Disclosure and adverse effects of complementary and alternative medicine used by hospitalized patients in the North East of England

Objectives: This study aimed to investigate the prevalence, disclosure and adverse effects of complementary and alternative medicine (CAM) use in hospitalised patients, and to explore the associations between patients’ perceived side-effects and relevant factors. Methods: Patients who were admitted to a district general hospital and met the eligibility criteria were interviewed using a semi-structured questionnaire. Their medications and pertinent details were verified from the medical notes. All quantitative and qualitative data were collated and analysed. A chi-squared test was performed to test the associations of the perceived CAM side-effects with the significance level determined at a=0.05. Results: A total of 240 in-patients completed the study. They were mostly white British (98.8%). The prevalence of CAM use within two years was 74.6% and one month 37.9%. Only 19 of 91 patients (20.9%) using CAM within one month disclosed their current CAM applications. Nearly half of patients (45.8%) who used CAM within two years experienced various CAM side-effects that tended to resolve after discontinuation. Slightly more than half (57.6%) perceived CAM side-effects and their perceptions were significantly associated with gender (P=0.048) and consideration for future CAM use (P=0.033). Potential interactions between herbal remedies/dietary supplements and prescribed drugs, such as garlic with lisinopril or aspirin, were assessed in 82 patients (45.8%). Conclusion: Most in-patients used CAM and experienced some adverse effects. The disclosure of CAM use and its adverse outcomes should be encouraged by healthcare professionals

Combination of clozapine with an atypical antipsychotic: a meta-analysis

Purpose The purpose of this paper is to review the efficacy of atypical antipsychotics in combination with clozapine. Previous meta-analyses have assessed the use of both typical and atypical antipsychotics in combination with clozapine, combination treatment being withheld only for those patients deemed treatment resistant. Design/methodology/approach Outcomes assessed included: positive, negative and overall symptom score. The total numbers of participants (n=588) were scored using the Positive and Negative Symptom Scale/the Brief Psychiatric Rating Scale and effect sizes were used to judge the efficacy of the combination treatments. Data gained from the ten randomized, double blind, placebo controlled trials were analysed using the R statistical software. Findings The effect sizes gained from analysis showed a small benefit of combination therapy over clozapine monotherapy. Therefore, it is the recommendation of this analysis that alternative avenues be sought in order to treat patients who have a sub-optimal response to clozapine with a combination other than two second generation antipsychotics. Research limitations/implications The initial trials search unveiled 1,412 studies. After the inclusion and exclusion criteria were applied, ten trials were used in this meta-analysis. Practical implications The recommendation of this analysis that alternative medications be sought in order to treat patients who have a sub-optimal response to clozapine with a combination other than two second generation antipsychotics. This route should only be used once all other treatment options have been exhausted. Originality/value This meta-analytical study looks specifically at the combination of atypical antipsychotics with clozapine in comparison to clozapine monotherapy. This work extends existing meta-analysis by incorporating data from more recent trials. </jats:sec

Data integration with self-organising neural network reveals chemical structure and therapeutic effects of drug ATC codes

Anatomical Therapeutic Codes (ATC) are a drug classification system which is extensively used in the field of drug development research. There are many drugs and medical compounds that as yet do not have ATC codes, it would be useful to have codes automatically assigned to them by computational methods. Our initial work involved building feedforward multi-layer perceptron models (MLP) but the classification accuracy was poor. To gain insights into the problem we used the Kohonen self-organizing neural network to visualize the relationship between the class labels and the independent variables. The information gained from the learned internal clusters gave a deeper insight into the mapping process. The ability to accurately predict ATC codes was unbalanced due to over and under representation of some ATC classes. Further difficulties arise because many drugs have several, quite different ATC codes because they have many therapeutic uses. We used chemical fingerprint data representing a drugs chemical structure and chemical activity variables. Evaluation metrics were computed, analysing the predictive performance of various self-organizing models