Objectives To identify factors that predict repeat admission to hospital for adverse drug reactions (ADRs) in older adults.Design Population based retrospective cohort study.Setting All public and private hospitals in Western Australia.Participants 28 548 patients aged ≥60 years with an admission for an ADR during 1980-2000 followed for three years using the Western Australian data linkage system.Results 5056 (17.7%) patients had a repeat admission for an ADR. Repeat ADRs were associated with sex (hazard ratio 1.08, 95% confidence interval 1.02 to 1.15, for men), first admission in 1995-9 (2.34, 2.00 to 2.73), length of hospital stay (1.11, 1.05 to 1.18, for stays ≥14 days), and Charlson comorbidity index (1.71, 1.46 to 1.99, for score ≥7); 60% of comorbidities were recorded and taken into account in analysis. In contrast, advancing age had no effect on repeat ADRs. Comorbid congestive cardiac failure (1.56, 1.43 to 1.71), peripheral vascular disease (1.27, 1.09 to 1.48), chronic pulmonary disease (1.61, 1.45 to 1.79), rheumatological disease (1.65, 1.41 to 1.92), mild liver disease (1.48, 1.05 to 2.07), moderate to severe liver disease (1.85, 1.18 to 2.92), moderate diabetes (1.18, 1.07 to 1.30), diabetes with chronic complications (1.91, 1.65 to 2.22), renal disease (1.93, 1.71 to 2.17), any malignancy including lymphoma and leukaemia (1.87, 1.68 to 2.09), and metastatic solid tumours (2.25, 1.92 to 2.64) were strong predictive factors. Comorbidities requiring continuing care predicted a reduced likelihood of repeat hospital admissions for ADRs (cerebrovascular disease 0.85, 0.73 to 0.98; dementia 0.62, 0.49 to 0.78; paraplegia 0.73, 0.59 to 0.89).Conclusions Comorbidity, but not advancing age, predicts repeat admission for ADRs in older adults, especially those with comorbidities often managed in the community. Awareness of these predictors can help clinicians to identify which older adults are at greater risk of admission for ADRs and, therefore, who might benefit from closer monitoring.

Over the past 2 decades, e-learning has evolved as a new pedagogy within pharmacy education. As learners and teachers increasingly seek e-learning opportunities for an array of educational and individual benefits, it is important to evaluate the effectiveness of these programs. This systematic review of the literature examines the quality of e-learning effectiveness studies in pharmacy, describes effectiveness measures, and synthesizes the evidence for each measure. E-learning in pharmacy education effectively increases knowledge and is a highly acceptable instructional format for pharmacists and pharmacy students. However, there is limited evidence that e-learning effectively improves skills or professional practice. There is also no evidence that e-learning is effective at increasing knowledge long term; thus, long-term follow-up studies are required. Translational research is also needed to evaluate the benefits of e-learning at patient and organizational levels.

Background—: Survivors of nonfatal coronary heart disease (CHD) can reduce their risk of further events by various preventive interventions. The impact of such measures as delivered over 11 years, on population rates of subsequent major CHD events, has not been extensively studied. This study determined population trends in the prevalence of clinically manifest CHD and the proportion of major CHD events that occur in this population. Methods and Results—: A population longitudinal person-based event-linked file of CHD extracted from State Hospital Morbidity Data and Death Registry for 1980 to 2005 was used to identify, for each year from 1995 to 2005, survivors who had a hospitalization for CHD over the previous 15 years (population with established CHD), and to examine the occurrence of CHD death and hospitalization with a principal diagnosis of myocardial infarction in both populations with and without established CHD. The average annual age-standardized prevalence of CHD in the Perth metropolitan region (population 1.6 million) was 28 373 (8.8%) in men and 14 966 (4.0%) in women. Age-specific prevalence increased exponentially with age, from <1% in 35 to 39 age group to 42% in 80 to 84 age group in men and half that in women. The percentage of total CHD events (n=28 941) that occurred in the population with established CHD was approximately 43% in both men and women, 55% and 51%, respectively, for CHD death and 35% and 36% for nonfatal myocardial infarction. Conclusions—: More than 40% of major CHD events annually occur in persons with manifest disease, highlighting the imperative to implement systems of care that support effective secondary prevention.

Aims: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models. Methods and results: We identified all Western Australian patients aged above 65 years admitted for HF between 2003 and 2008 in the linked Hospital Morbidity Data Collection. Taking into consideration the class imbalance problem, we developed a multi-layer perceptron (MLP)-based approach to predict 30 day HF readmission or death and compared the predictive performances using the performance metrics, that is, area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity and specificity with other ML and regression models. Out of the 10 757 patients with HF, 23.6% were readmitted or died within 30 days of hospital discharge. We observed an AUC of 0.55, 0.53, 0.58, and 0.54 while an AUPRC of 0.39, 0.38, 0.46, and 0.38 for weighted random forest, weighted decision trees, logistic regression, and weighted support vector machines models, respectively. The MLP-based approach produced the highest AUC (0.62) and AUPRC (0.46) with 48% sensitivity and 70% specificity. Conclusions: We show that for the medical data with class imbalance, the proposed MLP-based approach is superior to other ML and regression techniques for the prediction of 30 day HF readmission or death.