Many medicines have long-term benefits and prevent illness. These preventative medicines are often stopped in the last few weeks of a person’s life, when it is clear they will die soon and not benefit from the medicines. However, the benefit of continuing to take preventative medicines over a longer period – the last year or two of life – may also be small, so it may be reasonable for patients to stop preventative medicines sooner.
However, we do not know which people with limited life expectancy should consider stopping preventative medicines. And we do not know what the consequences of stopping preventative medicines might be for patients.
This study will use existing scientific research about the benefits and risks of different preventative medicines. It will combine this with an existing method for estimating how long a person might live. We will use routine prescribing information from CPRD and apply statistical methods to work out what the effect of stopping these medicines might be for people with limited life expectancy.
Our research will provide us with important information that will help health professionals and patients make informed decisions about stopping preventative medicines with limited benefit.
Many medicines have long-term benefits, but patients with limited life expectancy may not live long enough to benefit fully. In such situations deprescribing may be appropriate. This happens in palliative care settings, but evidence is lacking about how deprescribing should happen in broader practice, and whether it can be done safely. In particular, quantifiable information about risks involved may help inform patient-centred decision making.
This study aims to produce algorithms that quantify the potential loss of therapeutic benefit and reduction in drug-related harm from deprescribing long-term preventative medicines in patients with limited life expectancy.
A cohort study will be conducted in CPRD GOLD, with four elements:
1. The QMortality algorithm will be independently validated using linked ONS mortality data, using QMortality to estimate 1- and 2-year mortality, and predictive ability assessed using ROC, C-statistics, D-statistics, calibration curves and overall fit. HES APC data will be an input for QMortality.
2. Baseline cumulative hazard will be estimated along with QMortality coefficients to estimate survival functions.
3. Baseline risk of major adverse cardiovascular events [MACE] and adverse drug events [ADE] will be estimated using existing risk models alongside QMortality to estimate likelihood of outcomes in a patient’s remaining life. MACE/ADE will be determined from HES and GOLD. Combining estimates of relative treatment effect (safety/efficacy) from the literature for three key drugs (antihypertensives, statins, anticoagulants) will enable determination of individualised benefit/harm estimates for each treatments in the context of limited life expectancy.
4. A descriptive epidemiological analysis will provide understanding of the prevalence and distribution of life expectancy and medication benefits/harms.
Resulting algorithms will underpin a clinical informatics toolkit to support personalised deprescribing in primary care. Findings will also provide insight into the scope for deprescribing and help identify the types of patients most likely to gain from deprescribing.
Major adverse cardiovascular events; Adverse drug events; Mortality
Rupert Payne - Chief Investigator - University of Exeter
Rupert Payne - Corresponding Applicant - University of Exeter
Gary Abel - Collaborator - University of Exeter
Jo Butterworth - Collaborator - University of Exeter
Nurunnahar Akter - Collaborator - University of Exeter
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation