Development of a claims-based flare algorithm for systemic lupus erythematosus

Abstract

Objective

To develop a claims-based algorithm identifying systemic lupus erythematosus (SLE) flares using a linked claims-electronic medical record (EMR) dataset.

Methods

This study was a retrospective analysis of linked administrative claims and EMR data spanning 1 January 2003 to 31 March 2019. Included were adult SLE patients with at least 12 months of continuous enrollment in claims data, 12 months of clinical activity in EMR, and an absence of malignancies excluding basal and squamous cell carcinoma. Patient follow-up was divided into 30-day windows, and a proxy SLEDAI-2K score based on the EMR data was calculated for each 30-day period. A flare was defined as an increase of at least 4 from the baseline score. A series of potential flare predictor variables identified in claims were based on a combination of established variables from a previous algorithm, with the addition of other SLE-related indicators based on clinical input. Logistic regression models were built to predict monthly SLE flares.

Results

Inclusion criteria identified 2427 patients. Results from a logistic model with forward selection capping the number of variables at 10 performed well with a c-statistic of 0.76 and a Brier score of 0.07. The top five predictors were any inpatient admission (OR = 4.76), outpatient office visit (OR = 3.04), MRI (OR = 2.26), ER visit (OR = 2.25), and number of rheumatology visits (OR = 1.75); p < .01 for all.

Conclusions

The final algorithm shows promise in providing an alternative and more streamlined way for identifying likely flares in administrative claims data that will advance the study of SLE within the context of flares.

Keywords:

• Systemic lupus erythematosus
• electronic medical records
• disease flare
• administrative claims
• algorithm

Transparency

Declaration of funding

This study was funded by Eli Lilly and Company.

Declaration of financial/other relationships

IG, CC, JW, and DN are employed by Eli Lilly and Company. HV is employed by IBM Watson Health which received funding from Eli Lilly and Company to conduct this study. JT, NZ, and VN were employed by IBM Watson Health during the production of the manuscript. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgements

Medical writing services were provided by Jessamine Winer-Jones of IBM Watson Health. These services were paid for by Eli Lilly and Company.

Data availability statement

The data that support the findings of this study are available from IBM Watson Health. Restrictions apply to the availability of these data, which were used under license for this study.

Ethical approval

All database records are statistically de-identified and certified to be fully compliant with US patient confidentiality requirements set forth in the Health Insurance Portability and Accountability Act of 1996. Because this study used only de-identified patient records and did not involve the collection, use, or transmittal of individually identifiable data, this study was exempted from Institutional Review Board approval.