, None; J. Graf, None; G. Schmajuk, None; J. Yazdany, None.

To cite this abstract in AMA style:

Gianfrancesco M, Trupin L, McCulloch C, Shiboski S, Graf J, Schmajuk G, Yazdany J. Differences in Longitudinal Disease Activity Measures between Research Cohort and Non-Cohort Participants with Rheumatoid Arthritis Using Electronic Health Record Data [abstract]. Arthritis Rheumatol. 2018; 70 (suppl 10). https://acrabstracts.org/abstract/differences-in-longitudinal-disease-activity-measures-between-research-cohort-and-non-cohort-participants-with-rheumatoid-arthritis-using-electronic-health-record-data/. Accessed January 16, 2021.
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