A consensus-based practical and daily guide for the treatment of acne patients

Background: Many current guidelines provide detailed evidence-based recommendations for acne treatment. Objective: To create consensus-based, simple, easy-to-use algorithms for clinical acne treatment in daily office-based practice and to provide checklists to assist in determining why a patient may not have responded to treatment and what action to take. Methods: Existing treatment guidelines and consensus papers were reviewed. The information in them was extracted and simplified according to daily clinical practice needs using a consensus-based approach and based on the authors' clinical expertise. Results: As outcomes, separate simple algorithms are presented for the treatment of predominant comedonal, predominant papulopustular and nodular/conglobate acne. Patients with predominant comedonal acne should initially be treated with a topical retinoid, azelaic acid or salicylic acid. Fixed combination topicals are recommended for patients with predominant papulopustular acne with treatment tailored according to the severity of disease. Treatment recommendations for nodular/conglobate acne include oral isotretinoin or fixed combinations plus oral antibiotics in men, and these options may be supplemented with oral anti-androgenic hormonal therapy in women. Further decisions regarding treatment responses should be evaluated 8 weeks after treatment initiation in patients with predominant comedonal or papulopustular acne and 12 weeks after in those with nodular/conglobate acne. Maintenance therapy with a topical retinoid or azelaic acid should be commenced once a patient is clear or almost clear of their acne to prevent the disease from recurring. The principal explanations for lack of treatment response fall into 5 main categories: disease progression, non-drug-related reasons, drug-related reasons, poor adherence, and adverse events. Conclusion: This practical guide provides dermatologists with treatment algorithms adapted to different clinical features of acne which are simple and easy to use in daily clinical practice. The checklists to establish the causes for a lack of treatment response and subsequent action to take will facilitate successful acne management. © 2016 European Academy of Dermatology and Venereolog

Correction to: Pathogenesis, Clinical Signs and Treatment Recommendations in Brittle Nails: A Review (Dermatology and Therapy, (2020), 10, 1, (15-27), 10.1007/s13555-019-00338-x)

Unfortunately, the co-author name was incorrectly published as “Jose L. López-Esterbaranz” instead of ‘Jose L. López-Estebaranz” in the original article. The correct version of author name is updated here. The original article has been corrected. © 2020, The Author(s)

Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis : predictive model based on machine learning

Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. In the present study, we used predictive models based on machine learning to detect variables associated with achieving MDA in patients with recent-onset PsA. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest-type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. We used a confusion matrix to visualize the performance of the model. The sample comprised 158 patients. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. In our model, the variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index). The percentage of hits in the confusion matrix was 85.94%. A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA