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FRI0641 Detection of changes in sle disease activity is highly improved with sle-das as compared to sledai: derivation and preliminary validation of the sle disease activity score (SLE-DAS)
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  1. D. Jesus1,
  2. A. Matos2,
  3. C. Henriques2,
  4. M. Zen3,
  5. A. Doria3,
  6. D. S. JAP1,4,
  7. L. Inês1,4,5
  1. 1Rheumatology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra
  2. 2Polytechnic Institute of Viseu, CI&DETS, Viseu, Portugal
  3. 3Division of Rheumatology, University of Padova, Padova, Italy
  4. 4Faculty of Medicine, University of Coimbra, Coimbra
  5. 5Faculty of Health Sciences, University of Beira Interior, Covilhã, Portugal

Abstract

Background: SLEDAI is a widely used instrument to measure disease activity of systemic lupus erythematosus (SLE). However, it lacks sensitivity to discriminate improvement/worsening as it only scores items categorically and does not include several relevant lupus features, such as hemolytic anemia.

Objectives: To derive and validate a SLE Disease Activity Score (SLE-DAS) with improved sensitivity to change, while maintaining the high specificity and simplicity of use of the SLEDAI.

Methods: 324 patients fulfilling ACR’97 and/or SLICC’12 classification criteria for SLE and regularly followed at a tertiary care lupus clinic from January 2014 to December 2017 were included. At each outpatient visit, clinical and laboratory data were collected and disease activity (last 30 days) was scored with Physician Global Assessment (PGA) (0–3 scale) and SLEDAI-2K. To derive the SLE-DAS we analyzed data from the study visit with higher disease activity from each patient, applying multivariate linear regression analysis, with PGA as dependent variable/gold-standard. Independent variables tested in the models included items from SLEDAI-2K and continuous variables for swollen joint count, proteinuria, platelet and white blood cells counts. Some features absent from SLEDAI, such as hemolytic anemia, gastrointestinal and cardiopulmonary involvement were added to the model.

To assess correlation validity we performed a Spearman’s correlation between the SLE-DAS, PGA and SLEDAI-2K at last follow-up visit. We tested performance of SLEDAI-2K (change ≥4) and SLE-DAS to discriminate a clinically meaningful worsening and improvement in SLE disease activity (change in PGA ≥0.3) using Receiver Operating Characteristic (ROC) curve analysis. We determined the best cut-offs values of SLE-DAS to detect changes in PGA ≥0.3 and calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV). Statistical significance was set at 0.05.

Results: The final SLE-DAS model included 17 items. The SLE-DAS score at last follow-up visit presented high correlation with PGA (rho=0.975, p<0.0005) and SLEDAI-2K (rho=0.94, p<0.0005). For improvement in PGA≥0.3, in ROC analysis a change in SLE-DAS presented a much higher performance [area under curve (AUC)=0.927 (95% CI=0.885–0.969, p<0.0005)] than SLEDAI-2K [AUC=0.787 (95% CI=0.718–0.857), p<0.0005] (figure 1). For worsening of PGA≥0.3, change in SLE-DAS and SLEDAI-2K presented an AUC of 0.994 (95% CI=0.988–1.000, p<0.0005) and 0.914 (95% CI=0.870–0.959, p<0.0005), respectively (figure 1). The optimal discriminative cut-off for either a PGA increase or reduction was change in SLE-DAS ≥1.72 (table 1).

Table 1

Performance of SLE-DAS and SLEDAI-2K to detect change in SLE disease activity

Figure 1 Receiver operating curve (ROC) comparing the performance of SLE-DAS and SLEDAI-2K to detect a clinical meaningful improvement (A) and worsening (B) in SLE disease activity.

Conclusions: The SLE-DAS presents good construct validity and much higher discriminative power to detect changes in SLE disease activity as compared to SLEDAI-2K. External validation in another SLE cohort is underway.

Disclosure of Interest: None declared

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