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. Author manuscript; available in PMC: 2025 Sep 23.
Published in final edited form as: Prim Care Diabetes. 2024 Jan 13;18(2):132–137. doi: 10.1016/j.pcd.2023.12.001

Deintensification of potentially inappropriate medications amongst older frail people with type 2 diabetes: Protocol for a cluster randomised controlled trial (D-MED study)☆

Lauren O’Mahoney a,b, Patrick Highton a,b,*, Ruksar Abdala a,b, Helen Dallosso c, Clare L Gillies d, Seema Ragha d, Fiona Munday a, John Robinson e, Andrew Marshall e, James P Sheppard f, Kamlesh Khunti a,b, Samuel Seidu a
PMCID: PMC7618157  EMSID: EMS208800  PMID: 38220558

Abstract

Aims

Amongst elderly people with type 2 diabetes (T2D) over prescribing can result in emergency ambulance call-outs, falls and fractures and increased mortality, particularly in frail patients. Current clinical guidelines, however, remain focused on medication intensification rather than deintensification where appropriate. This study aims to evaluate the effectiveness of an electronic decision-support system and training for the deintensification of potentially inappropriate medications amongst older frail people with T2D, when compared to ‘usual’ care at 12-months.

Methods

This study is an open-label, multi-site, two-armed pragmatic cluster-randomised trial. GP practices randomised to the ‘enhanced care’ group have an electronic decision support system installed and receive training on the tool and de-intensification of diabetes medications. The system flags eligible patients for possible deintensification of diabetes medications, linking the health care professional to a clinical algorithm. The primary outcome will be the number of patients at 12-months who have had potentially inappropriate diabetes medications de-intensified.

Results

Study recruitment commenced in June 2022. Data collection commenced in January 2023. Baseline data have been extracted from 40 practices (3145 patients).

Conclusions

Digital technology, involving computer decision systems, may have the potential to reduce inappropriate medications and aid the process of de-intensification.

Trial registration

International Standard Randomised Controlled Trial Number: ISRCTN53221378. Available at: https://www.isrctn.com/ISRCTN53221378.

Keywords: Type 2 diabetes, Primary health care, Overtreatment, De-intensifying medications

Abbreviations

HbA1c

glycated haemoglobin

NHS

National Health Service

T2D

type 2 diabetes

UoL

University of Leicester

PRIMIS

Primary Care Information Service

EMIS

Egton Medical Information Systems.

1. Introduction

Primary care health care professionals play a fundamental role in diabetes management, delivering up to 90% of care to type 2 diabetes (T2D) patients [1]. Globally it is estimated that 122.8 million people aged ≥ 65 years have T2D and it is predicted this number will more than double by 2045, rising to an estimated 253.4 million people worldwide [2]. Treatment guidelines for diabetes advocating tight glucose, blood pressure and cholesterol control will allow many people living with T2D to have a good quality of life, live independently and self-manage their condition to improve their life expectancy. However, for some older people it is a different story; many will suffer a progressive decline in their physical and/or mental health, becoming frail with increasing health and social needs [3]. In addition to their diabetes, many live with multiple long-term conditions, of which the prevalence increases substantially with age [4], and require multiple medications.

Therapeutic inertia relates to prescribing decisions made by health care professionals and was first defined as failure to increase therapy when treatment goals are not met, however it also applies to not with-holding therapy or de-intensifying therapy when appropriate to do so [5]. Managing treatment for this group is complex and there is a need to strike a balance between controlling the condition with appropriate medications to prevent ill health, whilst also stopping or reducing medications (referred to in this protocol as deintensification) to prevent adverse treatment effects and hypoglycaemia. Inappropriate management results in greater adverse outcomes, such as increased emergency ambulance call-outs [6], unplanned admissions with falls and fractures [7], mortality [8,9], and unnecessary prescribing costs. Consequently, medication management poses a significant burden on both the individual and society [10,11]. A joint consensus report by the American Diabetes Association and the European Association for the Study of Diabetes highlighted additional clinical considerations are needed in older people with diabetes which included de-prescribing medication to avoid unnecessary harm where appropriate [12]. However which de-intensification approaches are appropriate, beneficial, and easily implemented in primary care remains to be determined [13].

In diabetes care, glucose-lowering therapies are required to prevent progressive, longer-term diabetes-related complications. For older people, a decision needs to be made about whether the clinical benefits of treatment with glucose-lowering therapies outweigh the adverse treatment effects, such as risk of hypoglycaemia. As opposed to age, frailty should be a key determinant of individualised target setting and treatment choices [11], though to what extent frailty affects glycaemic control in elderly patients remains unknown [14]. The risk of falls is twice as high among elderly patients (aged ≥65) in comparison to younger patients with hypoglycaemia associated with a greater than 50% increase in the hazard of falls [15]. Additionally, frailty-related weight loss has been shown to improve insulin sensitivity, and subsequently increase the risk of hypoglycaemia [16].

A comprehensive holistic review of the patient’s current glycaemic management, instead of using just a single glycated haemoglobin (Hb1Ac) target parameter, should be the basis for this decision [17]. However, there has been little guidance for healthcare providers on how to safely de-intensify potentially inappropriate glucose-lowering treatments [18] which has resulted in therapeutic inertia in this area [19]. A major limitation to the development of such guidelines for the elderly has been the lack of evidence from trials as this population is often excluded [20]. However, in recent years, this problem has started to be addressed and new evidence and guidance has emerged [21]. A recent systematic review highlighted overtreatment amongst older people with T2D, frailty, and multimorbidity, and suggested that under these conditions the deintensification of treatment is safe [22]. The International Diabetes Federation has published global guidelines on managing older people with T2D [20] and Primary Care Diabetes Europe has released a position statement which offers detailed guidance on deintensification [17]. Several clinical tools for deprescribing potentially inappropriate medications have been identified but the development of these tools is poorly reported and very few have been evaluated in clinical practice [23].

In the UK, as part of the 10-year National Health Service (NHS) plan, the importance of digital technology in delivering effective health outcomes was highlighted [24]. A recent review concluded that digital technology involving computer decision systems may have the potential to reduce inappropriate medications and aid the process of deprescribing, however, randomised controlled trials to evaluate the effectiveness of such interventions, with reasonable follow-up, are needed in this area [25]. The primary objective of this study is to evaluate the effectiveness of the D-Med intervention in increasing the proportion of potentially inappropriate diabetes medications de-intensified in a cohort of older people aged 65 years and over, with T2D and moderate to severe frailty, when compared to ‘usual’ care. We will conduct an open label, multi-site, two-armed (an intervention ‘enhanced care’ group and a control ‘usual’ care group), pragmatic 12-month follow-up cluster randomised trial.

2. Methods

2.1. Study setting

This study is a pragmatic 12-month follow-up cluster randomised controlled trial. The trial has been prospectively registered at the International Clinical Trials Registry Platform (ID: ISRCTN53221378), and ethical approval granted by the local NHS Research Ethics Committee (21/EM/0163). The protocol is described in accordance with the Standard Protocol Items: Recommendations for Intervention Trials 2013 statement [26] (supplementary file; Table 1). Primary care (GP) practices (representing clusters of individual patients) have been recruited from across four regions in England, UK. As this is an implementation cluster randomised trial, individual patient consent was not required. Instead, consent was obtained at practice level from a GP, designated as the principal investigator, for the practice to take part. An example of this form can be found in the supplementary material (supplementary file; document 1). For this study Primary Care Information Service (PRIMIS) will carry out all data extraction on behalf of the University of Leicester. PRIMIS is a specialist team of health informaticians who are internationally recognised experts on the intelligent use of primary care data.

2.2. Eligibility criteria

All practices with a list size of at least 4000 patients and using either the Egton Medical Information Systems (EMIS) Web or SystmOne clinical database system across the 4 regions are invited to take part. At baseline, PRIMIS run a remote database search to identify potential patients eligible for deintensification who fulfilled the following inclusion criteria: confirmed diagnosis of T2D, issued diabetes medication in the last 3- months prior to the baseline search, aged 65 years or older, and last HbA1c reading < 53 mmol/mol (7.0%) in the previous 12-months prior to the baseline search. Due to the way practices code frailty differently, it was not possible to effectively include frailty criteria in the baseline search of eligible patients across practices. However, the Rockwood Frailty Scale was included in the data output of each extraction (rather than used as a selection criterion due to difficulties mentioned above) so that frail patients within the larger group included in the data extractions can be considered for medication deintensification. Patients who fulfilled any of the following criteria were excluded: no confirmed diagnosis of T2D, no diabetes medication issued in the last 3- months, aged 64 years old and younger, last Hb1AC reading ≥ 53 mmol/mol (7.0%) in the previous 12-months prior to the baseline search, receiving palliative/end of life care, patient has opted out of sharing their personal data as part of the national data opt out policy.

2.3. Study intervention

The intervention is an electronic decision support system developed and remotely installed on the practice system by PRIMIS. The system flags patients eligible for a medication review and possible deintensification of their diabetes medication. When a flagged patient attends an appointment at the practice, an alert links the health care professional to a clinical algorithm, developed for this study, and based on the International Diabetes Federation categorisation of the elderly population with type 2 diabetes [20] (supplementary file; Fig. 1). The clinical algorithm details essential steps in the care of older frail patients with T2D and specific interventions to address current NICE recommendations for the management of T2D in adults, which advise to consider relaxing the target HbA1c level with particular consideration for people who are older or frail. The alert includes the option to forward a request to a colleague to carry out the medication review or to delay the review until another date. Prompts and deprescribing algorithms in electronic medical records are considered key facilitators of deprescribing implementation strategies in primary health care settings [27]. Two staff in each practice were asked to attend a one hour training webinar which covered how to use the electronic system to identify and manage their frail older patients with T2D who may require deintensification of potentially inappropriate diabetes medications. The training programme is supplemented with a 3-month performance review (telephone call with clinical advisor) and access to email support, plus monthly follow-up support calls with the clinical advisor to monitor patient safety. In addition to the appointment prompts the system can also be used to run searches and identify people who can be approached proactively. The usual care control GP practices will continue with their ‘usual’ care routine for patients with T2D, which may vary geographically but typically consist of regular HbA1c, blood pressure and cholesterol testing (every ~3 months), foot, eye and kidney function screening (every ~12 months) and discussion/adaptation of medications as required (without the use of the deintensification tool).

Fig. 1. Study flow diagram.

Fig. 1

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2.4. Outcomes

All outcome data will be extracted from patients’ primary care records at baseline, 6-months, and 12-months. The primary outcome will be the proportion of patients at 12 months who have had potentially inappropriate diabetes medications deintensified (stopped or reduced) since the baseline assessment. This will be measured by extracting, from the practices’ databases, recorded prescription data (type, dose, frequency, mode, and start/end dates) for all diabetes medications at each of the time points. The following secondary outcomes will also be measured: HbA1c, blood pressure, changes in T2D and blood pressure medication prescribing, including contraindications and adverse events. Medication-related adverse events will include: falls and fractures, hypoglycaemic and hyperglycaemic related adverse events, emergency admissions for hypoglycaemic and hyperglycaemic related events, and death. A study flowchart of the trial design is provided (Fig. 1).

2.5. Sample size

Based on preliminary searches, assuming an average of 40 patients per practice being eligible for medication deintensification and a conservative intra-class correlation of 0.05, 40 practices (20 in each arm, total number of patients 1520) are needed to detect a difference of 10% in the primary outcome of medication deintensification, allowing for the dropout of two practices. This assumes a small change in deintensification during the trial of 5% in the control arm (due to current practices and changes in care) and 15% in the intervention arm. After discussion with clinicians, a difference of 10% was used for the sample as this was felt to be an appropriate minimum and clinically important difference to assess. The sample size was calculated based on number of patients, not practices, with 90% power, and for a 5% significance level. Variation in cluster size between practices was allowed for, and the inflation for unequal cluster size was based on a reported average coefficient of variation of 0.65 [28]. The sample size was calculated in Stata using the clustersampsi command.

2.6. Assignment of intervention

Practices were assigned to either the control or intervention arm on a 1:1 basis, stratified for ethnicity (White European or other ethnic groups including Black and Asian) using a variable block size with concealed computer generated allocation sequence produced prior to study commencement. The sequence was generated by an independent statistician and allocation was carried out by an independent researcher. The research team were not aware of the allocation sequence during practice recruitment, to minimise allocation bias. As this is an open, unblinded trial, study staff and practices need to be made aware of their group allocation.

2.7. Data collection, management, and analysis

All outcome data will be extracted from patients’ primary care records and will consist of pseudonymised demographic and clinical data collected routinely as part of diabetes care. Data will be extracted in two files with different formats. One file format will involve one line of data (values and dates), per patient, per practice being extracted at each time point for the patient-level demographic and clinical variables. The other file format will be for the extraction of multiple items for prescription data and outcome events and will consist of either multiple lines per patient (EMIS) or multiple columns for each entry (SystmOne). Data extraction is carried out remotely using Away from My Desk (Away From My Desk Limited, United Kingdom) software [29]. Results are uploaded to a secure online database and transferred to the research team via encrypted NHS email systems. At the lead study site, the pseudonymised data files will be accessed on the Secure UoL IT network through secure, encrypted Windows 10 University provided and managed ‘fully assured’ laptops or desktop PCs which are covered by Cyber Essentials certification. Additionally, all staff who will have access to the data will have had to complete mandatory University and NHS Data Protection training (NHS-based staff only) and will need to comply with the UoL Information Security Policy and Data Management Protocol.

2.8. Statistical methods

The statistical analysis will be carried out by intention-to-treat. Continuous variables will be summarised as mean and standard deviation or median and interquartile range, and categorical variables will be given as counts and percentages. To adjust for cluster, we will use robust generalised estimating equations with an exchangeable correlation structure. For the primary outcome (medication de-intensified or not), we will use a logit link with a binomial distribution. Potential confounding factors will be investigated and adjusted for. All analyses will be carried out in Stata (Version 15.0). The missing indicator method will be used to account for missing data which attempts to incorporate the missing data patterns into the data analysis. The statistical analysis plan will be prepared and approved by the Independent Data Monitoring Committee before analysis commences.

2.9. Monitoring

Relevant sections of data collected during the study may be looked at by responsible individuals from the research team, sponsor, NHS Trust or regulatory authorities to permit trial-related monitoring, audits and inspections. The University of Leicester operates a risk-based audit programme to which this study will be subject. PRIMIS has robust procedures in place to quality assure the remote data extraction, and no quality control checks will be required as the data will be directly extracted into the files which will be used for analysis. Detailed reports of study activities will be required on a regular basis by the funder, National Institute for Health and Care Research Applied Research Collaborations East Midlands, to track progress and the achievement of key milestones. In addition, an annual report will be required for the Ethics Committee.

2.10. Dissemination

Study results will be disseminated in peer-reviewed journals and presented at annual scientific meetings. Authorship will be in line with the International Committee of Medical Journal Editors Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals [30]. We will also share the results of this study directly with stakeholders by developing infographics.

3. Results

Study recruitment commenced in June 2022. Data collection commenced in January 2023. The study is ongoing, and we anticipate that results will be reported in late 2024. Baseline data have been extracted from 41 practices (21 control and 19 intervention). From these practices 3415 eligible patients have been identified.

4. Discussion

4.1. Overview

There is a clear need for better evidence to guide treatment decisions for elderly patients with T2D, current guidelines remain more focused on preventing under-treatment than over-treatment. Continuing hypoglycaemic agents despite low HbA1c could put people with frailty at greater risk compared to deintensifying these patients [14], additionally they add an unnecessary financial burden to existing NHS financial pressures. This paper describes the design of a pragmatic cluster randomised controlled trial that will investigate the effectiveness of an electronic decision-support system, plus training with follow-up support and performance review in reducing the proportion of potentially inappropriate diabetes medications prescribed in a cohort of older people aged ≥ 65, with T2D and moderate to severe frailty.

4.2. Strengths and limitations

Electronic health record systems capture and store information on patients’ health over time and are routinely used in primary care setting. In recent years tools have been developed to allow the extraction of data and subsequent secondary use for research purposes [31]. Within the current study, to allow for the collection of large amounts of prescription data, primary care data is being extracted. Thus, the proposed research relies on the accuracy of primary care electronic health records, which could vary from practice to practice. Additionally, due to complexities and variation in the coding of frailty in general practice, frailty score was not used as an eligibility criterion when searching and extracting patient data, but was used following extraction to identify frail patients within the included population. Strengths of this study include its pragmatic design and delivery. As individual patient consent is not required, the included sample is much more representative of the true population treated by general practice (i.e., no selection or recruitment bias). Similarly, the use of routine data extraction to obtain all outcome measures means that no additional assessments are required and patient burden is minimised. Lastly, the delivery of the intervention is highly pragmatic and aligns closely with current routine clinical practice, thus minimising clinician burden and ensuring any findings are more representative of a real-life scenario (i.e., outside of a research setting).

5. Conclusions

The results of this study will provide insights into the effectiveness of the proposed intervention in a primary care setting. If the results of the D-Med Study are positive, there is a potential for “scaling up” under real-world conditions to reach a greater proportion of the eligible population. Skills, competencies, and workforce required for wider implementation would need to be assessed, and the results of this study would provide policy makers and senior decision makers with vital information to facilitate widespread adoption.

Supplementary Material

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.pcd.2023.12.001.

Supplementary Material

Acknowledgements

SS is the principal investigator and will oversee the project implementation. LO, PH, HD, SS, KK, designed the trial. LO and RA coordinated data collection. JR, AM, and colleagues at PRIMIS developed and remotely installed the electronic decision support system. CG developed the statistical analysis plan, and CG and SR and performed randomisation of practices. LO wrote the protocol paper with input from others. All authors have commented on and approved the final version of the paper. Funding is provided by National Institute of Health Research Applied Research Collaboration East Midlands. The sponsor has no involvement with the collection, management, analysis, and interpretation of data or writing of the final report.

Footnotes

Declaration of Competing Interest

KK has acted as a consultant and speaker for Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, and Merck Sharp & Dohme. He has received grants in support of investigator and investigator-initiated trials from Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim, and Merck Sharp & Dohme. SS has received funds for research, consultancy to or honoraria from Boehringer Ingelheim, Lilly, Merck Sharp & Dohme Limited, Novartis Pharmaceuticals UK Ltd, Novo Nordisk Limited, Sanofi-Aventis, Amgen, Astra Zeneca, Abbott Janssen-Cilag Ltd, NAPP, and Takeda UK Ltd. JPS receives funding from the Wellcome Trust/Royal Society via a Sir Henry Dale Fellowship (ref: 211182/Z/18/Z), the National Institute for Health and Care Research (NIHR) and from the British Heart Foundation (refs: PG/21/10341; FS/19/13/34235). This research was funded in part, by the Wellcome Trust [211182/Z/18/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. All other authors: none declared.

Data availability

The data sets generated and analysed during this study will be available from the corresponding author on reasonable request with appropriate institutional review board approval and data use agreement.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material
EMS208800-supplement-Supplementary_Material.docx (243.7KB, docx)

Data Availability Statement

The data sets generated and analysed during this study will be available from the corresponding author on reasonable request with appropriate institutional review board approval and data use agreement.

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