Adverse Drug Events in Ageing Populations (ADDRESS-AP)

Better understanding the link between medications and adverse drug events in older people

Medications

Aims

To better understand which patients are at a higher risk of being harmed by taking lots of medications
To examine which medications are more likely to cause adverse drug events
To explore whether stopping these medications can reduce the harm

Why is this important?

Many older people have several health conditions and can be prescribed many different medications. Taking too many medications can increase the chance of side effects, such as suddenly feeling confused (called delirium) and falling over. Delirium can happen suddenly, over one to two days, resulting in confusion and distress to the person and those around them, especially when they don’t know what’s happening. Falls are very common in older people, causing distress and injury, loss of confidence, independence and sometimes even death. As a result, the patients often rate these outcomes as the most important medication related harms they would want to avoid.

Medication related harm is the cause of 1 in 10 hospital admissions. At the moment, doctors do not know which patients are most likely to experience these harms or how best to prevent them. One suggestion is to stop prescribing medications which might cause harm, but this approach has not been tested in clinical trials and so doctors do not know what will happen if they try it in the real world.

Methods

This project will use information from millions of patient’s GP records to see if there is a link between medications and harms such as delirium and falls. These records are provided by the Clinical Practice Research Datalink (CPRD) and will be ‘anonymised,’ which means that the research team will not know who they belong to.

In the first part of this project, we will use this information to create a tool which can predict who is most likely to experience adverse drug events such as delirium and falls in the future. We will also examine whether there is a link between commonly prescribed medications and these harms. To do this, we will use statistical methods and artificial intelligence to help find new connections between medications and harms that haven't been explored before.

In the final part of this project, we will look at what happens when people stop taking their medications. We will study a group of people who have had a medication review with their doctor or pharmacist and compare those who had a medication stopped with those who did not. We will look at whether those who stopped medications experienced more or less adverse drug events as a result of stopping medication.

How will this benefit patients?

This work will lead to the development of new clinical decision support tools for both patients and doctors to use in shared decision making about whether continue or stop taking medications which may do them more harm than good.