Ongoing CRIS projects: mood disorders
A feasibility study on an automated identification of potential patients with treatment resistant depression (TRD) for studies being run in Oxford.
TRD is common and it is disabling. There is relatively little certainty about how it should best be treated. As a result, there has been an increased focus recently on trials which assess novel interventions and which explore biological mechanistic processes related to TRD. These trials, many of which are run in Oxford, require patients with TRD to be identified and, where appropriate, invited to enrol in the studies. It may be possible to facilitate this process by searching Electronic Health Records (EHR) HER databases such as the Clinical Records Information System (CRIS) system. In the first instance, the feasibility of this approach depends on being able to identify, using available search mechanisms, patients who are likely to be suitable for running studies. This study will assess this using de-identified data.
The study aims to iteratively arrive at an effective search strategy—iterations will involve running a search on patients, identifying patients found by the search who are unlikely to be suitable for enrolment in studies and using this information to improve the next generation of search. A search strategy will be developed and will be used to identify potential study patients, based on treatment (antidepressant) names mentioned in the EHR and ongoing contact with clinical teams. The suggested cohort of potentially suitable patients will be assessed for suitability (or not) for trial enrolment by trial clinicians. The trial clinicians will narratively describe the barriers to likely recruitment in identified patients and will discuss these with the information technician to assess whether it may be possible to improve the search strategy. This process will continue until either no improvement in search occurs or an acceptable hit rate (i.e. % of identified patients likely to be reasonable candidates for ongoing studies) is obtained.
Prevalence of Treatment Resistant Depression (TRD) in Child and Adolescent Mental Health Services (CAMHS) for 11-18 year olds
Depression in adolescence is relatively common and, unfortunately, in certain cases difficult to treat with either medication, such as antidepressants, or psychological treatments such as counselling and CBT. We are unclear how often treatment fails in the first instance and resistance occurs. It is important to know this, so that we can understand how big the problem is and, importantly, look at ways of improving our treatments for depression in this age group.
Difficult to Treat Depression
This external study request, aims to identify the prevalence of difficult to treat depression (DTD) across the UK, understand a range of metrics on healthcare utilization (admissions, medication, treatments) and patient outcomes (suicidality, employment status). It will compare the identified DTD cohort to the larger non-DTD cohort (i.e. patients who haven’t failed treatment but have been diagnosed with depression) to understand the excess cost of DTD depression and identify other variables (aside failed treatments) that could be used to identify DTD individuals. This study will be used to inform commissioners about the current burden of disease and access to further treatment options.
Conducting a study of people with depression who appear not to be getting better on a single medication
In clinical trials of treatments for people with mental illness, a group of patients are given a treatment (called the “intervention arm” of the trial) and compared to another different group who are given an inactive or placebo (the “control arm”). At the end of the trial the patients in both arms are compared to see if the treatment made a difference to their symptoms or overall quality of life. Importantly, the comparison allows side effects and adverse events to be monitored thus establishing the safety of the treatment. Participating in clinical trials can be burdensome for patients and usually there are no immediate treatment benefits for volunteering. Results from controlled clinical trials are often criticized because the patients enrolled are not sufficiently representative of the patients who might receive the treatment in the future. Finally, existing trials can be inefficient because often, for every patient in the intervention arm, many more patients are required in the control arm to make statistically valid comparisons.
Consequently, researchers have sought ways to make trials more efficient and at the same time make results more applicable to “real world” clinical practice. One approach is to conduct a traditional clinical trial, but at the same time, enroll a group of patients who won’t be given the experimental treatment. These patients will continue their existing treatment in the usual way they would in the NHS and these participants form what is known as an external control arm (ECA). At the end of the trial researchers can then compare people in the treatment arm with those in the ECA to examine the frequency of side-effects, changes in symptoms and safety data for patients who had their usual treatment. For example, if researchers think a new medication might cause people to faint, they can see how many people fainted in the intervention arm and compare this statistically to those in the ECA. Additionally, many illnesses fluctuate over time so patients in the external control arm can add valuable information about the natural time course of symptoms and adverse events when looking for clinically meaningful differences when compared to participants in the treatment arm.
Oxford Health NHS Foundation Trust (OHFT) and the University of Oxford have partnered with Akrivia Health (an Oxford-based health technology company) to deliver an external control arm for a study sponsored by Janssen, a pharmaceutical company. Janssen are conducting a study of people with depression who appear not to be getting better on a single medication. We will approach patients who are treated by OHFT to see if they would consider being a participant in this external control arm. Participants will continue their care and treatment with their NHS team and participation in the study will not modify their current or any future treatment plans with their team. Participants will be asked to attend a series of regular interviews with the study team, over approximately one year and to have blood tests – as they would in a traditional clinical trial and these are in addition to their usual care or treatment plan.
In addition, each participant will be asked if their anonymised medical records can be examined at regular intervals using technology developed by Akrivia Health in collaboration with the team at OHFT and the University of Oxford. Through examination of these records it is hoped/anticipated that patterns of adverse events like falls, sleep problems or worsening of depression symptoms can be detected.
Only summary numerical data will be extracted from participant’s medical records (for example, the number of times the participant had outpatient appointments with their team; a list of current medication and doses; any descriptions of side-effects of treatment). Data that describes a person or is unique to them (e.g. their date of birth or NHS number) will not be extracted or disclosed to the sponsor (Janssen) and Akrivia Health will not be able to determine the identity of a patient or access the participant’s source medical notes. Similarly, any summary data that could – in combination – be used to identify the participant will not be provided to the sponsor; for example, very specific symptoms, the participants address or occupation.
The study’s sponsor (Janssen) will benefit by having data from the external control arm that describes how patients progress in terms of side-effects, adverse events and healthcare contact when they continue their normal NHS standard of care for the treatment of depression. The results of this study will enable researchers at OHFT and the University of Oxford to establish if external control arms are feasible, practical and add important information to traditional clinical trials that could bring about benefit for patients in the future.
Page last reviewed: 1 November, 2021