Which extra-renal flare is 'difficult to treat' in systemic lupus erythematosus? A one-year longitudinal study comparing traditional and machine learning approaches.

OBJECTIVES: To describe phenotypes and outcomes of extra-renal flares in SLE, to identify clusters of extra-renal flares based on baseline features, and to develop a machine learning (ML) tool capable of predicting 'difficult to treat' (D2T) flares. METHODS: Extra-renal flares that occurred in our cohort over the last five years with at least one year of follow-up were included. Baseline clinical variables were described and flares assigned to clusters. Attainment of remission and low disease activity state (LLDAS) at 12 months were compared. Flares were then considered 'D2T' in case of non-attainment of LLDAS at 6 and 12 months. Baseline features were used to train a ML model able to predict future D2T-flares, at admission. Traditional approaches were then compared with informatic techniques. RESULTS: Among 420 SLE patients of the cohort, 114 flares occurred between 2015 and 2021; 79 extra-renal flares, predominantly mucocutaneous (24.1%) and musculoskeletal (45.6%), were considered. After 12 months, 79.4% and 49.4% were in LLDAS and in remission, respectively, while 17 flares were classified as D2T (21.5%); D2T flares received a higher cumulative and daily dose of glucocorticoids. Among the clusters, cluster 'D' (mild-moderate flares with mucocutaneous manifestations in patients with history of skin involvement) was associated with the lowest rate of remission. Among clinical data, not being on LLDAS at 3 months was the unique independent predictor of D2T flares. CONCLUSIONS: Our clusterization well separates extra-renal flares according to their baseline features and may propose a new identification standard. D2T flares, especially refractory skin manifestations, are frequent in SLE and represent an unmet need in the management of the disease as they are associated with higher glucocorticoid (GC) dosage and risk of damage accrual. Our ML model could help in the early identification of D2T flares, flagging them to elevate the attention threshold at admission.

LRP5 high bone mass (Worth-type autosomal dominant endosteal hyperostosis): case report and historical review of the literature.

PURPOSE: LRP5 high bone mass (HBM) is an autosomal dominant endosteal hyperostosis caused by mutations of the low-density lipoprotein receptor-related protein 5 (LRP5) gene. Alternative names included "autosomal dominant osteosclerosis" and "Worth disease." The aim of the paper is to provide an historical overview of a disorder whose literature is complicated and confusing due to the past use of several denominations and lack of reviews. METHODS: We collected case reports of HBM with evidence of autosomal dominant transmission preceding the identification of the LRP5 mutations in 2002 (Worth-type endosteal hyperostosis) and cases of LRP5 HBM confirmed by genetic analysis since 2002. The prevalence of relevant clinical and laboratory findings was estimated. We described an affected woman with neurological manifestations. RESULTS: A 44-year-old Caucasian woman with torus palatinus complained of headache, hypo-/anosmia, and complete mixed deafness. Dual-energy X-ray absorptiometry (DEXA) scan revealed elevated bone mass. The A242T mutation of the LRP5 gene was detected. Including the present case, 155 patients have been reported to date. Neurological involvement and increased serum alkaline phosphatase (ALP) were present in 19.4% and 3.7% of cases, respectively. Facial changes and torus palatinus were observed in 61% and 41% of cases, respectively. CONCLUSIONS: We present the only historical review on Worth-type endosteal hyperostosis, now known as LRP5 HBM. Neurological manifestations, previously considered absent in the disease, affect 19.4% of the patients. Genetic analysis and appropriate denomination of LRP5 HBM are fundamental for diagnosis and to mitigate the confusion that has long characterized this disease.

Osteoporosis and gut microbiota, radiofrequency echographic multispectrometry and machine learning: one year in review 2023.

Osteoporosis is a prevalent bone disease with a relevant burden of mortality and comorbidity, especially due to fragility fractures occurring as a result of reduced bone mineral density. In this review we provide a critical digest of the most recent literature regarding the relationship between gut microbiota and osteoporosis, and discuss the role of radiofrequency echographic multi-spectrometry (REMS) and machine learning in the diagnostic work-up and prevention of osteoporosis.