OPERAM: OPtimising thERapy to Prevent Avoidable Hospital Admissions in the Multimorbid Older People: a Cluster Randomised Controlled Trial
OPERAM: OPtimising thERapy to Prevent Avoidable Hospital Admissions in the Multimorbid Older People: a Cluster Randomised Controlled Trial
The objective of this Randomised Controlled Trial (RCT) is to evaluate whether the Systematic Tool to Reduce Inappropriate Prescribing (STRIP) including STRIP assistant (STRIPA) implemented by an appropriately qualified team will lead to an improvement in clinical and economic outcomes among patients aged 70 years and more with multimorbidity and polypharmacy.
Background:
Drug-related morbidity and mortality is an increasing problem in European healthcare systems. Multimorbidity, polypharmacy and old age are important risk factors for drug-related hospital admissions (DRA). The reported incidence of DRAs in the elderly may be as high as 30% of all acute cases, and about half of DRAs are likely to be preventable. They are mainly related to prescribing problems and non-compliance with drug regimens. A significant proportion of healthcare costs are spent on unnecessary interventions and inappropriate medications. The Systematic Tool to Reduce Inappropriate Prescribing (STRIP) is a structured method to perform a medication review to optimise pharmacotherapy.
Design:
European multi-centre, cluster randomised, controlled trial of people aged 70 years or older, with multimorbidity and polypharmacy, being on an ambulatory visit or on a hospital stay in one of the four participating centres in Ireland, Belgium, Switzerland and the Netherlands. A cluster is defined around a treating physician, i.e. the treating physician is randomised and defines the allocation of his patients. Clusters of patients will be randomised to the intervention arm receiving STRIP for optimising therapy or to the control arm undergoing usual clinical care. The patients of physicians who are allocated to the intervention group will undergo a systematic drug review and pharmacotherapy optimisation by a physician and a pharmacist using STRIP, including the STRIPA software. That provides the research team with a recommendation of changes in the patient's medication. Based on STRIPA recommendation and agreement on changes to the patients' pharmacotherapy between the team of the research physician and pharmacist and the prescribing physician, will the patient receive structured counselling about his/her medication; general practitioners will receive a report. Patients will be further followed for 1 year with follow-up phone calls after 2, 6 and 12 months. For the purpose of this trial, all hospitalisations during follow-up of participants will be adjudicated to assess their relationship to adverse drug events.
Objectives:
The primary objective is to assess the effect of a structured medication review and pharmacotherapy optimisation using the STRIP on drug-related hospitalisations (DRA) caused by over-, mis-, and underuse or over-, mis-, and underprescribing of medications.
Secondary objectives will be to assess the impact of pharmacotherapy optimisation on falls, quality of life, polypharmacy, medication changes, activities of daily living, and mortality.
Statistical considerations:
80 clusters with a cluster size ranging from 12 to 38 participants will be included. Therefore, 2000 patients, 1000 patients in each arm, will be recruited over 18 months. The trial will have 80% power with this sample size.
The primary analysis will be an intention-to-treat (ITT) analysis, whereby all randomised patients will be included in the group they were allocated to.
The primary outcome of drug-related admission will be analysed using a random-effects competing risk proportional hazards model that accounts for the competing risk of death and for clustering of data within centre and prescribing physician.
Overall survival will be analysed using a random-effects Cox proportional hazards model that accounts for clustering of data within centre and prescribing physician. The analysis of falls will also take into account the competing risk of death. Continuous outcomes will be analysed by random-effects linear regression. All effect measures will be accompanied by 95% confidence intervals and all p-values will be two-sided.
Inclusion Criteria:
Exclusion Criteria: