Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record

Abstract

Objective: Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.

Methods: Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGA^EX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).

Results: SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.

Conclusion: We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

Figure 1.. Flow chart of selection of SLE cases.

We used a large, de-identified electronic health record called the Synthetic Derivative to select our SLE cases. We used an algorithm with a positive predictive value of 90% requiring ≥ 4 or more SLE ICD-9 codes (710.0) and a positive ANA (≥ 1:160). We performed chart review to confirm SLE case status and then selected SLE cases that had both available genetic data (existing data on the MEGA^EX chip) and clinical data (including billing codes).

Figure 2.. Boxplot of Systemic lupus erythematosus (SLE) phenotype risk scores (PheRS) in SLE cases vs. controls.

(A) We identified 789 SLE cases, all who had a SLE diagnosis confirmed by a rheumatologist. We identified 2,261 controls with no known autoimmune disease diagnoses. The horizontal line indicates the median score. (B) Boxplot of SLE genetic risk scores (GRS) in SLE cases vs. controls. Both SLE cases and controls were required to have genetic data available on MEGA^EX chip. The genetic risk score consisted of 58 SLE risk SNPs with a 95% sampling and 95% call rate.

Figure 3.. Controls with the highest genetic risk scores (GRS) and phenotype risk scores (PheRS).

The bar graphs show the proportion of diagnoses of the controls with the 50 highest GRS and PheRS. Categories included systemic lupus erythematosus (SLE), incomplete SLE, other autoimmune disease, and no autoimmune disease or not a case.

Figure 4.. Scatterplot of age of SLE diagnosis and SLE genetic risk score (GRS) and phenotype risk score (PheRS).

A) Age of SLE diagnosis and GRS in all SLE cases, B) Age of SLE diagnosis and GRS in White SLE cases, C) Age of SLE diagnosis and GRS in Black SLE cases, D) Age of SLE diagnosis and PheRS in all SLE cases, E) Age of SLE diagnosis and PheRS in White SLE cases, and F) Age of SLE diagnosis and PheRS in Black SLE cases. Age of SLE diagnosis was obtained from chart review.

Figure 5.. AUC for models for SLE case status using SLE phenotype risk score (PheRS) and genotype risk score (GRS).

A) Models in all SLE and control individuals. B) Models in White SLE and control individuals only. C) Models in Black SLE and control individuals only. The blue line denotes an unadjusted model using only the PheRS. The red line denotes an unadjusted model for SLE case status using only the GRS while the purple line denotes an unadjusted model with both PheRS and GRS.