Improving the care of people with diabetes-related ketoacidosis (DKA): The DEKODE (Digital Evaluation of Ketosis and Other Diabetes Emergencies) study

Diabetes-related Ketoacidosis (DKA) is one of the most common causes of emergency hospital admissions for people with type 1 diabetes (T1DM). A triad of hyperglycaemia, acidosis, and ketosis characterises DKA. DKA is associated with morbidity and mortality, costing the NHS more than £90M annually to manage the condition.

My award-winning research has shown that DKA care varies widely between patients and can be minimised through regular feedback on key performance indicators.

Inspired by these findings, I created DEKODE (Digital Evaluation of Ketosis and Other Diabetes-related Emergencies), a system to provide regular electronic feedback on key performance indicators of DKA care, which has already been demonstrated to provide patient benefit. I would like further to develop this work through the following work packages.

My research aims to improve the safety and management of DKA in people admitted to hospitals in the UK.

I will achieve this through four work packages: 1: Description of the inequities in DKA care and outcomes in the UK I will explore the influences of age, sex, ethnicity, deprivation, and region on DKA care and outcome using powerful data sets, including Clinical Practice Research Datalink (CPRD) Aurum and CPRD Gold. 2: Lived experiences of people with DKA.

I will conduct surveys and interviews with people admitted with DKA and the healthcare professionals involved in its management.

With a focus on inclusivity, I will ensure appropriate representation of differentially impacted groups identified in work package 1, unravelling reasons for differential outcomes with DKA. 3: DEKODE- understanding the model, its implementation and effectiveness.

I will undertake in-depth interviews with medical students, junior doctors and consultants involved in developing and piloting the DEKODE initiative. These interviews will explore details of their experience and insights with introducing and operationalising DEKODE.

Comparing these findings to published literature, I will outline the similarities and unique aspects that define DEKODE's success.

Additionally, I will invite healthcare professionals to focus group discussions to understand their motivations and adaptations of the DEKODE model. 4: Health economics evaluation of the DEKODE model I will assess the cost-effectiveness of the DEKODE model versus usual care for people admitted with DKA in hospitals in the UK.

I will undertake a model-based cost-utility analysis with outcomes measured in quality-adjusted life years (QALYs).

At the end of this fellowship, I will have: The necessary information and infrastructure to implement the best approach to managing DKA within the NHS An in-depth understanding of patient preferences and support expected when admitted with DKA. I can then use this information to develop measures to help people with diabetes avoid developing DKA.

The training required to implement similar programmes for other diabetes-related conditions.

I will share my findings with stakeholders to identify and mitigate risk factors for DKA, thus preventing future episodes. The findings can be used to create evidence-based guidelines for DKA. This can lead to reduced variation in DKA care between and within hospitals. I will also share my findings in various formats in collaboration with patient support groups to reach end users.