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FENO suppression testing is practical and feasible during assessment for biologics in severe asthma. Patients with significant FENO suppression were less likely to be recommended biologics but saw similar reductions in exacerbation frequency.http://bit.ly/35oSoxP

Biologic therapies are used in atopic and/or eosinophilic asthma that remains uncontrolled on optimised conventional therapy [1–4]. Nonadherence with prescribed therapy is common in severe asthma [5] and National Health Service England's commissioning guideline [6] advises adherence assessment prior to commencing biologic therapy., FENO suppression testing is practical and feasible during assessment for biologics in severe asthma. Patients with significant FENO suppression were less likely to be recommended biologics but saw similar reductions in exacerbation frequency. http://bit.ly/35oSoxP

Objectives: Suppression of FeNO following a week of directly observed inhaled corticosteroid (ICS) therapy can provide objective evidence of non-adherence. We report the effect of demonstrating this on long-term clinical outcomes. Methods: Consecutive patients adherent to prescribed therapy based on clinical assessment, with FeNO levels greater than 45 ppb on two occasions and meeting criteria for biological therapy trials, underwent supervised directly observed ICS therapy over 8 days. In this, patients existing ICS/LABA inhalers were changed to once daily fluticasone / vilanterol (Relvar 184/22). Inhaled therapy technique was taught by and taken under direct supervision of a specialist nurse; in person or remotely via Skype. FeNO, spirometry and the Asthma Control Questionnaire -7 (ACQ-7) were recorded on the first and last days. Those identified as non-adherent (≥42% FeNO suppression at day-8) were appropriately educated. Initiation of biological therapy at 6 months or more of follow up is presented. Results: Of 46 (males 23, mean age 49) subjects 19 (males 7, mean age 41) were identified as non-adherent. Of these 19 subjects, 14 did not need to progress to biological therapies. FeNO levels significantly decreased from baseline to day 8 (p Conclusion: Combining once-daily ICS with remote access technology, FeNO suppression is a feasible objective test in routine clinical practice and demonstrating non-adherence may lead to sustained improvement in asthma control.

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality. The cornerstone of pharmacological treatment for COPD is bronchodilation. Inhaled glycopyrronium bromide is a long-acting muscarinic antagonist developed as a maintenance treatment for patients with COPD. Phase III trials have shown that glycopyrronium produces rapid and sustained bronchodilation with an efficacy similar to tiotropium and is well tolerated, with a low incidence of muscarinic side effects in patients with moderate to severe COPD. A combination of glycopyrronium bromide with indacaterol maleate (QVA149) has recently been approved as a once-daily maintenance therapy in adult patients with COPD. Phase III trials (the IGNITE program) with QVA149 have demonstrated significant improvements in lung function versus placebo, glycopyrronium, and tiotropium in patients with moderate to severe COPD, with no safety concerns of note. Hence QVA149 is a safe treatment option for moderate to severe COPD patients in whom long-acting muscarinic antagonist monotherapy is inadequate.

Introduction Measurement of fractional exhaled nitric oxide (FeNO) is an easy to perform non-invasive test and surrogate marker of eosinophilic airway inflammation. It has been suggested that suppression of FeNO following a week of directly observed inhaled corticosteroid (ICS) therapy provides objective evidence of non-adherence. Though guidelines for the prescription of novel steroid sparing agents recommend this strategy as part of evaluation, it has not been rigorously evaluated in routine clinical practice. We report the outcomes of using this strategy as part of standard clinical care in our centre. Methods Consecutive consenting patients with FeNO levels greater than 45 ppb on two occasions who were adherent to prescribed therapy on the basis of clinical history, and meeting criteria for initiation of biological therapy undertook directly observed ICS therapy – supervised (DOTS) over 8 days. In this, patients existing ICS/LABA combination inhalers were changed to once daily fluticasone/vilanterol (Relvar 184/22). Inhaler technique was taught by a specialist nurse and inhaled therapy taken under direct supervision (in person or remotely via Skype). FeNO, spirometry, eosinophil count and the Asthma Control Questionnaire −7 (ACQ-7) were recorded on the first and last days. FeNO was also recorded on day 4. Data are presented as mean ±SD. Results Sixteen subjects (7 males, age 43±18 years) completed assessments (1 did not have day 4 FeNO value). All but one had ≥80% prescription pick-up rates checked with GP records. FeNO levels decreased from 121±63 ppb to 71±36 ppb at day 4 and 66±43 ppb at day 8 (p Conclusion Combining the use of once a day therapy with remote assessment using appropriate technology, FeNO suppression is a feasible objective test of adherence in the routine clinical setting. Despite appropriate refill collection rates over half of subjects were identified as non-adherent to inhaled therapy. Although these individuals did not commence biological therapy, improvements have been sustained. We recommend routine use of this assessment in severe asthma services.

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.

The aim of this paper is to design an efficient and fast clonal algorithm for solving various numerical and combinatorial real-world optimization problems effectively and speedily, irrespective of its complexity. The idea is to accurately read the inherent drawbacks of existing immune algorithms (IAs) and propose new techniques to resolve them. The basic features of IAs dealt in this paper are: hypermutation mechanism, clonal expansion, immune memory and several other features related to initialization and selection of candidate solution present in a population set. Dealing with the above-mentioned features we have proposed a fast clonal algorithm (FCA) incorporating a parallel mutation operator comprising of Gaussian and Cauchy mutation strategy. In addition, a new concept has been proposed for initialization, selection and clonal expansion process. The concept of existing immune memory has also been modified by using the elitist mechanism. Finally, to test the efficacy of proposed algorithm in terms of search quality, computational cost, robustness and efficiency, quantitative analyses have been performed in this paper. In addition, empirical analyses have been executed to prove the superiority of proposed strategies. To demonstrate the applicability of proposed algorithm over real-world problems, Machine-loading problem of flexible manufacturing system (FMS) is worked out and matched with the results present in literature.

Introduction Intrathecal injection of the wrong drug during chemotherapy treatment of cancer patients has been identified as a major patient safety issue, prompting an update of the national guidelines 1 in 2008 and an NPSA rapid response report. 2 In situ simulation helps in identification of latent errors (potential hazards in the system) 3 and effective training. We conducted an in situ simulation exercise to deliver training of safe administration of intrathecal chemotherapy. Methods An unannounced in situ simulation activity was carried out in the Haematology intrathecal day-unit. Low fidelity simulation in the form of an actor as patient was employed. Haematology consultant, nursing staff and pharmacist were available to provide assistance to the candidate during the scenario. 4 participants from the Haematology department (2 consultants, 2 registrars) were involved. The participants completed a pre- and post-simulation questionnaire. Video debriefing was performed immediately after each scenario, focussing on adherence to guidelines, decision making, team working and leadership. Results Three latent errors (2 equipment, 1 knowledge) were highlighted and recommendations were made to rectify these issues. Knowledge deficits were identified and addressed during the debrief. Feedback from the candidates was very positive, with unanimous appreciation of realism of the scenario. Impact on practice Two of the latent errors were resolved promptly, leading to improved patient care. This pilot study has been well received and the results are encouraging. We believe this is the first endeavour of its kind: using In situ simulation to improve patient safety in intrathecal chemotherapy. We plan to deliver this exercise as a regular training session for the Haematology department. References Department of Health (DH). HSC 2008/0 01 updated national guidance on the safe administration of intrathecal chemotherapy . London: DH, 2008 National Patient Safety Agency. National Patient Safety Agency, Rapid Response Report, Using Vinca Alkaloid adult minibags (adult/adolescent units) (NPSA/2008/RRR004), 2009. [Accessed on 05.05.2015] Patterson MD, et al . In situ simulation: detection of safety threats and teamwork training in a high risk emergency department. BMJ Qual Saf. 2013;22:468–477

Background In spite of doctor-patient consultations being a fundamental part of clinical practice, poor communication is cited consistently as a factor in patient complaints. 1 Effective consultation skills have been extensively linked to many positive patient outcomes. 2 Unfortunately, consultation skills teaching receives little attention in most specialist training. Simulation based learning (SBL) is becoming a widely used methodology in medical education. In this project, we aim to assess the effect of SBL on consultation skills compared to traditional teaching methods. Methods This is a prospective, randomised-controlled, multi-site trial. Subjects are trainee doctors recruited from within the Yorkshire and Humber Deanery, at Foundation year 2 or Core trainee level. Subjects undergo a video-assessed consultation with a patient presenting with chest pain, either in an outpatient chest pain clinic or on a medical admissions unit. The videos are then assessed using the MAAS-Global and mini-CEX consultation assessment tools. Subjects are then randomised to undergo either traditional teaching, in the form of a tutorial, or a simulation course. Both groups are reassessed in the same way as at the initial assessments. Results/outcomes The principal outcome will be a comparison of the change in the MAAS-Global assessment score for the 2 groups between baseline and after intervention. Subsidiary outcomes will include comparison of the change in score in the patient-survey for the trainee performance, the consultant-survey and qualitative analysis of the perceptions of trainees and trainers of the utility and acceptability of the training. Conclusions Results of this study will be used to further inform the debate around the use of simulation training in healthcare. There are still those that are cynical about the potential for transfer into the clinical environment, and a positive result from this study would help to inform that debate. References Kelly M. A practical guide for teachers of communication skills. A summary of current approaches . Radcliffe Publishing, 2007 Kurtz, SM, Silverman J, Draper J. Teaching and learning communication skills in medicine . Radcliffe Pub, 2005

Background Evidence shows lack of competency among health care professionals (HCPs) to demonstrate correct inhaler technique to patients. Aim: We conducted a multicentre RCT at 2 tertiary hospitals to investigate whether simulation-based training using Aerosol Inhalation Monitor (AIM) and written information is more effective compared to training using written information alone. Method 160 HCPS (69 doctors, 80 nurses, 8 physiotherapists, 3 pharmacists) were randomised into 2 groups of simulation and written information (Intensive Intervention Arm (IIA; n=80)) and written information only (Minimal Intervention Arm (MIA; n=80)) arms. Inhaler technique was assessed using the AIM machine and checklist score (maximum score 20; 10 for MDI and 10 DPI) which were conducted immediate-post teaching and 1-week after. Results: Cumulative combined (MDI+DPI) median scores for the IIA group showed significant improvement at post-teaching compared to MIA at 1-week (p

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.