Title: Developing statistical methods to improve the diagnosis and care of hospitalised patients with COVID-19
Funder: The Academy of Medical Sciences

Abstract: This research will develop innovative, and generalisable, statistical methods to assess the relationship between, and utility of, combinations of diagnostic tests for healthcare decision-making in COVID-19. Diagnostic tests are typically assessed by independently synthesising evidence on measures of their accuracy (such as sensitivity/specificity). However, multiple test combinations (including near-patient testing, serologic immunoassays, radiological imaging and PCR) may need to be evaluated to make a comprehensive diagnosis in complex diseases, which are difficult to diagnose early and include rapidly evolving near-patient testing. Novel statistical methods are required to describe interdependencies between tests to deliver optimal diagnosis and healthcare decision-making for patients. To facilitate this goal, I will address each of the following aims: 1) Identify the most appropriate diagnostic test from electronic health records for early detection of COVID-19 This work package will develop methods for the comparison of individual diagnostic tests to identify the most appropriate single test for early detection of COVID-19. 2) Determine the optimal combination of diagnostic tests for diagnosis and management of COVID-19 This work package will develop methods to model the relationship between diagnostic tests to identify the optimal sequences of tests for COVID-19. 3) Allow for different sources (heterogeneity) of evidence to inform healthcare decision-making in COVID-19 This work package will develop methods to combine individual patient data with summary data to ensure that all available evidence is considered for healthcare decision-making. This research has the opportunity to influence emerging guidance on the appropriate evaluation of combinations of diagnostic tests, and management of COVID-19.

Lay abstract: This research will develop statistical methods to better understand the relationship between multiple tests, used for diagnosis, in individuals with COVID-19. However, the statistical methods will be applicable to broad range of disease areas. COVID-19 is a viral illness that can lead to a severe reaction and damage to the lungs, potentially leading to respiratory failure. Mass diagnosis of COVID-19 is essential to control the spread and management of the illness. The best test used for diagnosis, known as polymerase chain reaction (PCR), is performed in a laboratory and can take days to get a result. However, new tests that provide instant results are rapidly evolving. Tests used for diagnosis are assessed for use in the NHS on a case-by-case basis. However, for illnesses that are difficult to detect early and/or include new, rapidly evolving tests, such as COVID-19, it is important to consider the best combinations of tests. Understanding the relationship between combinations of tests, will allow healthcare decision-makers to make the best use of healthcare resources, which will improve patient management and care. The key aims of this research are to develop statistical methods to answer the following three questions: 1) Which test used for diagnosis, at which time, is the most effective for early detection of COVID-19? 2) Which combinations or sequences of tests used for diagnosis are the most effective for the detection and management of COVID-19? 3) Can we reduce uncertainty in the healthcare decision by including all of the available evidence?

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: INFECTION