Developing Electronic Health Record Algorithms That Accurately Identify Patients With Systemic Lupus Erythematosus

Abstract

Objective To study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9) codes, laboratory testing, and medications to identify SLE patients. Methods We used Vanderbilt's Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive anti-nuclear antibody (ANA), ever use of medications, and a keyword of “lupus” in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5759 subjects. Results The algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥ 1:40), and ever use of both disease-modifying antirheumatic drugs (DMARDs) and steroids while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes. Conclusion We developed and validated the first EHR algorithm that incorporates lab values and medications with the SLE ICD-9 code to identify patients with SLE accurately.

Phenome-wide association study identifies marked increased in burden of comorbidities in African Americans with systemic lupus erythematosus

“…Using our validated algorithm [20], we identified 1097 subjects with SLE including 3 with missing race/ethnicity data, 2 as “other” and 52 as “unknown” race/ethnicity, 25 Asians, 30 Hispanics, 270 African Americans, and 715 Caucasians. We then restricted analyses to the African American and Caucasian subjects with SLE.…”

“…Further, administrative databases can have a fairly short duration of follow up [26, 27]. In contrast, our EHR has follow up over several decades with subjects with SLE having on average 9 years of follow up [20]. PheWAS has the power to capture diverse comorbidities in the EHR and uncover how these comorbidities contribute to racial disparities in SLE.…”

“…To identify SLE patients within the Synthetic Derivative, we used our validated EHR algorithm [20] of ≥ 4 counts of the SLE ICD-9 code (710.0) and a positive anti-nuclear antibody (ANA) with a titer ≥1:160 while excluding ICD-9 codes for systemic sclerosis (710.1) and dermatomyositis (710.3). This previously described algorithm [20] has a positive predictive value (PPV) of 89% and sensitivity of 86%.…”

“…This previously described algorithm [20] has a positive predictive value (PPV) of 89% and sensitivity of 86%.…”

Real world big data for clinical research and drug development

“…A nationwide cohort study of the incidence and mortality of acute and chronic pancreatitis in The Netherlands found that disease burden and healthcare costs will probably increase, linked to the ageing Dutch population [31]. An algorithm developed at Vanderbilt University that enabled the rapid searching of an EHR database of 2.5 million subjects to accurately identify systemic lupus erythematosus [32]. The ability to use algorithms and large datasets to rapidly identify previously undiagnosed and unknown patient populations would not only have a direct impact on lupus research but also has the potential to be applicable to autoimmune disorders more broadly.…”