Early Lupus Project: one-year follow-up of an Italian cohort of patients with systemic lupus erythematosus of recent onset.
Gian Domenico SebastianiI PreveteA IulianoMatteo PigaF IannoneL ColadonatoM GovoniA BortoluzziM MoscaC TaniA DoriaL IaccarinoA TincaniM FrediF ContiF R SpinelliM GaleazziF BellisaiA ZanettiG CarraraC A ScirèA MathieuPublished in: Lupus (2018)
Objective To describe the clinical and serological features of a prospectively followed cohort of early diagnosed systemic lupus erythematosus (SLE) patients during a one-year follow-up period. Methods SLE patients with disease duration less than 12 months were consecutively enrolled in a multicentre, prospective study. At study entry and then every 6 months, a large panel of data was recorded. Results Of 260 patients enrolled, 185 had at least 12 months of follow-up; of these, 84.3% were female, 92.4% were Caucasians. Mean diagnostic delay was about 20 months; higher values of European Consensus Lupus Activity Measurement (ECLAM) and of organs/systems involved were both associated with shorter diagnostic delay. Clinical and serological parameters improved after study entry. However, patients' quality of life deteriorated and cardiovascular risk factors significantly increased. About one-third of patients with active disease at study entry went into remission (ECLAM = 0). Negative predictors for remission were: oral ulcers, arthritis, low C4, anti-SSB (Ro) antibodies and therapy with mycophenolate. There was a widespread use of glucocorticoids both at baseline and during follow-up. Conclusion Clinical symptoms and serological parameters improve during the first period after diagnosis. However, patients' quality of life deteriorates. The widespread use of glucocorticoids is probably the reason for the early significant increase of some cardiovascular risk factors.
Keyphrases
- systemic lupus erythematosus
- end stage renal disease
- cardiovascular risk factors
- ejection fraction
- chronic kidney disease
- prognostic factors
- stem cells
- cardiovascular disease
- clinical trial
- machine learning
- metabolic syndrome
- physical activity
- quality improvement
- study protocol
- artificial intelligence
- cell therapy
- patient reported