Competing risk and heterogeneity of treatment effect in clinical trials

It has been demonstrated that patients enrolled in clinical trials frequently have a large degree of variation in their baseline risk for the outcome of interest. Thus, some have suggested that clinical trial results should routinely be stratified by outcome risk using risk models, since the summary results may otherwise be misleading. However, variation in competing risk is another dimension of risk heterogeneity that may also underlie treatment effect heterogeneity. Understanding the effects of competing risk heterogeneity may be especially important for pragmatic comparative effectiveness trials, which seek to include traditionally excluded patients, such as the elderly or complex patients with multiple comorbidities. Indeed, the observed effect of an intervention is dependent on the ratio of outcome risk to competing risk, and these risks – which may or may not be correlated – may vary considerably in patients enrolled in a trial. Further, the effects of competing risk on treatment effect heterogeneity can be amplified by even a small degree of treatment related harm. Stratification of trial results along both the competing and the outcome risk dimensions may be necessary if pragmatic comparative effectiveness trials are to provide the clinically useful information their advocates intend

Medication administration errors for older people in long-term residential care

Background Older people in long-term residential care are at increased risk of medication errors. The purpose of this study was to evaluate a computerised barcode medication management system designed to improve drug administrations in residential and nursing homes, including comparison of error rates and staff awareness in both settings. Methods All medication administrations were recorded prospectively for 345 older residents in thirteen care homes during a 3-month period using the computerised system. Staff were surveyed to identify their awareness of administration errors prior to system introduction. Overall, 188,249 attempts to administer medication were analysed to determine the prevalence of potential medication administration errors (MAEs). Error classifications included attempts to administer medication at the wrong time, to the wrong person or discontinued medication. Analysis compared data at residential and nursing home level and care and nursing staff groups. Results Typically each resident was exposed to 206 medication administration episodes every month and received nine different drugs. Administration episodes were more numerous (p < 0.01) in nursing homes (226.7 per resident) than in residential homes (198.7). Prior to technology introduction, only 12% of staff administering drugs reported they were aware of administration errors being averted in their care home. Following technology introduction, 2,289 potential MAEs were recorded over three months. The most common MAE was attempting to give medication at the wrong time. On average each resident was exposed to 6.6 potential errors. In total, 90% of residents were exposed to at least one MAE with over half (52%) exposed to serious errors such as attempts to give medication to the wrong resident. MAEs rates were significantly lower (p < 0.01) in residential homes than nursing homes. The level of non-compliance with system alerts was low in both settings (0.075% of administrations) demonstrating virtually complete error avoidance. Conclusion Potentially inappropriate administration of medication is a serious problem in long-term residential care. A computerised barcode system can accurately and automatically detect inappropriate attempts to administer drugs to residents. This tool can reliably be used by care staff as well as nurses to improve quality of care and patient safety

Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability

Reducing Adverse Self-Medication Behaviors in Older Adults with Hypertension: Results of an e-health Clinical Efficacy Trial

A randomized controlled efficacy trial targeting older adults with hypertension (age 60 and over) provided an e-health, tailored intervention with the “next generation” of the Personal Education Program (PEP-NG). Eleven primary care practices with advanced practice registered nurse (APRN) providers participated. Participants (N = 160) were randomly assigned by the PEP-NG (accessed via a wireless touchscreen tablet computer) to either control (entailing data collection and four routine APRN visits) or tailored intervention (involving PEP-NG intervention and four focused APRN visits) group. Compared to patients in the control group, patients receiving the PEP-NG e-health intervention achieved significant increases in both self-medication knowledge and self-efficacy measures, with large effect sizes. Among patients not at BP targets upon entry to the study, therapy intensification in controls (increased antihypertensive dose and/or an additional antihypertensive) was significant (p = .001) with an odds ratio of 21.27 in the control compared to the intervention group. Among patients not at BP targets on visit 1, there was a significant declining linear trend in proportion of the intervention group taking NSAIDs 21–31 days/month (p = 0.008). Satisfaction with the PEP-NG and the APRN provider relationship was high in both groups. These results suggest that the PEP-NG e-health intervention in primary care practices is effective in increasing knowledge and self-efficacy, as well as improving behavior regarding adverse self-medication practices among older adults with hypertension

Automatic Filtering and Substantiation of Drug Safety Signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions

Drug-induced acute myocardial infarction: identifying 'prime suspects' from electronic healthcare records-based surveillance system.

BACKGROUND: Drug-related adverse events remain an important cause of morbidity and mortality and impose huge burden on healthcare costs. Routinely collected electronic healthcare data give a good snapshot of how drugs are being used in 'real-world' settings. OBJECTIVE: To describe a strategy that identifies potentially drug-induced acute myocardial infarction (AMI) from a large international healthcare data network. METHODS: Post-marketing safety surveillance was conducted in seven population-based healthcare databases in three countries (Denmark, Italy, and the Netherlands) using anonymised demographic, clinical, and prescription/dispensing data representing 21,171,291 individuals with 154,474,063 person-years of follow-up in the period 1996-2010. Primary care physicians' medical records and administrative claims containing reimbursements for filled prescriptions, laboratory tests, and hospitalisations were evaluated using a three-tier triage system of detection, filtering, and substantiation that generated a list of drugs potentially associated with AMI. Outcome of interest was statistically significant increased risk of AMI during drug exposure that has not been previously described in current literature and is biologically plausible. RESULTS: Overall, 163 drugs were identified to be associated with increased risk of AMI during preliminary screening. Of these, 124 drugs were eliminated after adjustment for possible bias and confounding. With subsequent application of criteria for novelty and biological plausibility, association with AMI remained for nine drugs ('prime suspects'): azithromycin; erythromycin; roxithromycin; metoclopramide; cisapride; domperidone; betamethasone; fluconazole; and megestrol acetate. LIMITATIONS: Although global health status, co-morbidities, and time-invariant factors were adjusted for, residual confounding cannot be ruled out. CONCLUSION: A strategy to identify potentially drug-induced AMI from electronic healthcare data has been proposed that takes into account not only statistical association, but also public health relevance, novelty, and biological plausibility. Although this strategy needs to be further evaluated using other healthcare data sources, the list of 'prime suspects' makes a good starting point for further clinical, laboratory, and epidemiologic investigation