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Background: Traumatic brain injury (TBI) in the elderly is a noteworthy pathology due to the exponential increase in population age, and the effects of antiplatelet and anticoagulation on patients’ outcomes are still a matter of dispute. The aim of the present study was to evaluate the impact of various antithrombotic agents on patients with mild TBI, focusing on the risk of intracranial bleeding (ICH) and length of hospitalization (LOS). Methods: A retrospective analysis was conducted, including patients with a diagnosis of TBI admitted to the Emergency Department between 2021 and 2022. Patients were classified according to the concurrent antithrombotic therapy as aspirin (ASA), antiplatelets, direct oral anticoagulants (DOACs), and low-molecular-weight heparin (LMWH). The primary outcome was the ICH occurrence, while the secondary outcome was the LOS. The statistical analysis was performed via logistic regression models in R and STATA 13.1 software. Fisher’s exact test was used for the statistical significance. Results: 267 patients with mild TBI were included; 148 were not on antithrombotic agents, 43 were on aspirin, 33 on DOACs, 5 on LMWH, 22 on antiplatelets, and 16 on VKA. Out of the total, 9 patients experienced ICH, none of which were on DOACs, LMWH, or VKA, but 4—out of 65—were on antiplatelets, and 5—out of 148—were not on antithrombotic therapies. Patients not on antithrombotic therapy had the shortest LOS at 0.46 days, while those on VKA had the longest LOS at 1.19 days; similar trends were observed for patients on DOAC and LMWH. Conclusions: The results reveal that TBI patients on anticoagulants/antiplatelets had longer hospital stays compared with those on aspirin alone. Notably, VKA was the strongest predictor for an extended LOS. Regarding ICH, patients taking only aspirin were twice as likely to experience bleeding compared with those on anticoagulants/antiplatelets. However, to achieve statistically significant evidence, further research with a larger cohort of patients is needed.

Objective To describe demographic, clinical and laboratory features of systemic sclerosis sine scleroderma (ssSSc) in a large multicentre systemic sclerosis (SSc) cohort.Methods Data involving 1808 SSc patients from Italian Systemic sclerosis PRogression INvestiGation registry were collected. The ssSSc was defined by the absence of any cutaneous sclerosis and/or puffy fingers. Clinical and serological features of ssSSc were compared with limited cutaneous (lcSSc) and diffuse cutaneous (dcSSc) subsets.Results Among patients with SSc, only 61 (3.4%) were classified as having ssSSc (F/M=19/1). Time from Raynaud’s phenomenon (RP) onset to diagnosis was longer in ssSSc (3 years, IQR 1–16.5) than lcSSc (2 years, IQR 0–7), and dcSSc (1 year, IQR 0–3) (p

Introduction Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. Material and methods We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. Results 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. Conclusions Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

Background and aims: Treatment options for PsA, following non-steroidal anti-inflammatory drugs (NSAIDs), include conventional synthetic disease modifying anti-rheumatic drugs (csDMARDS), particularly methotrexate (MTX). The present study was performed to determine the non-adherence and discontinuation rates of different methotrexate (MTX) formulations in psoriatic arthritis (PsA). Approach and results: We performed a retrospective-cohort study on patients with PsA identified by disease-specific code in the administrative-health-databases of a Northern Italian region (Lombardy) between 2004 and 2015. Subjects were defined as non-adherent if less than 80% of the prescribed MTX dose was taken based on the time between each prescription. Discontinuation rates were calculated using the time between the first and the last MTX prescription over an observation period of 120 months. Among 8952 patients with PsA, 33% were treated with MTX (mean dosage 10 mg/week ± 2.5 mg standard deviation), more frequently (59%) in its parenteral formulation at a 10 mg weekly dosage (35%). Oral glucocorticoids were prescribed to 21% of patients, while non-steroidal anti-inflammatory drugs to 45%. Approximately 37% of patients with PsA were defined as non-adherent to MTX, with the oral formulation associated with an increased risk of non-adherence (hazard ratio 2.08, 95% confidence interval 1.84–2.35, p

The epidemiology of autoimmune diseases is characterized by a significant sex dimorphism, with the majority of disorders being more prevalent in women. In a parallel fashion, the immune system shows sex-dependent differences in number and functions of both its innate and its adaptive arms, with women capable to mount a more vigorous response compared to men. This enhanced reactivity may contribute to the stronger defense against infectious agents and to the reasons for which, on the other hand, women are more prone to develop autoimmune diseases. Several factors have been studied and implied to play a role for such an imbalance, most notably sex chromosomes, sex hormones, and gut microbiota differences between sexes. Experimental studies on rodents demonstrate that sex chromosome abnormalities, alterations of gut microbiota composition, and fluctuations of sex hormone concentrations decrease the susceptibility to autoimmunity in female probes or increase it in the male counterparts. Nevertheless, it would be reductive to consider sex only as a risk factor; based on clinical experience, autoimmune disease onset and course differ between men and women in terms of disease progression and severity. Eventually, research has focused on sex as a determinant of antirheumatic treatment response with promising evidence for a further personalized management of patients with autoimmune diseases.