Association of autoantibodies with the IFN signature and NETosis in patients with systemic lupus erythematosus.

Objective: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a variety of disease symptoms and an unpredictable clinical course. To improve treatment outcome, stratification based on immunological manifestations commonly seen in patients with SLE such as autoantibodies, type I interferon (IFN) signature and neutrophil extracellular trap (NET) release may help. It is assumed that there is an association between these immunological phenomena, since NET release induces IFN production and IFN induces autoantibody formation via B-cell activation. Here we studied the association between autoantibodies, the IFN signature, NET release, and clinical manifestations in patients with SLE. Methods: We performed principal component analysis (PCA) and hierarchical clustering of 57 SLE-related autoantibodies in 25 patients with SLE. We correlated each autoantibody to the IFN signature and NET inducing capacity. Results: We observed two distinct clusters: one cluster contained mostly patients with a high IFN signature. Patients in this cluster often present with cutaneous lupus, and have higher anti-dsDNA concentrations. Another cluster contained a mix of patients with a high and low IFN signature. Patients with high and low NET inducing capacity were equally distributed between the clusters. Variance between the clusters is mainly driven by antibodies against histones, RibP2, RibP0, EphB2, RibP1, PCNA, dsDNA, and nucleosome. In addition, we found a trend towards increased concentrations of autoantibodies against EphB2, RibP1, and RNP70 in patients with an IFN signature. We found a negative correlation of NET inducing capacity with anti-FcER (r = -0.530; p = 0.007) and anti-PmScl100 (r = -0.445; p = 0.03). Conclusion: We identified a subgroup of patients with an IFN signature that express increased concentrations of antibodies against DNA and RNA-binding proteins, which can be useful for further patient stratification and a more targeted therapy. We did not find positive associations between autoantibodies and NET inducing capacity. Our study further strengthens the evidence of a correlation between RNA-binding autoantibodies and the IFN signature.

The predictive value of antinuclear antibodies in patients with suspected connective tissue disease for the development of connective tissue diseases.

OBJECTIVES: Early confirmation of the diagnosis of connective tissue diseases (CTD) is important, as prolonged disease activity can result in irreversible organ damage. Although antinuclear antibodies (ANAs) have been shown to precede the diagnosis of SLE, this has not been investigated in large cohorts for other CTDs. In this study, we investigated whether the presence of antinuclear autoantibodies in undiagnosed patients suspected of having CTDs is predictive of development of a future CTD. METHODS: We screened the Electronic Health Records of a cohort of 1030 patients, who were tested for ANAs and their specificity in 2013/2014, to evaluate whether new CTD diagnoses had been recorded by a clinician between the original blood draw date and 2020. We compared the prevalence of ANAs in patients who developed a new CTD diagnosis during follow-up with patients with similar symptoms at baseline who did not receive a subsequent CTD diagnosis and with patients with an established CTD at baseline. RESULTS: Sixteen out of 1030 patients had developed a new CTD in the studied time period. The mean time period between baseline blood draw and subsequent CTD diagnosis of these patients was approximately 2.3 years. Eleven out of 16 (69%) newly diagnosed patients had positive ANA screening tests, compared to 54% of patients with a CTD diagnosis at baseline (p=ns) and 30% of symptomatic undiagnosed patients (p<0.001). This resulted in a positive predictive value (PPV) of 7% and a negative predictive value (NPV) of 98% for the development of a new CTD in undiagnosed symptomatic patients. For specific ANAs associated with the suspected CTD, the PPV was 12%, with a NPV of 98%. CONCLUSIONS: Progression to a CTD diagnosis is rare in undiagnosed patients. Undiagnosed patients with symptoms associated with a CTD who progress to a CTD more often have ANAs than patients with similar symptoms who do not progress to a CTD. ANA testing can be used to more stringently select patients who should remain in follow-up.

Microarray analysis of autoantibodies can identify future Systemic Lupus Erythematosus patients.

OBJECTIVE: Reliable early ascertainment in patients with SLE is important to prevent the accumulation of irreversible organ damage. Autoantibodies are often present in the serum of patients before the first symptoms arise, therefore they are of potential use as early diagnostic tools. METHODS: We used a custom-made antibody microarray containing 57 autoantigens to analyze serum samples of 1519 patients previously tested for anti-dsDNA and 361 samples of self-reported healthy blood bank donors (BBD). The 1519 patients included 483 patients with SLE, 346 patients with other immune mediated inflammatory diseases (IMID), 218 patient controls without relevant clinical symptoms (Non-IMID), and 472 patients that did not fit in any of the previous groups (Rest). The Non-IMID and BBD groups were used individually to create multivariable prediction models to distinguish samples of patients with SLE from these control groups. We subsequently used these models to predict the outcome for samples of patients who developed SLE while in follow-up (pre-SLE). RESULTS: Out of 1036 patients with no diagnosis of SLE at the moment of sample collection, 17 patients developed SLE while in follow-up (mean time to diagnosis 7.2 months). The best performing model (AUC 0.83) identified 9 out of 17 (53%) pre-SLE samples as SLE, with a specificity of 94%. CONCLUSION: Serum samples of patients who will develop SLE in the future already show a shift of the autoantibody profile prior to diagnosis. In this study, we show that these autoantibody profiles can be used to identify these future SLE patients.

Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus.

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.

Erratum to: Diagnosis and treatment of patients with antiphospholipid syndrome: a mixed-method evaluation of care in The Netherlands.

[This corrects the article DOI: 10.1093/rap/rkaa021.].

Diagnosis and treatment of patients with antiphospholipid syndrome: a mixed-method evaluation of care in The Netherlands.

OBJECTIVES: The aims were to gain insight into the care provided to patients with APS in The Netherlands and to identify areas for improvement from the perspective of both patients and medical specialists. METHODS: APS care was evaluated using qualitative and quantitative methods. Perspectives on APS care were explored using semi-structured interviews with medical specialists, patient focus groups and a cross-sectional, online patient survey. In order to assess current practice, medical records were reviewed retrospectively to collect data on clinical and laboratory manifestations and pharmacological treatment in six Dutch hospitals. RESULTS: Fourteen medical specialists were interviewed, 14 patients participated in the focus groups and 79 patients completed the survey. Medical records of 237 patients were reviewed. Medical record review showed that only one-third of patients were diagnosed with APS within 3 months after entering specialist care. The diagnostic approach and management varied between centres and specialists. Almost 10% of all patients and 7% of triple-positive patients with thrombotic APS were not receiving any anticoagulant treatment at the time of medical record review. Correspondingly, poor recognition and fragmentation of care were reported as the main problems by medical specialists. Additionally, patients reported the lack of accessible, reliable patient education, psychosocial support and trust in physicians as important points for improvement. CONCLUSION: Delayed diagnosis, variability in management strategies and fragmentation of care were important limitations of APS care identified in this study. A remarkable 10% of patients did not receive any anticoagulant treatment.