The safety of glucocorticoids in patients with inflammatory musculoskeletal conditions
Lead Research Organisation:
University of Manchester
Department Name: Medical and Human Sciences
Abstract
Glucocorticoids, also known as steroids, are drugs commonly used for the treatment of rheumatoid arthritis and other inflammatory conditions. Steroids are a good treatment because they quickly ease joint pain, swelling and stiffness, thus improving disability. However, as with most medication, there are potential side effects.
When starting medication, most patients want to know their chance of developing side effects. However, we don‘t know accurately how commonly side effects occur, what dose of steroid leads to side effects, or what sort of person is particularly at risk. The chance of side effects probably varies according to many factors, including how old a patient is, their gender, whether they have other diseases, or are taking other medication. This research aims to answer these questions, and to provide a likelihood of side effects tailored to the individual.
Only patients themselves know whether they take their medicines, at what dose, or whether they develop side effects. In order to improve risk measurement, we need to collect such information directly from patients. Then, having measured risk, we need to improve the way we communicate this information to patients. This project aims to develop electronic systems to advance this two-way communication.
When starting medication, most patients want to know their chance of developing side effects. However, we don‘t know accurately how commonly side effects occur, what dose of steroid leads to side effects, or what sort of person is particularly at risk. The chance of side effects probably varies according to many factors, including how old a patient is, their gender, whether they have other diseases, or are taking other medication. This research aims to answer these questions, and to provide a likelihood of side effects tailored to the individual.
Only patients themselves know whether they take their medicines, at what dose, or whether they develop side effects. In order to improve risk measurement, we need to collect such information directly from patients. Then, having measured risk, we need to improve the way we communicate this information to patients. This project aims to develop electronic systems to advance this two-way communication.
Technical Summary
Aim
To quantify the risk of single and multiple adverse events in patients with inflammatory musculoskeletal conditions treated with glucocorticoid (GC) therapy.
Objectives
To explore the association between time-varying GC dosage and route of administration of GC and outcome.
To develop and validate a predictive tool to estimate likelihood of GC-associated adverse events.
To address the influence of adherence upon risk estimates.
To better understand the benefit/ harm balance of GC use.
To improve risk communication through development of an electronic visual tool
Design and Methodology
The planned analyses will use databases that are established or in development. First, nested case-control and cohort studies, with modelling of time-varying GC exposure including dose, will be undertaken using respected UK primary care databases: the General Practice Research Database and The Health Improvement Network. Prediction modelling will be used to identify patients at risk of GC-associated adverse events (including infection, cardiovascular disease, diabetes, fractures), in one dataset with validation in the second dataset. In order to address the influence of factors captured only in secondary care, such as parenteral GC therapy and disease severity measures, further analysis will be undertaken in two additional databases under development, the arc Early Inflammatory Arthritis Information System and the NorthWest e-Health e-Labs project. Additional funding will be sought to develop and implement novel technology to capture patient reported outcome measures and adherence data, and to develop an electronic visual tool for risk communication.
Scientific and medical opportunities
It is not clear which GC treatment regimes place patients at risk of adverse events, what degree of risk is conferred, or what predisposes patients to such events. This makes accurate communication of risk difficult. The work described above will unpick some of the complexities of GC-associated adverse events. It will also move risk estimation towards personalised risk assessment, whereby patients can be informed of their individual risk of side effects given their profile, including demographics, disease characteristics and co-morbidity.
Pharmacoepidemiology is currently limited by the scope of available datasets. These projects will improve capture of clinical data, both from clinicians and directly from patients. Although focussed on GC safety, the skills, techniques and visual tools developed in these projects will have wider application for investigating the safety of other medications.
To quantify the risk of single and multiple adverse events in patients with inflammatory musculoskeletal conditions treated with glucocorticoid (GC) therapy.
Objectives
To explore the association between time-varying GC dosage and route of administration of GC and outcome.
To develop and validate a predictive tool to estimate likelihood of GC-associated adverse events.
To address the influence of adherence upon risk estimates.
To better understand the benefit/ harm balance of GC use.
To improve risk communication through development of an electronic visual tool
Design and Methodology
The planned analyses will use databases that are established or in development. First, nested case-control and cohort studies, with modelling of time-varying GC exposure including dose, will be undertaken using respected UK primary care databases: the General Practice Research Database and The Health Improvement Network. Prediction modelling will be used to identify patients at risk of GC-associated adverse events (including infection, cardiovascular disease, diabetes, fractures), in one dataset with validation in the second dataset. In order to address the influence of factors captured only in secondary care, such as parenteral GC therapy and disease severity measures, further analysis will be undertaken in two additional databases under development, the arc Early Inflammatory Arthritis Information System and the NorthWest e-Health e-Labs project. Additional funding will be sought to develop and implement novel technology to capture patient reported outcome measures and adherence data, and to develop an electronic visual tool for risk communication.
Scientific and medical opportunities
It is not clear which GC treatment regimes place patients at risk of adverse events, what degree of risk is conferred, or what predisposes patients to such events. This makes accurate communication of risk difficult. The work described above will unpick some of the complexities of GC-associated adverse events. It will also move risk estimation towards personalised risk assessment, whereby patients can be informed of their individual risk of side effects given their profile, including demographics, disease characteristics and co-morbidity.
Pharmacoepidemiology is currently limited by the scope of available datasets. These projects will improve capture of clinical data, both from clinicians and directly from patients. Although focussed on GC safety, the skills, techniques and visual tools developed in these projects will have wider application for investigating the safety of other medications.
