Background: Change of first-line treatment of uncomplicated malaria to artemisinin-combination therapy (ACT) is widespread in Africa. To expand knowledge of safety profiles of ACT, pharmacovigilance activities are included in the implementation process of therapy changes. Ghana implemented first-line therapy of artesunate-amodiaquine in 2005. Drug utilization data is an important component of determining drug safety, and this paper describes how anti-malarials were prescribed within a prospective pharmacovigilance study in Ghana following anti-malarial treatment policy change.
PurposeAn important element of risk management is the planning and implementation of risk minimisation measures (RMMs) and the evaluation of their effectiveness by process or outcome indicators. The aim of this review is to summarize the characteristics of risk minimisation (RM) effectiveness studies in Europe and provide an overview of RMMs and their effectiveness.MethodsThis was a qualitative review of RM effectiveness studies in the European Union electronic Register of Post‐Authorization Studies (EU PAS Register); data extracted included study design, population, sample size, data sources, drug information, RMMs, study period, indicators, and their reported effectiveness.ResultsOf the 872 records in the EU PAS Register, 19 studies evaluating the effectiveness of RMMs were included. Eleven were cross‐sectional surveys and 8 used secondary data sources. Eighty‐nine percent (17/19) evaluated additional RMMs (used when routine RMMs are considered insufficient), and 36% (7/19) evaluated changes in routine RMMs (applicable to all medicinal products). A total of 42 effectiveness indicators were identified: 18 process and 24 outcomes. Half of the indicators (21/42) were successful; 2% (1/42) indicators were partially successful; 17% (7/42) indicators were inconclusive. Effectiveness of the remaining 31% (13/42) indicators could not be determined owing to limited information. The United Kingdom was the most frequent country for the conduct of RM effectiveness studies.ConclusionsMost of the included studies evaluated additional RMMs. Half of the effectiveness indicators (process and/or outcome) were reported as successful. This review provides evidence to support the development of future guidance on the effectiveness of RM in Europe.
Background: The aim of this study was to identify characteristics with independent predictive value for bowel cancer for use in the clinical assessment of patients attending colorectal outpatient clinics.Methods: This was a 22-year (1986-2007) retrospective cohort analysis of data collected prospectively from patients who attended colorectal surgical outpatient clinics in Portsmouth. The data set was split randomly into two groups of patients to generate and validate a predictive model. Multivariable logistic regression was used to create and validate a system to predict outcome. Receiver operating characteristic (ROC) curves and Hosmer-Lemeshow test were used to evaluate the model's predictive capability. The likelihood of bowel cancer was expressed as the odds ratio (OR).
Conclusion:A clinical assessment that systematically identifies or excludes four symptom-age combinations, a mass and IDA (SAMI) stratifies patients as having a low and higher risk of having bowel cancer. This could improve patient selection for referral and investigation.
Chronic obstructive pulmonary disease (COPD) has a rising global incidence and acute exacerbation of COPD (AECOPD) carries a high health-care economic burden. Classification and regression tree (CART) analysis is able to create decision trees to classify risk groups. We analysed routinely collected laboratory data to identify prognostic factors for inpatient mortality with AECOPD from our large district hospital. Data from 5,985 patients with 9,915 admissions for AECOPD over a 7-year period were examined. Randomly allocated training (n = 4,986) or validation (n = 4,929) data sets were developed and CART analysis was used to model the risk of all-cause death during admission. Inpatient mortality was 15.5%, mean age was 71.5 (±11.5) years, 56.2% were male, and mean length of stay was 9.2 (±12.2) days. Of 29 variables used, CART analysis identified three (serum albumin, urea, and arterial pCO(2)) to predict in-hospital mortality in five risk groups, with mortality ranging from 3.0 to 23.4%. C statistic indices were 0.734 and 0.701 on the training and validation sets, respectively, indicating good model performance. The highest-risk group (23.4% mortality) had serum urea >7.35 mmol/l, arterial pCO(2) >6.45 kPa, and normal serum albumin (>36.5 g/l). It is possible to develop clinically useful risk prediction models for mortality using laboratory data from the first 24 h of admission in AECOPD.
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