The inhibition of the mitogen-activated protein kinases signalling pathway through combined use of BRAF and MEK inhibitors (BRAFi+MEKi) represents an established therapeutic option in patients with ...BRAF-mutated, advanced melanoma. These efficient therapies are well tolerated with mostly moderate and reversible side effects and a discontinuation rate due to adverse events of 11.5%–15.7%. Median duration of therapy ranges between 8.8 and 11.7 months. Based on data from confirmatory trials, safety profiles of three BRAFi+MEKi combinations were reviewed, that is, dabrafenib plus trametinib, vemurafenib plus cobimetinib and encorafenib plus binimetinib. Many adverse events are class effects, such as cutaneous, gastrointestinal, ocular, cardiac and musculoskeletal events; some adverse events are substance associated. Fever (dabrafenib) and photosensitivity (vemurafenib) are the most common and clinically prominent examples. Other adverse events are less frequent and the association to one substance is less strong such as anaemia, facial paresis (encorafenib), neutropenia (dabrafenib), skin rash, QTc prolongation and increased liver function tests (vemurafenib). This narrative review provides recommendations for monitoring, adverse event evaluation and management focusing on the clinically relevant side effects of the three regimens.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of ...smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps.
We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use.
After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis.
Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use.
This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Objectives: To analyze occurrence and plasticity of two recently described distinct subtypes of Th1 cells named classic (CD161−/CCR6−) and non-classic (CD161+/CCR6+) Th1 cells in early rheumatoid ...arthritis (RA) patients and healthy controls (HCs).
Methods: Frequencies of in vivo-generated Th1 cell populations were assessed after cytokine secretion assay for IFNγ/IL-17 and surface staining for CD161/CCR6. Viable Th1 cells (IFNγ+IL-17−) were sorted into classic Th1 (CD161-CCR6−) and non-classic Th1 (CD161+CCR6+) cells, trans-differentiated under different Th cell-inducing conditions, and assessed for plastic changes by analyzing the Th cell-associated cytokine and transcription factor profiles.
Results: Ex vivo frequencies of classic (CD161−CCR6−) and non-classic (CD161+CCR6+) Th1 cells as well as related Th1 cell subpopulations CD161+CCR6− and CD161−/CCR6+ did not differ significantly between RA and HCs. However, trans-differentiation of ex vivo non-classic (CD161+CCR6+) and CD161−/CCR6+ Th1 cells resulted in a substantial shift toward Th17 and Th1/Th17 phenotypes, particularly under Th17-inducing conditions. In contrast, classic (CD161−/CCR6−) and CD161+CCR6− Th1 cells showed higher plasticity towards IL-4-producing cells, most of them shifting to a Th1/Th2 phenotype.
Conclusion: Whereas non-classic (CD161+/CCR6+) and CD161-CCR6+ Th1 cells demonstrated an increased plasticity towards IL-17- phenotypes, classic Th1 and CD161+CCR6− Th1 cells showed more plasticity towards IL-4-producing phenotypes.
Innate immune memory allows macrophages to adequately respond to pathogens to which they have been pre-exposed. To what extent different pattern recognition receptors, cytokines and resolution ...signals influence innate immune memory needs further elucidation. The present study assessed whether lipopolysaccharide (LPS) tolerance in monocytes and macrophages is affected by these factors. Human CD14
cells were isolated from peripheral blood, stimulated by LPS and re-stimulated after 3 days of resting. Hereafter, immune-responsive gene 1 (IRG-1), heme oxygenase 1 (HO-1), tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) expression were assessed. Our study revealed the following findings: (1) While pre-stimulation with the Toll-like receptor 4 ligand LPS inhibits the induction of IRG-1, TNF-α and IL-6 expression, pre-stimulation with TLR 1/2 ligands only affects cytokine production but not IRG-1 expression upon subsequent TLR4 engagement. (2) Prior TNF-α stimulation does not affect LPS tolerance but rather increases LPS-mediated cytokine expression. (3) Dimethyl itaconate (DMI) inhibits the expression of IRG-1 in a dose-dependent manner but does not affect TNF-α or IL-6 expression. (4) Docosahexaenoic acid (DHA) partly inhibits IRG-1 expression in monocytes but not in M
and M
polarized macrophages. LPS tolerance is not affected in these cells by DHA. The data presented in this study partly corroborate and extend previous findings on innate immune memory and warrant further studies on LPS tolerance to gain a better understanding of innate immune memory at the molecular level.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) is a relatively new disease entity caused by ICI agents during cancer therapy. Reactive arthritis (ReA) is a well-known disease ...entity caused by urogenital or gastrointestinal bacterial infection or pneumonia. In this sense, ICI-IA and ReA are both defined by a reaction to a well-specified causal event. As a result, comparing these diseases may help to determine therapeutic strategies.
We compared ICI-IA and ReA with special focus on pharmacological management. Specifically regarding treatment, we conducted a literature search of studies published in the PubMed database. Inclusion criteria were studies on treatment with non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids (GC), or disease modifying antirheumatic drugs (DMARDs) in ICI-IA or ReA. During systematic selection, 21 studies evaluating ICI-IA and 14 studies evaluating ReA were included.
In ICI-IA, prospective and retrospective studies have shown effects of non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoid (GC), sulfasalazine (SSZ), methotrexate (MTX), hydroxychloroquine (HCQ) and TNFi. In ReA, retrospective studies evaluated NSAIDs and GC. A randomized controlled trial reported the effect of SSZ, and a retrospective study reported the effect of MTX and SSZ in combination with tumor necrosis factor alpha inhibition (TNFi). For both entities, small case reports show treatment effects of interleukin 6 receptor inhibition (IL-6Ri).
This literature review identified both similarities and differences regarding the pathogenesis and clinical features of ReA and ICI-IA. Studies on treatment reported effectiveness of NSAIDs, GC, MTX, SSZ and TNFi in both diseases. Further, small case reports showed effects of IL-6Ri.
Display omitted
•ICI-IA and ReA are both characterized by a synovial reaction to a well-defined causal event.•There were both similarities and differences in the clinical features of ReA and ICI-IA.•We reveal the efficacy of several csDMARDs and bDMARDs in both ReA and ICI-IA.•The potential for a self-limiting disease course is important for choice of treatment strategy.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Since immune checkpoint inhibitors became the standard of care for an increasing number of indications, more patients have been exposed to these drugs and physicians are more challenged with ...the management of a unique spectrum of immune-related adverse events (irAEs) associated with immune checkpoint inhibitors. Those irAEs of autoimmune or autoinflammatory origin, or both, can involve any organ or tissue, but most commonly affect the dermatological, gastrointestinal and endocrine systems. Rheumatic/systemic irAEs seem to be less frequent (although underreporting in clinical trials is probable), but information on their management is highly relevant given that they can persist longer than other irAEs. Their management consists of anti-inflammatory treatment including glucocorticoids, synthetic and biologic immunomodulatory/immunosuppressive drugs, symptomatic therapies as well as holding or, rarely, discontinuation of immune checkpoint inhibitors. Here, we summarize the management of rheumatic/systemic irAEs based on data from clinical trials but mainly from published case reports and series, contextualize them and propose perspectives for their treatment.
Although neurohormones and Renin‐Angiotensin‐Aldosterone‐System (RAAS) components are important predictors of cardiovascular mortality (CVM), their importance for predicting outcomes in patients ...with/without RAAS‐blockers and different degrees of arterial stiffness is less understood. We therefore analyzed long‐term data from the Ludwigshafen Risk and Cardiovascular Health (LURIC) study in 3316 patients subdivided according to pulse pressure (PP) and RAAS‐blocker use. Patients on RAAS‐inhibition had higher renin and noradrenaline, lower aldosterone and aldosterone/renin quotient (ARQ). Renin and noradrenaline significantly predicted CVM in patients without RAAS‐blocker (HR = 1.17, 1.15) and in patients receiving angiotensin‐converting‐enzyme (ACE) inhibitors (HR = 1.17, 1.29), whereas aldosterone predicted CVM only in patients receiving ACE‐inhibitors (HR = 1.13). CVM was predicted independently from PP by renin, noradrenaline and angiotensin II. Independently from RAAS inhibition renin decreased and ARQs increased with rising PP. Furthermore, noradrenaline increased with PP, but only without ACE‐inhibition. The HR for CVM in the ACE‐inhibitor group were 1.29, 1.28, 1.29 for renin in the first, second and third PP quartiles and 1.22, and 1.19 for aldosterone in the second and fourth quartile. Furthermore, we showed that noradrenaline predicts CVM in all PP quartiles in patients with ACE‐inhibition. In the RAAS‐blocker‐free group, the HR for renin for CVM were 1.36 and 1.18 in the third and fourth PP quartiles, but neither aldosterone nor noradrenaline were predictive for CVM within the PP quartiles. Renin and noradrenaline are strong predictors of CVM regardless of RAAS blockade, whereas aldosterone is predictive only in the ACE‐inhibitor group. Catecholamines but not renin are associated with rising PP.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Psoriasis is a chronic inflammatory skin disease. The visibility of erythematous plaques on the skin as well as the pain and itchiness caused by the skin lesions frequently leads to psychological ...distress in patients. Smartphone apps are widespread and easily accessible. Earlier studies have shown that apps can effectively complement current management strategies for patients with psoriasis. However, no analysis of such apps has been published to date.
The aim of this study is to systematically identify and objectively assess the quality of current publicly available German apps for patients with psoriasis using the Mobile Application Rating Scale (MARS) and compile brief ready-to-use app descriptions.
We conducted a systematic search and assessment of German apps for patients with psoriasis available in the Google Play Store and Apple App Store. The identified apps were randomly assigned to 1 of 3 reviewers, who independently rated them using the German MARS (MARS-G). The MARS-G includes 15 items from 4 different sections (engagement, functionality, aesthetics, and information) to create an overall mean score for every app. Scores can range from 1 for the lowest-quality apps to 5 for the highest-quality apps. Apps were ranked according to their mean MARS-G rating, and the highest-ranked app was evaluated independently by 2 patients with psoriasis using the user version of the MARS-G (uMARS-G). Furthermore, app information, including origin, main function, and technical aspects, was compiled into a brief overview.
In total, we were able to identify 95 unique apps for psoriasis, of which 15 were available in both app stores. Of these apps, 5 were not specifically intended for patients with psoriasis, 1 was designed for clinical trials only, and 1 was no longer available at the time the evaluation process began. Consequently, the remaining 8 apps were included in the final evaluation. The mean MARS-G scores ranged from 3.51 to 4.18. The app with the highest mean MARS-G score was Psoriasis Helferin (4.18/5.00). When rated by patients, however, the app was rated lower in all subcategories, resulting in a mean uMARS-G score of 3.48. Most apps had a commercial background and a focus on symptom tracking. However, only a fraction of the apps assessed used validated instruments to measure the user's disease activity.
App quality was heterogeneous, and only a minority of the identified apps were available in both app stores. When evaluated by patients, app ratings were lower than when evaluated by health care professionals. This discrepancy highlights the importance of involving patients when developing and evaluating health-related apps as the factors that make an app appealing to users may differ between these 2 groups.
Deutsches Register Klinischer Studien DRKS00020963; https://tinyurl.com/ye98an5b.
To characterize treatment patterns for patients with psoriatic arthritis (PsA) currently receiving any disease-modifying antirheumatic drug (DMARD).
The
S
trategy for
P
soriatic
A
rthritis
I
n
G
...ermany (SPAIG) study was a retrospective observational study conducted from May to November 2017 at 46 rheumatology centers. Current and previous treatment data were collected at a single visit from adult patients with PsA and psoriasis who received DMARD treatment for ≥6 of the previous 12 months. The primary outcome was the proportion of patients receiving a biologic DMARD (bDMARD). Multinomial logistic regression analysis was used to evaluate associations between current characteristics and initial choice of therapy.
Mean age of the 316 patients was 55.1 years and mean PsA disease duration was 9.9 years. PsA activity was generally comparable across treatment groups. In this cohort, 57.3% of patients were currently treated with bDMARDs, 37.7% with conventional synthetic DMARDs, and 4.4% with targeted synthetic DMARDs. Almost half (48.4%) of patients reported DMARD modifications in the previous 12 months. Specific comorbidities and patient/disease characteristics were associated with initial therapy.
DMARD treatment of PsA is frequently modified, suggesting the need for more effective therapies and assessment tools.
Background Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex, multifactorial diseases significantly impacting health and quality of life. Predicting treatment response and disease ...progression is crucial for optimizing therapeutic interventions, yet challenging. Automated machine learning (AutoML) technology shows promise for rapidly creating accurate predictive models based on patient features and treatment data. Objective This study aims to develop highly accurate machine learning (ML) models using AutoML to address key clinical questions for PsV and PsA patients, including predicting therapy changes, identifying reasons for therapy changes, and factors influencing skin lesion progression or an abnormal Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score. Methods Clinical study data from 309 PsV and PsA patients were extensively prepared and analyzed using AutoML to build and select the most accurate predictive models for each variable of interest. Results Therapy change at 24 weeks follow-up was modeled using the extreme gradient boosted trees classifier with early stopping (area under the receiver operating characteristic curve AUC of 0.9078 and logarithmic loss LogLoss of 0.3955 for the holdout partition). Key influencing factors included the initial systemic therapeutic agent, the Classification Criteria for Psoriatic Arthritis score at baseline, and changes in quality of life. An average blender incorporating three models (gradient boosted trees classifier, ExtraTrees classifier, and Eureqa generalized additive model classifier) with an AUC of 0.8750 and LogLoss of 0.4603 was used to predict therapy changes for 2 hypothetical patients, highlighting the significance of these factors. Treatments such as methotrexate or specific biologicals showed a lower propensity for change. An average blender of a random forest classifier, an extreme gradient boosted trees classifier, and a Eureqa classifier (AUC of 0.9241 and LogLoss of 0.4498) was used to estimate PASI (Psoriasis Area and Severity Index) change after 24 weeks. Primary predictors included the initial PASI score, change in pruritus levels, and change in therapy. A lower initial PASI score and consistently low pruritus were associated with better outcomes. BASDAI classification at onset was analyzed using an average blender of a Eureqa generalized additive model classifier, an extreme gradient boosted trees classifier with early stopping, and a dropout additive regression trees classifier with an AUC of 0.8274 and LogLoss of 0.5037. Influential factors included initial pain, disease activity, and Hospital Anxiety and Depression Scale scores for depression and anxiety. Increased pain, disease activity, and psychological distress generally led to higher BASDAI scores. Conclusions The practical implications of these models for clinical decision-making in PsV and PsA can guide early investigation and treatment, contributing to improved patient outcomes.
PDF
