Patient/carers' recollection of medicines related information from an out-patient clinic appointment

AIM: To identify what medicines related information children/young people or their parents/carers are able to recall following an out-patient clinic appointment. METHOD: A convenience sample of patients' prescribed at least one new long-term (>6 weeks) medicine were recruited from a single UK paediatric hospital out-patient pharmacy. A face-to-face semi-structured questionnaire was administered to participants when they presented with their prescription. The questionnaire included the following themes: names of the medicines, therapeutic indication, dose regimen, duration of treatment and adverse effects.The results were analysed using Microsoft Excel 2013. RESULTS: One hundred participants consented and were included in the study. One hundred and forty-five medicines were prescribed in total. Participants were able to recall the names of 96 (66%) medicines and were aware of the therapeutic indication for 142 (97.9%) medicines. The dose regimen was accurately described for 120 (82.8%) medicines with the duration of treatment known for 132 (91%). Participants mentioned that they had been advised about side effects for 44 (30.3%) medicines. Specific counselling points recommended by the BNFc1, were either omitted or not recalled by participants for the following systemic treatments: cetirizine (1), chlorphenamine (1), desmopressin (2), hydroxyzine (2), itraconazole (1), piroxicam (2), methotrexate (1), stiripentol (1) and topiramate (1). CONCLUSION: Following an out-patient consultation, where a new medicine is prescribed, children and their parents/carers are usually able to recall the indication, dose regimen and duration of treatment. Few were able to recall, or were told about, possible adverse effects. This may include some important drug specific effects that require vigilance during treatment.Patients, along with families and carers, should be involved in the decision to prescribe a medicine.2 This includes a discussion about the benefits of the medicine on the patient's condition and possible adverse effects.2 Treatment side effects have been shown to be a factor in treatment non-adherence in paediatric long-term medical conditions.3 Practitioners should explain to patients, and their family members or carers where appropriate, how to identify and report medicines-related patient safety incidents.4 However, this study suggests that medical staff may not be comfortable discussing the adverse effects of medicines with patients or their parents/carers.Further research in to the shared decision making process in the paediatric out-patient clinic when a new long-term medicine is prescribed is required to further support medicines adherence and the patient safety agenda.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.