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ObjectiveMany autoantibodies are known to be associated with SLE, although their role in clinical practice is limited because of low sensitivity and weak associations with clinical manifestations. There has been great interest in the discovery of new autoantibodies to use in clinical practice. In this study, we investigated 57 new and known antibodies and their potential for diagnostics or risk stratification.MethodsBetween 2014 and 2017, residual sera of all anti-dsDNA tests in the UMC Utrecht were stored in a biobank. This included sera of patients with SLE, patients with a diagnosis of another immune-mediated inflammatory disease (IMID), patients with low (non-IMID) or medium levels of clinical suspicion of SLE but no IMID diagnosis (Rest), and self-reported healthy blood bank donors. Diagnosis and (presence of) symptoms at each blood draw were retrospectively assessed in the patient records with the Utrecht Patient-Oriented Database using a newly developed text mining algorithm. Sera of patients were analysed for the presence of 57 autoantibodies with a custom-made immunofluorescent microarray. Signal intensity cut-offs for all antigens on the microarray were set to the 95th percentile of the non-IMID control group. Differences in prevalence of autoantibodies between patients with SLE and control groups were assessed.ResultsAutoantibody profiles of 483 patients with SLE were compared with autoantibody profiles of 1397 patients from 4 different control groups. Anti-dsDNA was the most distinguishing feature between patients with SLE and other patients, followed by antibodies against Cytosine-phosphate-Guanine (anti-CpG) DNA motifs (p<0.0001). Antibodies against CMV (cytomegalovirus) and ASCA (anti-Saccharomyces cerevisiae antibodies) were more prevalent in patients with SLE with (a history of) lupus nephritis than patients with SLE without nephritis.ConclusionAntibodies against CpG DNA motifs are prevalent in patients with SLE. Anti-CMV antibodies are associated with lupus nephritis.

Objective Electronic health records (EHR) are increasingly being recognized as a major source of data reusable for medical research and quality monitoring, although patient identification and assessment of symptoms (characterization) remain challenging, especially in complex diseases such as systemic lupus erythematosus (SLE). Current coding systems are unable to assess information recorded in the physician’s free‐text notes. This study shows that text mining can be used as a reliable alternative. Methods In a multidisciplinary research team of data scientists and medical experts, a text mining algorithm on 4607 patient records was developed to assess the diagnosis of 14 different immune‐mediated inflammatory diseases and the presence of 18 different symptoms in the EHR. The text mining algorithm included key words in the EHR, while mining the context for exclusion phrases. The accuracy of the text mining algorithm was assessed by manually checking the EHR of 100 random patients suspected of having SLE for diagnoses and symptoms and comparing the outcome with the outcome of the text mining algorithm. Results After evaluation of 100 patient records, the text mining algorithm had a sensitivity of 96.4% and a specificity of 93.3% in assessing the presence of SLE. The algorithm detected potentially life‐threatening symptoms (nephritis, pleuritis) with good sensitivity (80%‐82%) and high specificity (97%‐97%). Conclusion We present a text mining algorithm that can accurately identify and characterize patients with SLE using routinely collected data from the EHR. Our study shows that using text mining, data from the EHR can be reused in research and quality control.

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