Dr Chris Sainsbury BSc MBChB MD FRCP FFCI

• FFCI – Founding Fellow of the Faculty of Clinical Informatics, 2017
• FRCP – Fellow of the Royal College of Physicians (Glasgow), 2008
• MD – University of Glasgow, 2005
• MRCP – Member of the Royal College of Physicians, 1998
• MBChB – University of Edinburgh, 1995
• BSc – Parasitology and Entomology, University of Edinburgh, 1993

Dr Sainsbury trained in Diabetes & Endocrinology in Glasgow. His MD was in the area of endothelial dysfunction in hypertension. Following this, he collaborated with the Mathematics department at the University of Glasgow, and with colleagues in respiratory medicine in the development of a 1-dimensional fluid dynamic model of the systemic and pulmonary circulation, incorporating a model of capillary beds.

For the past 5 years Dr Sainsbury has been focusing on analysis of inpatient capillary blood (CBG) glucose data, and on analysing and applying machine learning approaches to data from the Scottish national diabetes database – SCI-Diabetes.

 With colleagues in Glasgow has published on the utility of inpatient CBG data to stratify inpatients for risk of adverse outcome during admission and post discharge (focusing particularly on metrics of variability), on the use of CBG data to assess the performance of clinical staff in managing diabetes, and on the association between HbA1c variability and outcome in Type 1 diabetes.

His current research is largely focused on the application of machine learning approaches to diabetes, including using recurrent neural networks and generative adversarial networks to predict outcomes in diabetes using time series data, to investigate data-driven classification of diabetes subtypes from large datasets, and also to devise novel solutions to the general problem of clinical data sharing using synthetic datasets.

In 2018 he was awarded an NRS Senior Fellowship following the award of a 1M grant from Innovate UK along with colleagues across Scotland, with the aim of bringing machine learning driven decision support to the clinical front line. Also in 2018, Dr Sainsbury joined the Institute of Applied Health Research at the University of Birmingham as an Honorary Senior Research Fellow, with the aim of applying similar machine learning approaches to primary care datasets.

In parallel to my clinical practice, Dr Sainsbury is active in research and health improvement via data analysis, in collaboration with a group of consultant and junior colleagues, and also with non-clinical collaborators. His research aims to both leverage the significant advantage of having access to large diabetes datasets - and to test methods of applying insights gained from this data analysis to day-to-day clinical practice.

 Broadly, current themes of investigation are:

(i) Generation of machine learning (supervised learning using multi-dimensional recurrent neural networks) algorithms to allow both risk prediction and virtual n=1 trials of intervention to be performed - allows selection of the best therapeutic approach to influence a clinical parameter important to the patient (eg blood pressure, average glucose, risk of death etc). This is an exciting concept which has the potential to apply real-world insight from the national patient cohort at an individual patient level.

 (ii) Development and clinical application of comprehensive, data-driven inpatient diabetes management system. This project monitors both the performance of the clinical system at managing patients with diabetes (and the complications that they might encounter eg low blood sugar), and identifies individual patients at risk of adverse outcome, allowing a proactive approach to be taken to management.

 (iii) Investigation of the association of variability of glucose / cardiovascular clinical metrics over multiple timescales with adverse outcome (eg mortality / admission to hospital). This work has lead to multiple publications, including recently the first description of an association between HbA1c (average glucose) variability and all-cause mortality in Type 1 diabetes.

Research groups and centres:

the glucose.ai research group:  http://glucose.ai

• Consultant Physician (Endocrinology, Diabetes Mellitus and General (Internal) Medicine)  Queen Elizabeth University Hospital, and Gartnavel General Hospital, Glasgow, UK (Aug 2010 - present)
•  NRS (NHS Research Scotland) Senior Fellow (2018-)
•  MyDiabetesIQ project group member (2018-). Innovate UK funded.
• Co-PI, glucose.ai (http://glucose.ai) research group Co-Founder, kvatchii (http://kvatchii.com). A University of Glasgow spinout company bringing the benefits of machine-learning-augmented patient-held medical records to India
• Member, Clinical Study Group 4 (Acute) Diabetes UK (2017-)
• Chair, NHS Greater Glasgow and Clyde MCN Data and Innovation Subgroup. (2016-)