Alveolar haemorrhage in ANCA-associated vasculitis: Long-term outcome and mortality predictors.

INTRODUCTION: Alveolar haemorrhage (AH) is considered an important cause of morbidity and early mortality in anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitides (AAV). OBJECTIVES: The aim of this study was to identify predictors of outcome in patients with AH-AAV and to evaluate outcome and causes of death in this subset. MATERIALS AND METHODS: A multicenter retrospective study was conducted in 29 Italian Centers. Clinicians were asked to recruit all patients diagnosed with AAV-associated AH during the last 10 years, from 2007 to 2016. Univariate and multivariable analysis were performed. RESULTS: One-hundred and six patients were included (median age at onset of 55 years [IQR 42-67]). The majority were ANCA-positive (PR3 57.1%, MPO 33.7%) and 72.6% had also renal involvement. At presentation, anaemia was shown in 97 (92.4%) patients, hemoptysis in 54 (51.9%), respiratory failure in 68 (66.7%), of whom 48 (70.6%), requiring respiratory support. At the end of the 37 months [IQR 13-77] follow-up, 19/106 (17.9%) patients were dead. The main causes of death were active disease and infections. By stepwise regression analysis, age >65 years (HR 3.66 [95% CI 1.4-9.51], p = 0.008) and the need for respiratory support (HR 4.58 [95% CI 1.51-13.87], p = 0.007) at AH onset were confirmed to be predictive of mortality. CONCLUSIONS: Predictors of outcome in AAV-AH were determined. Factors related to the patient's performance status and the severity of the lung involvement strongly influenced the outcome. Balancing harms and benefits for the individual patient in induction and maintenance treatment strategies is crucial.

Joint involvement in systemic lupus erythematosus: From pathogenesis to clinical assessment.

OBJECTIVE: In the present review, the different phenotypes, clinimetric and imaging tools able to assess joint involvement in patients affected by Systemic Lupus Erythematosus (SLE) have been described and summarized. Furthermore, the current knowledge about the pathogenic mechanism and the potential biomarkers of this feature is reported. METHODS: A literature search was done in PubMed, accessed via the National Library of Medicine PubMed interface (http://www.ncbi.nlm.nih.gov/pubmed). Firstly, PubMed was searched using the term "systemic lupus erythematosus" OR "lupus" in combination with (AND) "joint" OR "articular".Secondly, the same PubMed research was combined with other terms, such as "pathogenesis" OR "genetic" OR "antibodies" OR "biomarkers" OR "cytokines" OR "imaging" OR "ultrasonography" OR "magnetic resonance" OR "clinimetry". RESULTS: After a stringent selection, we evaluated in the present review 13 papers concerning clinical phenotypes of SLE joint involvement, 14 concerning clinimetric assessment, 20 concerning imaging, and finally, 28 concerning pathogenesis and biomarkers. Further relevant data were obtained from the reference lists of articles returned using these search terms and from authors own experience and knowledge of the literature. CONCLUSION: Despite the prevalence and severity of SLE joint involvement, more awareness and a deeper evaluation of the clinical heterogeneity of this manifestation are mandatory. Moreover, longitudinal studies are needed to assess the progression of this manifestation and to provide standard definitions and examination/recording protocols.

Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models.

OBJECTIVE: The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks. METHODS: We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs. RESULTS: At the first visit in the Lupus Clinic, 35.8% of patients had an SDI≥1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage. CONCLUSION: We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage.

Evaluation of the patient acceptable symptom state (PASS) in Italian patients affected by systemic lupus erythematosus: association with disease activity indices.

OBJECTIVES: The aim of this study was to evaluate the discriminant capability of the patient acceptable symptom state (PASS) according to disease activity, in a cohort of Italian patients affected by systemic Lupus erythematosus (SLE). METHODS: Consecutive SLE patients were enrolled. At each visit, the patients underwent a complete physical examination and the clinical/laboratory data were collected in a standardized, computerized, and electronically-filled form. The evaluation of serum complement C3 and C4 levels and determination of autoantibodies was obtained. Disease activity was assessed with the SLEDAI-2K and ECLAM, while chronic damage was measured with the SLICC. Finally, PASS was assessed in all patients by asking to answer yes or no to a single question. RESULTS: One hundred sixty-five patients were enrolled (M/F 12/153; mean age 40.4±11.8 years, mean disease duration 109.1±96.2 months). No patients refused to answer, suggesting the acceptability of PASS. A total of 80% of patients rated their state as acceptable. The patients with an acceptable status had significantly lower mean SLEDAI-2K and ECLAM scores than the others [1.8±2.7 versus 3.4±2.3(P=0.004); 0.7±0.9 versus 1.4±1.1(P=0.0027)]. No significant differences were observed when considering chronic damage, evaluated with SLICC. CONCLUSIONS: In the clinical practice, SLE patients assessment performed by using complex disease activity indices such as SLEDAI-2K and ECLAM, could be time consuming. In our study, for the first time, we used PASS, a quick and easily comprehensible tool, to evaluate the patients' status, this single question seems to be able to discriminate patients with different disease activity, especially when this is determined by musculoskeletal involvement.