Personalised blood testing schedules in chronic disease management

Lead Research Organisation: University of Glasgow
Department Name: College of Medical, Veterinary, Life Sci

Abstract

The management of chronic diseases, primarily cardiovascular disease and diabetes, accounts for 50 percent of pathology activity in the UK and 50 percent of clinical biochemistry testing. The number of clinical biochemistry tests requested is estimated to have increased by 10 percent annually for the last two decades. This has been due to a combination of factors including the GP contract and quality outcomes framework, introduction of chronic disease management programmes and guidelines, patient requests and the ease of requesting with electronic systems and phlebotomy. Yet there is a paucity of evidence that frequent testing actually improves patient care or alters clinical outcomes. SIGN guidance for CVD prevention recommends risk factor screening (including lipids and renal function) at least every 5 years and annually once prescribed an anti-hypertensive or statin, but this guidance is based entirely on expert opinion. In contrast, in acute care generally associated with short re-test intervals (such as troponin measurement in MI patients), there has been extensive research to achieve re-test cost savings. Lack of data has been the barrier to similar research in longer-term routine biochemistry testing.

The widespread use of IT systems in primary care, for test requesting, and electronic health records means that this one size fits all approach to testing need not continue. We hypothesize that simple evidence-based algorithms could be developed which will allow the subsequent testing frequency to be determined by the previous test result(s), identifying those who have stable disease or risk factors, and those at higher risk of reaching a level that would require a modification in clinical management. Such a framework would allow for substantial improvements in patient care as well as cost savings in a key area of NHS expenditure, both in terms of consumables and staff time. For example, a single unnecessary blood test will require a clinical appointment, phlebotomy consumables, phlebotomist/clinician time, risk of venepuncture, shipping of sample, technician time, reagent consumables, interpretation by a biochemist is the results are unusual (although stable), and a possible repeat appointment to discuss the results. This is a key era of expenditure that might be optimised under a personalised medicine approach.

The NHS Greater Glasgow and Clyde Safe haven allows access to all diagnostic test results, chronic disease registers, pharmacy data and hospitalisations for 1.2 million individuals. The initial focus of the project will be lipid measurement. Working with colleagues from clinical biochemistry, testing schedules will be identified (including CVD and diabetes) that are currently placing a high demand on NHS laboratories. The student will also attend chronic disease review clinics, with the primary supervisor, to gain an understanding of the context for this testing; an established pathway under our flagship Clinical Observership Programme for basic scientists in our institute.

General aim: To develop algorithms to allow the personalisation of blood testing frequency (cholesterol, triglycerides, HDL-cholesterol, and creatinine) in the prevention and management of cardiovascular disease.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
MR/N013166/1 01/10/2016 30/09/2025
1952050 Studentship MR/N013166/1 11/09/2017 10/03/2021 Rosemary Brown
 
Title Project Code 
Description R code completed for the analysis of the SafeHaven datasets for the project, including substantial data cleaning and variable derivation. Currently, this code can be made available to others who approach for the methods, but will ultimately be made freely available through a repository. 
Type Of Material Improvements to research infrastructure 
Year Produced 2019 
Provided To Others? No  
Impact Methods related to data cleaning can be used to standardise the analysis of similar datasets derived from similar sources, improving reproducibility. Code used to estimate medication adherence have been made available to a similar project.