Post-traumatic stress disorder and post-traumatic stress symptoms in patients with systemic autoimmune diseases during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 outbreak led to an increase in mental disorders, particularly post-traumatic stress disorder (PTSD), in the general population and especially in high-risk populations such as patients with rheumatologic conditions. Although these latters are considered vulnerable to developing PTSD, few specific data have been particularly reported in the framework of the pandemic. The aim of the present study was to investigate PTSD and post-traumatic stress symptoms (PTSS) in a sample of patients with systemic autoimmune disease (SAD), followed in the framework of a prospective observational study during the pandemic. METHODS: The PERMAS project is a prospective observational study including patients with SAD and involving the Rheumatology and the Psychiatric Clinics of the Azienda Ospedaliero Universitaria Pisana (AOUP, Pisa, Italy) and the Institute of Management of the Scuola Superiore Sant'Anna (Pisa, Italy). The assessments included: a data-sheet for sociodemographic and clinical characteristics; the Trauma and Loss Spectrum-Self Report (TALS-SR) and the Impact of Event Scale-Revised (IES-R), for PTSD and PTSS; the 36-item Short Form Survey (SF-36) to assess quality of life. RESULTS: The total sample consisted of 252 patients with SAD, including 131 with connective tissue disease, 101 with arthritis and 20 with systemic vasculitis. The diagnostic groups differed significantly in age (p<0.001), gender (p<0.001), prevalence of full-blown and partial PTSD (p=0.001), and other psychopathologic variables. Connective tissue disease and SF-36 were significantly associated with the TALS-SR scores in both univariate (p<0.001) and multivariate (p<0.025; p<0.001) analyses. CONCLUSIONS: Patients with SAD, and, in particular, patients with connective tissue diseases reported an increased risk of developing stress-related psychopathological symptoms, indicating the need for special psychological monitoring of this high-risk group.

How Italy Tweeted about COVID-19: Detecting Reactions to the Pandemic from Social Media.

The COVID-19 pandemic required communities throughout the world to deal with unknown threats. Using Twitter data, this study aimed to detect reactions to the outbreak in Italy and to evaluate the relationship between measures derived from social media (SM) with both national epidemiological data and reports on the violations of the restrictions. The dynamics of time-series about tweets counts, emotions expressed, and themes discussed were evaluated using Italian posts regarding COVID-19 from 25 February to 4 May 2020. Considering 4,988,255 tweets, results highlight that emotions changed significantly over time with anger, disgust, fear, and sadness showing a downward trend, while joy, trust, anticipation, and surprise increased. The trend of emotions correlated significantly with national variation in confirmed cases and reports on the violations of restrictive measures. The study highlights the potential of using SM to assess emotional and behavioural reactions, delineating their possible contribution to the establishment of a decision management system during emergencies.

Adherence to treatment in Behçet's syndrome: a multi-faceted issue.

OBJECTIVES: The aim of this study is to explore the adherence to treatment in patients with Behçet's syndrome (BS), to identify the diverse adherence profiles and their correlations. METHODS: A cross-sectional study among adult BS patients was conducted administering an ad-hoc questionnaire to BS patients with the aim of investigating several dimensions related to BS management, including attitudes towards treatment. A Latent Class Analysis (LCA) was performed to identify adherence profiles and associated characteristics were identified using logistic regression analysis. RESULTS: A total of 207 patients answered the survey and 180 of them declared to take medication for BS, thus representing the study population. More than a third of the respondents have declared that they have skipped treatments before and autonomously modified (reduced or increased) the dosage of the treatment without medical consultation. LCA analysis allowed the identification of two distinct profiles, one more stick with recommended medication and the other less adherent to treatment. The less-adherent BS patient profile seems to be related with being in the third decade of life, being diagnosed with BS for more than 5 years and perceiving greater psychological impact of the disease. CONCLUSIONS: Addressing adherence in BS is not only related to measuring treatment adherence and identifying the barriers and the limitations; in fact, it should also encompass a wider approach that includes the awareness, the socio-psychological impact of the disease as well as patient education.

Duration of response after DEB-TACE compared to lipiodol-TACE in HCC-naïve patients: a propensity score matching analysis.

OBJECTIVES: To retrospectively compare long-term outcomes of first-line drug-eluting particle (DEB)- transarterial chemoembolization (TACE) and lipiodol-TACE, in patients with unresectable hepatocellular (HCC). METHODS: We retrospectively reviewed our database to identify adult patients with treatment-naïve unresectable HCC, who underwent TACE from 2006 to 2013. Patients were excluded in the absence of complete medical records relative to first TACE, 1-month follow-up, and/or sufficient follow-up data. Periprocedural complications, duration of hospitalization, 1-month tumor response by mRECIST, time to tumor progression (TTP) and target tumor progression (TTTP), and overall survival (OS) were evaluated. RESULTS: Out of an initial series of 656 patients, 329 patients were excluded for unavailability of sufficient baseline and/or follow-up data. The remaining 327 patients underwent either lipiodol-TACE (n = 160) or DEB-TACE (n = 167). Patients treated with lipiodol-TACE had a significantly higher tumor burden. By propensity score, patients were matched according to baseline differences (BCLC stage, uninodular or multinodular HCC, and unilobar or bilobar HCC), resulting in 101 patients in each treatment group. Lipiodol-TACE was associated with a significantly higher incidence of adverse events (p = 0.03), and longer hospitalization (mean, 2.5 days vs 1.9 days; p = 0.03), while tumor response, TTP, and OS were comparable. In patients achieving 1-month complete response (CR) of target tumor, TTTP was significantly (p = 0.009) longer after DEB-TACE compared to lipiodol-TACE (median, 835 vs 353 days), resulting in a lower number of re-treatments during the entire follow-up (0.75 vs 1.6, p = 0.01). CONCLUSION: Compared to lipiodol-TACE, DEB-TACE offers higher tolerability, reduced hospitalization, and more durable target tumor response after CR. KEY POINTS: • Compared to lipiodol-TACE, DEB-TACE is better tolerated and has reduced side effects, which translates into shorter hospitalization. • When complete radiological response according to the mRECIST is obtained 1 month after the procedure, DEB-TACE offers a more durable local tumor control compared to lipiodol-TACE. • In these patients, the longer duration of response after DEB-TACE translates into a lower number of re-interventions.

Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day.

Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text.