1. Comprehensive disease control in systemic lupus erythematosus
- Author
-
Carmelo Pirone, Alessio Sortino, C. Garufi, V. A. Pacucci, S. Mancuso, Enrica Cipriano, Marco Sciandrone, Lorenzo Dominici, Cristiano Alessandri, Francesca Romana Spinelli, Filmon Arefayne, Valeria Orefice, F. Natalucci, Simona Truglia, Matteo Lapucci, Giulio Olivieri, Fabrizio Conti, Alessandra Ida Celia, Carlo Perricone, and Fulvia Ceccarelli
- Subjects
medicine.medical_specialty ,Disease duration ,chronic damage ,comprehensive disease control ,disease activity ,machine learning models ,remission ,systemic lupus erythematosus ,Antiphospholipid ,Severity of Illness Index ,Antibodies ,Disease activity ,Cohort Studies ,03 medical and health sciences ,0302 clinical medicine ,Rheumatology ,Internal medicine ,medicine ,Humans ,Lupus Erythematosus, Systemic ,030212 general & internal medicine ,030203 arthritis & rheumatology ,Systemic lupus erythematosus ,Lupus Erythematosus ,business.industry ,Systemic ,Remission Induction ,Complete remission ,Middle Aged ,medicine.disease ,Disease control ,Anesthesiology and Pain Medicine ,Cohort ,Lower prevalence ,Antibodies, Antiphospholipid ,Disease Progression ,business ,Glucocorticoid ,medicine.drug - Abstract
Objectives We evaluated a monocentric SLE cohort in order to assess the frequency of Lupus comprehensive disease control (LupusCDC), a condition defined by the achievement of remission and the absence of damage progression. Methods Our longitudinal analysis included SLE patients with 5-years follow-up and at least one visit per year. Disease activity was assessed by SLE Disease Activity Index 2000 (SLEDAI-2K) and three different remission levels were evaluated (Complete Remission, CR; Clinical remission off-corticosteroids; clinical remission on-corticosteroids). Chronic damage was assessed according to SLICC Damage Index (SDI). LupusCDC was defined as remission achievement for at least one year plus absence of chronic damage progression in the previous one year. A machine learning based analysis was carried out, applying and comparing Nonlinear Support Vector Machines (SVM) models and Decision Trees (DT), whereas features ranking was performed with the ReliefF algorithm. Results We evaluated 172 patients [M/F 16/156, median age 49 years (IQR 16.7), median disease duration 180 months (IQR 156)]. SDI values (baseline mean±SD 0.7 ± 1.1) significantly increased during the follow-up period. In all time-points analyzed, LupusCDC including CR was the most frequently detected. The failure to reach this condition was significantly associated with renal involvement and with the intake of immunosuppressant drugs and glucocorticoid (GC). Ten patients (5.8%) have maintained LupusCDC during the whole 5-year follow-up: these patients had never presented renal involvement and showed lower prevalence of anti-phospholipid antibodies (p = 0.0001). Finally, the prevalence of GC intake was significantly lower (p = 0.0001). The application of machine learning models showed that the available features were able to provide significant information to build predictive models with an AUC score of 0.703 ± 0.02 for DT and 0.713 ± 0.02 for SVM. Conclusions Our data on a monocentric cohort suggest that the LupusCDC can efficaciously merge into one outcome SLE-related disease activity and chronic damage in order to perform an all-around evaluation of SLE patients.
- Published
- 2021