Development of a Complex Intervention for Secondary Prevention of Coronary Heart Disease in Primary Care Using the UK Medical Research Council Framework

Objective: To apply the UK Medical Research Council (MRC) framework for development and evaluation of trials of complex interventions to a primary healthcare intervention to promote secondary prevention of coronary heart disease. Study Design: Case report of intervention development. Methods: First, literature relating to secondary prevention and lifestyle change was reviewed. Second, a preliminary intervention was modeled, based on literature findings and focus group interviews with patients (n = 23) and staff (n = 29) from 4 general practices. Participants’ experiences of and attitudes toward key intervention components were explored. Third, the preliminary intervention was pilot-tested in 4 general practices. After delivery of the pilot intervention, practitioners evaluated the training sessions, and qualitative data relating to experiences of the intervention were collected using semistructured interviews with staff (n = 10) and patient focus groups (n = 17). Results: Literature review identified 3 intervention components: a structured recall system, practitioner training, and patient information. Initial qualitative data identified variations in recall system design, training requirements (medication prescribing, facilitating behavior change), and information appropriate to the prospective study participants. Identifying detailed structures within intervention components clarified how the intervention could be tailored to individual practice, practitioner, and patient needs while preserving the theoretical functions of the components. Findings from the pilot phase informed further modeling of the intervention, reducing administrative time, increasing practical content of training, and omitting unhelpful patient information. Conclusion: Application of the MRC framework helped to determine the feasibility and development of a complex intervention for primary care research.

Characterization and optimization of experimental variables within a reproducible bladder encrustation model and in vitro evaluation of the efficacy of urease inhibitors for the prevention of medical device-related encrustation

This study presents a reproducible, cost-effective in vitro encrustation model and, furthermore, describes the effects of components of the artificial urine and the presence of agents that modify the action of urease on encrustation on commercially available ureteral stents. The encrustation model involved the use of small-volume reactors (700 mL) containing artificial urine and employing an orbital incubator (at 37 degrees C) to ensure controlled stirring. The artificial urine contained sources of calcium and magnesium (both as chlorides), albumin and urease. Alteration of the ratio (% w/w) of calcium salt to magnesium salt affected the mass of encrustation, with the greatest encrustation noted whenever magnesium was excluded from the artificial urine. Increasing the concentration of albumin, designed to mimic the presence of protein in urine, significantly decreased the mass of both calcium and magnesium encrustation until a plateau was observed. Finally, exclusion of urease from the artificial urine significantly reduced encrustation due to the indirect effects of this enzyme on pH. Inclusion of the urease inhibitor, acetohydroxamic acid, or urease substrates (methylurea or ethylurea) into the artificial medium markedly reduced encrustation on ureteral stents. In conclusion, this study has described the design of a reproducible, cost-effective in vitro encrustation model. Encrustation was markedly reduced on biomaterials by the inclusion of agents that modify the action of urease. These agents may, therefore, offer a novel clinical approach to the control of encrustation on urological medical devices. (c) 2005 Wiley Periodicals, Inc.

Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.