Computer simulation of breast reduction surgery

Background: Plastic surgery of the breast, particularly breast reduction, is considered difficult. It can become a challenge for a less experienced surgeon to understand exactly what to do when facing a particular type of breast and how to avoid unsatisfactory results. Methods: The goal of this study was to create a computer model of the breast that provides a basis for the simulation of breast surgery, particularly breast reduction. The reconstruction of elastic parameters is based on observations of the breast with the patient in different positions. Results: It is shown that several measurements with the patient in different positions allow one to choose the parameters of the model and determine the elastic coefficients of the breast and the skin. The geometry of the breast before and after surgery is simulated. A qualitative study of the incision parameters’ influence on the final geometry of the breast is presented. Conclusion: The developed methodology and software allow one to estimate the form of the breast after the surgery by knowing its form before surgery and taking into consideration the parameters of incision applied by the surgeon at the time of surgery. The described approach can be used for the qualitative and quantitative study of breast reduction surgery with a satisfactory result. Level of Evidence: V (This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266.

Allergen Immunotherapy in MASK‐Air Users in Real‐Life: Results of a Bayesian Mixed‐Effects Model

Background: Evidence regarding the effectiveness of allergen immunotherapy (AIT) on allergic rhinitis has been provided mostly by randomised controlled trials, with little data from real-life studies. Objective: To compare the reported control of allergic rhinitis symptoms in three groups of users of the MASK-air® app: those receiving sublingual AIT (SLIT), those receiving subcutaneous AIT (SCIT), and those receiving no AIT. Methods: We assessed the MASK-air® data of European users with self-reported grass pollen allergy, comparing the data reported by patients receiving SLIT, SCIT and no AIT. Outcome variables included the daily impact of allergy symptoms globally and on work (measured by visual analogue scales-VASs), and a combined symptom-medication score (CSMS). We applied Bayesian mixed-effects models, with clustering by patient, country and pollen season. Results: We analysed a total of 42,756 days from 1,093 grass allergy patients, including 18,479 days of users under AIT. Compared to no AIT, SCIT was associated with similar VAS levels and CSMS. Compared to no AIT, SLIT-tablet was associated with lower values of VAS global allergy symptoms (average difference = 7.5 units out of 100; 95% credible interval [95%CrI] = -12.1;-2.8), lower VAS Work (average difference = 5.0; 95%CrI = -8.5;-1.5), and a lower CSMS (average difference = 3.7; 95%CrI = -9.3;2.2). When compared to SCIT, SLIT-tablet was associated with lower VAS global allergy symptoms (average difference = 10.2; 95%CrI = -17.2;-2.8), lower VAS Work (average difference = 7.8; 95%CrI = -15.1;0.2), and a lower CSMS (average difference = 9.3; 95%CrI = -18.5;0.2). Conclusion: In patients with grass pollen allergy, SLIT-tablet, when compared to no AIT and to SCIT, is associated with lower reported symptom severity. Future longitudinal studies following internationally-harmonised standards for performing and reporting real-world data in AIT are needed to better understand its 'real-world' effectiveness.info:eu-repo/semantics/publishedVersio

Digitally‐Enabled, Patient‐Centred Care in Rhinitis and Asthma Multimorbidity: The ARIA‐MASK‐air ® Approach

MASK-air® , a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air® is a Good Practice of DG Santé on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air® results should lead to change management in rhinitis and asthma.info:eu-repo/semantics/publishedVersio