Tofacitinib citrate is an oral Janus kinase 1/3 inhibitor approved for rheumatoid arthritis, ulcerative colitis, and active psoriatic arthritis. Tofacitinib is being increasingly used off-label for dermatological conditions, with varying efficacy across recent studies. A review of these studies will be a helpful resource for dermatologists considering the use of tofacitinib for conditions refractory to first-line therapies.MEDLINE, Embase, CINAHL Plus, Cochrane Library, Scopus, Web of Science, Clinicaltrials.gov, and the WHO International Clinical Trials Registry Platform were all searched for articles and trials mentioning the term 'tofacitinib', then manually reviewed to identify published data on off-label uses of tofacitinib. The article was structured according to the quality of the evidence available.Tofacitinib appears to show strong efficacy for numerous dermatologic conditions. Randomized controlled trial data is available for atopic dermatitis, alopecia areata, and plaque psoriasis. Case report and case series data is available for numerous other dermatologic conditions.While tofacitinib has a wide array of immunoregulatory properties, making it a possible candidate for treating many dermatologic conditions refractory to other treatments, further testing is needed to better characterize its efficacy and utility moving forward, as well as its safety and adverse effect profile.
Abstract In this study, we sought to analyze the readability of online patient education materials (PEMs) related to juvenile dermatomyositis (JDM). We analyzed the top 100 Google results and using six different readability scores, found 53 PEMs which had an average grade reading level of 17.4 (graduate level). PEMs by health care providers were written at higher grade levels than those by non‐health care providers. Our findings demonstrate a clear need for online JDM PEMs that are written at an appropriate reading level and can be comprehended by patients and families of all levels of health literacy.
Immune checkpoint inhibitors (ICIs) are a pillar of cancer therapy with demonstrated efficacy in a variety of malignancies. However, they are associated with immune-related adverse events (irAEs) that affect many organ systems with varying severity, inhibiting patient quality of life and in some cases the ability to continue immunotherapy. Research into irAEs is nascent, and identifying patients with adverse events poses a critical challenge for future research efforts and patient care. This study9s objective was to develop an electronic health record (EHR)-based model to identify and characterize patients with ICI-associated arthritis (checkpoint arthritis).
Methods
Forty-two patients with checkpoint arthritis were chart abstracted from a cohort of all patients who received checkpoint therapy for cancer (n=2,612) in a single-center retrospective study. All EHR clinical codes (N=32,198) were extracted including International Classification of Diseases (ICD)-9 and ICD-10, Logical Observation Identifiers Names and Codes (LOINC), RxNorm, and Current Procedural Terminology (CPT). Logistic regression, random forest, gradient boosting, support vector machine, K-nearest neighbors, and neural network machine learning models were trained to identify checkpoint arthritis patients using these clinical codes. Models were evaluated using receiver operating characteristic area under the curve (ROC-AUC), and the most important variables were determined from the logistic regression model. Models were retrained on smaller fractions of the important variables to determine the minimum variable set necessary to achieve accurate identification of checkpoint arthritis.
Results
Logistic regression and random forest were the highest performing models on the full variable set of 32,198 clinical codes (AUCs: 0.911, 0.894, respectively) (table 1). Retraining the models on smaller fractions of the most important variables demonstrated peak performance using the top 31 clinical codes, or 0.1% of the total variables (figure 1). The most important features included presence of ESR, CRP, rheumatoid factor lab, prednisone, joint pain, creatine kinase lab, thyroid labs, and immunization, all positively associated with checkpoint arthritis (figure 2).
Conclusions
Our study demonstrates that a data-driven, EHR based approach can robustly identify checkpoint arthritis patients. The high performance of the models using only the 0.1% most important variables suggests that only a small number of clinical attributes are needed to identify these patients. The variables most important for identifying checkpoint arthritis included several unexpected clinical features, such as thyroid labs and immunization, indicating potential underlying irAE associations that warrant further exploration. Finally, the flexibility of this approach and its demonstrated effectiveness could be applied to identify and characterize other irAEs.
Ethics Approval
This study was approved by the Northwestern University Institutional Review Board, ID STU00210502, with a granted waiver of consent AUC was calculated from the ROC curve. Sensitivity, specificity, PPV, and NPV were determined at the threshold maximizing the F1-score. AUC = area under the curve, ROC = receiver operating characteristic, PPV = positive predictive value, NPV = negative predictive value
Purpose: Apremilast is a phosphodiesterase-4 inhibitor FDA approved for psoriatic arthritis and moderate to severe plaque psoriasis. In recent years, multiple studies have suggested other potential uses for apremilast in dermatology. A summary of these various studies will be a valuable aid to dermatologists considering apremilast for an alternative indication.Materials and methods: The PubMed/MEDLINE and ClinicalTrials.gov databases were queried with the term 'apremilast,' with results manually screened to identify published data on off-label uses of apremilast. The article was structured by the quality of evidence available.Results: Apremilast use in dermatology beyond plaque psoriasis and psoriatic arthritis is frequently described in the literature, with a mixture of positive and negative results. Randomized controlled data is available for Behçet's disease, hidradenitis suppurativa, nail/scalp/palmoplantar psoriasis, alopecia areata, and atopic dermatitis.Conclusion: The relatively safe adverse effect profile of apremilast and its broad immunomodulatory characteristics may make it a promising option in the future for patients with difficult to treat diseases in dermatology, refractory to first line therapies, but further studies will be necessary to clarify its role.
