Cognitive aspects of childhood asthma

Research was undertaken to improve our knowledge about children’s awareness of respiratory sensations, beliefs about asthma of children with asthma and their parents, the nature and extent of childhood asthma sufferers’ psychological difficulties, and parents’ and children’s reasons for achieving good control of asthma. Recognition and accurate reporting of respiratory sensations have implications for asthma management. Illness beliefs explain differences in adaptation to chronic disease. Childhood asthma is situated within a family context. Asthma severity and the source of information may explain differences in reports of children’s psychological well-being. Low adherence with treatment recommendations has been reported, and chronic disease can influence quality of life. Participants were recruited from a hospital asthma database, primary care patient lists, and through state primary schools. The interviews involved physically healthy children, children with asthma, and the parents of children with asthma. Qualitative and quantitative methods involved the use of storyboards, semi- structured interviews, and questionnaires. The main arguments are that, (i) social interaction, in the context of childhood asthma, is a determinant of children’s sophisticated descriptions of respiratory sensations, (ii) children’s understanding of the different aspects of asthma is determined by their personal salience, and the necessity of acquiring strategies to resolve asthma-related difficulties, (iii) concordance in the beliefs of parents and their child about the child’s asthma is associated with less conflict about the child’s disease and disease-related situations, and the quality of family life mediates the relationship between belief concordance and the child’s psychological well-being, and (iv) participants’ reasons for achieving good control of asthma reflect the aspects of their lives that are most affected by asthma. It was concluded that the personal salience of different aspects of childhood asthma may encourage an awareness of symptoms, prompt discussion of internal states, foster concordance in beliefs, and motivate adherence with treatment recommendations

The use of classification and regression trees to predict the likelihood of seasonal influenza

Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therap

Ariel - Volume 9 Number 3

Executive Editor Emily Wofford Business Manager Fredric Jay Matlin University News John Patrick Welch World News George Robert Coar Editorials Editor Steve Levine Features Mark Rubin Brad Feldstein Photo Rick Spaide Circulation Victor Onufreiczuk Lee Wugofski Graphics and Art Steve Hulkower Commons Editor Brenda Peterso

Baker Center Journal of Applied Public Policy - Vol. IV, No. I

This is the 4th volume of the Baker Center Journal on Applied Public Policy. It includes articles on nuclear nonproliferation, American political development, election issues, Tennessee state trial courts, attitudes related to rich and poor people, and two student articles on science, innovation, technology and economic growth and explosive trace detection at airports

Design of MARQUIS2: study protocol for a mentored implementation study of an evidence-based toolkit to improve patient safety through medication reconciliation.

BackgroundThe first Multi-center Medication Reconciliation Quality Improvement Study (MARQUIS1) demonstrated that implementation of a medication reconciliation best practices toolkit decreased total unintentional medication discrepancies in five hospitals. We sought to implement the MARQUIS toolkit in more diverse hospitals, incorporating lessons learned from MARQUIS1.MethodsMARQUIS2 is a pragmatic, mentored implementation QI study which collected clinical and implementation outcomes. Sites implemented a revised toolkit, which included interventions from these domains: 1) best possible medication history (BPMH)-taking; 2) discharge medication reconciliation and patient/caregiver counseling; 3) identifying and defining clinician roles and responsibilities; 4) risk stratification; 5) health information technology improvements; 6) improved access to medication sources; 7) identification and correction of real-time discrepancies; and, 8) stakeholder engagement. Eight hospitalists mentored the sites via one site visit and monthly phone calls over the 18-month intervention period. Each site's local QI team assessed opportunities to improve, implemented at least one of the 17 toolkit components, and accessed a variety of resources (e.g. implementation manual, webinars, and workshops). Outcomes to be assessed will include unintentional medication discrepancies per patient.DiscussionA mentored multi-center medication reconciliation QI initiative using a best practices toolkit was successfully implemented across 18 medical centers. The 18 participating sites varied in size, teaching status, location, and electronic health record (EHR) platform. We introduce barriers to implementation and lessons learned from MARQUIS1, such as the importance of utilizing dedicated, trained medication history takers, simple EHR solutions, clarifying roles and responsibilities, and the input of patients and families when improving medication reconciliation

Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia.

In acute myeloid leukaemia (AML), the cell of origin, nature and biological consequences of initiating lesions, and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukaemic phase. Here, highly purified haematopoietic stem cells (HSCs), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3A mutations (DNMT3A(mut)) at high allele frequency, but without coincident NPM1 mutations (NPM1c) present in AML blasts. DNMT3A(mut)-bearing HSCs showed a multilineage repopulation advantage over non-mutated HSCs in xenografts, establishing their identity as pre-leukaemic HSCs. Pre-leukaemic HSCs were found in remission samples, indicating that they survive chemotherapy. Therefore DNMT3A(mut) arises early in AML evolution, probably in HSCs, leading to a clonally expanded pool of pre-leukaemic HSCs from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukaemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance

The central limit problem for random vectors with symmetries

Motivated by the central limit problem for convex bodies, we study normal approximation of linear functionals of high-dimensional random vectors with various types of symmetries. In particular, we obtain results for distributions which are coordinatewise symmetric, uniform in a regular simplex, or spherically symmetric. Our proofs are based on Stein's method of exchangeable pairs; as far as we know, this approach has not previously been used in convex geometry and we give a brief introduction to the classical method. The spherically symmetric case is treated by a variation of Stein's method which is adapted for continuous symmetries.Comment: AMS-LaTeX, uses xy-pic, 23 pages; v3: added new corollary to Theorem

Predictive response-relevant clustering of expression data provides insights into disease processes

This article describes and illustrates a novel method of microarray data analysis that couples model-based clustering and binary classification to form clusters of ;response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response. Predictions are subsequently made using an appropriate statistical summary of each gene cluster, which we call the ;meta-covariate' representation of the cluster, in a probit regression model. We first illustrate this method by analysing a leukaemia expression dataset, before focusing closely on the meta-covariate analysis of a renal gene expression dataset in a rat model of salt-sensitive hypertension. We explore the biological insights provided by our analysis of these data. In particular, we identify a highly influential cluster of 13 genes-including three transcription factors (Arntl, Bhlhe41 and Npas2)-that is implicated as being protective against hypertension in response to increased dietary sodium. Functional and canonical pathway analysis of this cluster using Ingenuity Pathway Analysis implicated transcriptional activation and circadian rhythm signalling, respectively. Although we illustrate our method using only expression data, the method is applicable to any high-dimensional datasets