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Biomechanical stress may exacerbate inflammation in psoriatic arthritis (PsA). This study aimed to investigate disease activity, work disability, and drug response/retention rates in PsA patients among two different occupation's types: blue-collar workers (BCol) with manual labor versus white-collar workers (WCol) with sedentary occupations. PsA patients registered in the Swiss cohort (SCQM) were classified as BCol or WCol workers and assessed at the initiation of a biologic or targeted synthetic disease-modifying anti-rheumatic drug (b-/tsDMARD). We compared the baseline characteristics at treatment start and the DAS28-CRP for the 1-year remission. Treatment retention was investigated using Kaplan–Meier curves and Cox regression analysis. Multivariable models were adjusted for potential confounders. Of 564 patients, 29% were BCol, and 71% were WCol workers. Baseline disease activity was comparable between both groups. BCol workers were predominantly male (79.8%) and more work disabled at baseline (84.0% vs. 27.9%; p < 0.01). One hundred seventy-four treatment courses (TCs) of 165 PsA patients were included for longitudinal analysis. Occupation did not significantly influence the achievement of DAS28-CRP remission at 1 year. Kaplan–Meier analysis (n = 671) indicated longer retention for BCol workers (mean retention duration: 3.15 years vs. 2.15 years, (p = 0.006). However, adjusted Cox regression analysis did not corroborate these findings. This study indicates that physically demanding occupations correlate with increased rates of work disability among PsA patients, while treatment response seems to be unaffected by the patients' occupation type. Additional research is required to thoroughly comprehend the relationship between physical workload, disease activity, and treatment outcomes. Key Points • This study indicates that physically demanding occupations correlate with increased rates of work disability among PsA patients. • The treatment response among of PsA patients seems unaffected by the patients' occupation type. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen. [ABSTRACT FROM AUTHOR]

Background Among heart failure (HF) patients, hospital readmissions are a major concern. The medication taken by a patient may provide information on comorbidities and conditions and may be used as an indicator to identify populations at an increased risk of HF readmission. Aim This study explored the use of non-cardiovascular medication at hospital discharge from the first HF admission in search of indicators of high risk of readmission for HF. Method The study included 22,476 HF patients from the Dutch PHARMO Database Network at their first HF hospitalization. The data was divided into training and validation sets. A Cox regression model with demographics, date of first HF hospital admission and non-cardiovascular medication present at discharge, adjusted for cardiovascular medication, was developed in the training set and subsequently implemented in the validation set. Results The study included 22,476 patients, mean age 76.7 years (range 18–104) and median follow-up time 2.5 years (range 0–15.7 years). During the study period 6,725 (29.9%) patients were readmitted for HF, with a median time-to-readmission of 7 months (range 0–14.3 years). Non-cardiovascular medication associated with a high risk of readmission for HF were identified as indicators of high risk, with no implied causal relationship. Patients prescribed antigout medications presented a 25% increased risk of readmission (HR 1.25, 95%CI 1.09–1.45, P = 0.002). Patients using insulin had an 18% higher risk of readmission versus patients not using insulin (HR 1.18, 95%CI 1.06–1.32, P = 0.002), but not versus patients treated with other blood-glucose-lowering drugs. No association between the risk of readmission and NSAIDs use was observed. Conclusion The results suggest that diabetes is responsible for the higher HF-readmission risk observed in patients prescribed insulin. The observed risk in users of antigout medication should be further investigated. The absence of an association with the use of NSAIDs should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Introduction: The Janus kinase (JAK) inhibitors tofacitinib and baricitinib are new treatments for rheumatic diseases. Recent concerns regarding the risk of thrombosis have led to warnings by competent authorities. We therefore aimed to examine the thromboembolic safety signal for tofacitinib and baricitinib.Methods: Individual case safety reports (ICSRs) for tofacitinib and baricitinib were retrieved from the World Health Organization global database VigiBase in April 2019. Primary outcomes were deep vein thrombosis (DVT) and pulmonary thrombosis (PT) or pulmonary embolism (PE). Patient demographics were summarized and then stratified by outcome. Disproportionality analyses were conducted by estimating the reporting odds ratios (RORs) and 95% confidence intervals (CIs) worldwide, and stratified by either Europe or the US.Results: In both the tofacitinib (n = 40,017) and baricitinib (n = 2138) ICSRs, patients with reported DVT or PT/PE were older and had higher reporting of prothrombotic medications or antithrombotic treatments, suggesting a pre-existing thromboembolic risk/event. In Europe, tofacitinib was associated with increased reporting for DVT (ROR 2.37, 95% CI 1.23-4.56) and PT/PE (ROR 2.38. 95% CI 1.45-3.89). For baricitinib, a threefold increased reporting odds was observed for DVT (ROR 3.47, 95% CI 2.18-5.52) and PT/PE (ROR 3.44, 95% CI 2.43-4.88) in Europe. In the US, tofacitinib was only associated with an elevated ROR of PT (ROR 2.05, 95% CI 1.45-2.90) and no baricitinib ICSRs were reported.Conclusion: This study supports the current recommendation for cautious use of tofacitinib in patients with high thromboembolic risk. Moreover, with a similar patient profile and elevated reporting for baricitinib, a potential class effect of JAK inhibitors cannot be ruled out. [ABSTRACT FROM AUTHOR]