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  • PO.4.78 Validation analysis of the physician global assessment (PGA) scale in patients with systemic lupus erythematosus included in relesser-pros registry

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Thursday 06 October 2022 from 13:00 to 14:10
PO.4 E- POSTER SESSION 4: disease activity measurements, flare and remission, epidemiology, etiology, genetics, epigenetics
PO.4.78 Validation analysis of the physician global assessment (PGA) scale in patients with systemic lupus erythematosus included in relesser-pros registry
  1. L Cáceres1,
  2. I Rúa-Figueroa Fernández De Larrrinoa1,
  3. N Jiménez2,
  4. M Galindo Izquierdo3,
  5. J Calvo Alen4,
  6. R Menor Almagro5,
  7. A Fernández Nebro6,
  8. J Martínez Barrios3 and
  9. JM Pego Reigosa7
  1. 1Division of Rheumatology ~ Las Palmas de Gran Canaria ~ Spain
  2. 2Investigation in Rheumatology and Immune-Diseases ~ Vigo ~ Spain
  3. 3Division of Rheumatology ~ Madrid ~ Spain
  4. 4Division of Rheumatology ~ Vitoria ~ Spain
  5. 5Division of Rheumatology ~ Jerez ~ Spain
  6. 6Division of Rheumatology ~ Málaga ~ Spain
  7. 7Division of Rheumatology ~ Vigo ~ Spain

Abstract

Objective There is currently no agreement on which scale should be used to evaluate SLE disease activity. The aim of this study was to analyze the construct and criterion validity of the physician global assessment (PGA), from 0 to 10, in the Spanish population, analyzing the correlation with SLEDAI and its ability to predict damage in order to promote its more widespread use.

Methods An observational, longitudinal and prospective design was performed. 1821 patients from the RELESSER-PROS registry and data from 5 annual consecutive visits were tested. Activity was analyzed using: PGA from 0 to 10 transformed to AM-PGA (mean-adjusted PGA), AM-SLEDAI (mean-adjusted SLEDAI); damage: SLICC/ACR Damage Index (SDI)) and health-related quality of life (QoL): EuroQoL 5D y Lupus Impact Tracker (LIT). The correlation between indices and their ability to predict damage progression (defined as any increase of 1 unit in SDI from baseline) and QoL was calculated. Pearson’s or Spearman’s correlation coefficients were calculated for each variable in comparison.

Results The correlation between PGA and SLEDAI was higher for lower PGA values and there was a correlation between AM-PGA and AM-SLEDAI, ranging 0.4 and 0.5. AM-SLEDAI explains a percentage of PGA variation that rises to 27.11% when introducing the number of domains affected by SLEDAI, in non-parametric model. AMS and AMP values are discrepant, especially for patients with low AMS and high AMP values and differs significantly in 3 domains: serological, neuropsychiatric, and renal. Excluding patients from the model, who were marked as discrepant, a significant linear relationship between AMS and AMP, around 0.5 shown up. Regarding damage, the correlation between AM-PGA, AM-SLEDAI and SLICC/ACR explained 13% of SDI variance. SLEDAI accounts for a higher percentage of SDI variance than PGA (10.18% vs. 5.65% in smoothed model), but both do it independently. Analysis the discrepant and non-discrepant patients showed a fairly discrete linear relationship, less than 3%, between the AMS and AMP with the LIT and the EQ5D6 for the non-discrepant patients, but this relationship was not found in discrepant patients.

Conclusion The correlation between PGA and SLEDAI is low and both should be used together. PGA could improve the assessment of disease activity and its use adds the possibility to improve damage prediction.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/lupus-2022-elm2022.105

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