Optimizing PharmacoTherapy In the Multimorbid Elderly in Primary CAre: a Cluster Randomized Controlled Trial (the OPTICA Trial)

Brief Summary

Detailed Description

Background: Policy makers face many challenges, when they plan health care systems to meet the needs of the fast growing elderly population. More than 60% of the elderly suffer from multiple chronic conditions (multimorbidity - commonly defined as ≥3 chronic diseases) and get multiple drugs (polypharmacy - commonly defined as ≥5 regular medications). Multimorbid patients are often excluded from randomized controlled trials (RCT) and consequently most prescribing guidelines address diseases in isolation. Consequently, there is inappropriate prescribing, which results in diminished health states and lower quality of life of the patients. Older patients usually rely on their general practitioners (GPs) to manage their medications. However, GPs often have limited time to adapt polypharmacy. Reviewing medications and understanding their interactions based on a list of diagnoses and drugs is complex and time consuming. Furthermore, due to the increase of patients with multimorbidity and polypharmacy, medication review especially in patients with many drugs is often neglected. Pilot data from the Netherlands showed that a software-assisted method, called STRIPA, was effective for optimizing pharmacotherapy in primary care. Through the OPTICA trial this tool will now be tested in the Swiss primary care context. Study design: The OPTICA trial is a single-site cluster randomized controlled trial (RCT), which will be conducted at the Institute for Primary Health Care of the University of Bern (BIHAM). All study-related tasks will be performed centrally at the BIHAM except for patient recruitment and use of the STRIPA, as these two tasks will be performed in participating GP offices. The GPs in the intervention group use the STRIP assistant, whereas the GPs in the control group conduct a sham intervention: usual medication review and shared-decision making with patient. STRIPA is designed to optimize drug therapy and to advise on safe and appropriate therapy using STOPP/START criteria (STOPP = 'screening tool of older people's prescriptions'; START = 'screening tool to alert to right treatment'). Patients will be followed for 1 year with follow-up phone calls after 6 and 12 months. Study objectives: The primary objective of this study is to assess the effect of pharmacotherapy optimization through STRIPA on the medication appropriateness, which is measured by the medication appropriateness index (MAI) for drug overuse and by the assessment of underutilization (AOU) for drug underuse. The secondary objective of the OPTICA study is to assess the impact of pharmacotherapy optimization by STRIPA on the parameters listed below (1- 4) as well as to examine the use of the STRIP assistant by GPs (5) and to examine patients' willingness to deprescribe (6): 1. Degree of polypharmacy 2. Degree of over- and underprescribing 3. Number of falls and fractures 4. Quality of life, including pain/discomfort 5. Health economic parameters, including direct costs of the intervention and incremental cost-effectiveness 6. Enablers and barriers faced by GPs when using the STRIP assistant 7. Patients' willingness to deprescribe Statistical considerations: 40 clusters with a cluster size of 8-10 patients will be included. 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. There will be two co-primary outcomes, the improvement in the MAI score at 12 months, defined as decrease of the score of at least one point, and improvement in the AOU index at 12 months, defined as a reduction of at least one prescribing omission. Both outcomes will be tested separately and success claimed if either test is significant. Adjustment for multiple testing will be performed. In the primary analysis, the investigators will assess outcomes at the patient level, accounting for the correlated nature of data among GPs by using multilevel mixed-effects models. For the co-primary outcomes, the investigators will present and compare the proportion of patients with improvement of the MAI score and AOU index in the control and intervention groups. The relative difference between groups will be assessed using a mixed-effects logistic model with a random intercept at the GP level in order to account for clustering. Secondary binary outcomes will be evaluated like the primary outcomes. Secondary continuous outcomes will be analyzed using random-effects linear regression with a random intercept at the GP level. Models will additionally be adjusted for the baseline value as a covariate. Secondary count outcomes will be analyzed using random-effects Poisson regression with a random intercept at the GP level. Health economic analyses will comprise the assessment i) of resource use and cost differences between the trial arms, ii) the assessment of differences in quality-adjusted lifetime between the trial arms (expressed as quality-adjusted life years \[QALYs\]), and iii) the assessment of the cost-effectiveness of the intervention in comparison with the comparator, i.e. medication review in accordance with standard care.

Primary Outcomes

Secondary Outcomes

• Patients' Quality-adjusted life years (QALYs)(12 months)
• Cost-effectiveness of the STRIPA intervention(12 months)
• Patients' degree of polypharmacy(12 months)
• Patients' quality of life measured by 5-level version of the European Quality of Life-5 Dimensions questionnaire (EQ-5D), including pain/discomfort.(12 months)
• Amount of informal care received by patients(12 months)
• Survival(12 months)
• Patients' degree of underprescribing, as measured by the Assessment of Underutilization(12 months)
• Patients' falls and fractures(12 months)
• Amount of formal care received by patients(12 months)
• Percentage of recommendations accepted by general practitioners (GPs)(12 months)
• Patient's willingness to deprescribe(Baseline)
• Patients' medical costs(12 months)
• Patients' degree of overprescribing, as measured by the Medication Appropriateness Index (MAI)(12 months)
• Percentage of recommendations rejected by general practitioners (GPs)(12 months)