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Abstract Number: 180

Identifying Comorbidities and Seropositivity in Rheumatoid Arthritis Patients Using Single-specialty Electronic Health Record Data

Huifeng Yun1, Fenglong Xie 2, Lang Chen 1, Shuo Yang 1 and Jeffrey Curtis 1, 1University of Alabama at Birmingham, Birmingham, AL, 2University of Alabama at Birmingham, Birmingham

Meeting: 2019 ACR/ARP Annual Meeting

Keywords: Comorbidity, EHR and diabetes, Rheumatoid arthritis (RA)

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Session Information

Date: Sunday, November 10, 2019

Session Title: Epidemiology & Public Health Poster I: RA

Session Type: Poster Session (Sunday)

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

Background/Purpose: Comorbidities are associated with worse clinical outcomes among patients with rheumatoid arthritis (RA), as does seropositive disease. Rheumatoid factor (RF) and anti-cyclic citrullinated peptide (CCP) antibody status are often tested at the time of RA diagnosis but may not be repeated and therefore may not appear in a rheumatologists’ electronic health record (EHR). Recent ICD10 coding allows discrimination between seropositive (M05) and seronegative (M06) patients, but the validity has not been examined. Moreover, the accuracy of identifying comorbidities that may not be managed, nor coded, in rheumatologists’ EHRs has received little attention.  We used a national rheumatology-based EHR registry to examine ICD10 coding to assess the accuracy of seropositive RA classification, and used a confluence of data (diagnoses, drugs, and lab results) to identify diabetes as an example of a common comorbidity that might be relevant for RA.

Methods: Using the ACR’s Rheumatology Informatics System for Effectiveness (RISE) EHR-based registry, we created a RA cohort requiring patients to have ≥ 2 rheumatologist visits. Seropositive RA patients were defined as ever have any ICD10 diagnosis code of M05 (i.e. ‘with RF’), as well as several permutations of the M05 code (requiring >=2 diagnoses, using the last code, etc). Using RF or anti-CCP lab tests as the gold standard, we calculated sensitivity (Se) and positive predicted value (PPV) based on number of diagnosis codes for RA and different combinations of RF and CCP. We identified diabetes based on ≥ 1ICD9/10 diagnosis code (ICD9:  250.*0, 250.*2; ICD10: E11*); prior prescriptions for diabetes, or elevated laboratory test (hemoglobin A1C ≥ 6.5% or random glucose >200 or fasting glucose ≥ 126mg/dl).  We also evaluated the number of physician visits needed to stabilize the prevalence estimate for diabetes.

Results: Among 22,340 eligible RA patients, 5,941 (27%) patients had a lab test for RF (ever), 7,090 (32%) had a test for CCP, and 7,953 (36%) had tests for RF or CCP. Using RF result as the gold standard, the Se for seropositivity using any M05 diagnosis code was 0.86, PPV 0.82; similarly, using CCP as the gold standard, the Se was 0.87 and PPV was 0.68. Combining (RF or CCP), the Se was 0.83 and PPV was 0.83. Requiring additional diagnosis codes, or examining the last code, minimally improved Se and PPV.
Among RA patients with diabetes, 90% were identified as having diabetes medications, 32% by diagnosis codes and 28% by laboratory tests. Diabetes medications by themselves accounted for 51% of all diabetes classification. Using all three types of data, the prevalence of diabetes (17% overall) stabilized after 2 physician visits (Figure 1).

Conclusion: Under ICD10, M05 is a reasonable proxy to identify seropositive RA patients with high sensitivity and positive predictive values. Comorbidities not usually managed by rheumatologists can be identified in a single-specialty EHR like RISE, and identification will be greatly facilitated if specific drugs for those conditions are used.

Disclaimer: This data was supported by the ACR’s RISE Registry. However, the views expressed represent those of the authors, not necessarily those of the ACR.

Figure 1: Number of rheumatology visits needed to stabilize diabetes prevalence in the RISE registry


Algorith1

Table1: Sensitivity and positive predicted values for identifying seropositive patients using different algorithms


Disclosure: H. Yun, BMS, 2, Bristol-Myers Squibb, 2, Pfizer, 2; F. Xie, None; L. Chen, None; S. Yang, None; J. Curtis, AbbVie, 2, 5, Abbvie, 2, 5, AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, 2, 5, Amgen, 2, 5, Amgen Inc., 2, 5, BMS, 2, 5, Bristol-Myers Squibb, 2, 5, Corrona, 2, 5, Crescendo, 2, 5, Eli Lilly, 2, 5, Eli Lilly and Company, 2, 5, Genentech, 2, 5, Janseen, 5, Janssen, 2, 5, Janssen Research & Development, LLC, 2, Lilly, 2, 5, Myriad, 2, 5, Patient Centered Outcomes Research Insitute (PCORI), 2, Pfizer, 2, 5, Radius Health, Inc., 9, Regeneron, 2, 5, Roche, 2, 3, 5, Roche/Genentech, 5, UCB, 2, 5.

To cite this abstract in AMA style:

Yun H, Xie F, Chen L, Yang S, Curtis J. Identifying Comorbidities and Seropositivity in Rheumatoid Arthritis Patients Using Single-specialty Electronic Health Record Data [abstract]. Arthritis Rheumatol. 2019; 71 (suppl 10). https://acrabstracts.org/abstract/identifying-comorbidities-and-seropositivity-in-rheumatoid-arthritis-patients-using-single-specialty-electronic-health-record-data/. Accessed March 27, 2023.
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