It is widely agreed that major improvements are needed in medical diagnosis, and that diagnosing patients more promptly improves both their prognosis and patient experience. Despite this, the potential for earlier diagnosis of certain conditions remains unknown. 'Diagnostic window' studies can help answer these questions by finding the time when patients with a condition (cases) begin to use healthcare differently (compared to a healthy baseline) before their subsequent diagnosis. Measuring diagnostic windows will help us understand – both in general, and for specific conditions – the potential for earlier diagnosis, and help target future research and diagnostic improvement efforts.
We will use Clinical Practice Research Datalink (CPRD) data to measure diagnostic windows for a range of different conditions (cancer, Parkinson's disease, and Celiac disease, amongst others). We expect to find diagnostic windows ranging in length from a few months to several years.
Additionally, for many of these conditions we do not know how many patients are diagnosed after an 'emergency presentation' (a visit to A&E or an unexpected hospital admission) or even only diagnosed after death. By linking CPRD to Hospital Episode Statistics (HES) and Office for National Statistics (ONS) death data we will describe these two groups of patients, who would likely benefit most from earlier diagnosis and targeted improvement efforts.
The aim of this study is to document the presence and quantify the length of diagnostic windows (pre-diagnostic time periods when diagnosis may be possible) for a range of diseases (colon cancer, ovarian cancer, pancreatic cancer, lung cancer, brain tumours, Parkinson’s disease, schizophrenia, celiac disease, inflammatory bowel disease, rheumatoid arthritis, ankylosing spondylitis, multiple sclerosis, chronic obstructive pulmonary disease, tuberculosis, Lyme disease, subacute bacterial endocarditis, coronary/ischaemic heart disease, polycystic ovary syndrome). The diagnostic window of a condition is an estimate of the earliest point in time before diagnosis when healthcare use in the as-yet-undiagnosed population starts to differ from its expected pattern. We will use this evidence to measure the potential for earlier diagnosis of the condition and guide future improvement efforts and research to improve diagnosis. We will explore different methods for measuring diagnostic windows, contributing to the wider development of the research area.
A secondary aim will be to document the frequency of emergency presentations before the diagnosis of included conditions and the proportion of the as-yet-undiagnosed population who present in emergency settings, as determined from HES data. This evidence will improve our understanding of how patients interact with healthcare prior to their diagnosis and establish whether different ‘routes to diagnosis’ have different predictors (e.g. socioeconomic status) and consequences (e.g. worse prognosis), as previously shown for neoplastic diseases.
The study population will consist of patients with a first diagnosis of one of the conditions of interest in CPRD, HES or ONS between 1999 and 2019, and controls matched on age, sex, and practice. CPRD and HES data will be used to identify pre-diagnostic healthcare utilisation, which we will then model using Poisson or negative binomial regression to investigate diagnostic windows. We will also examine whether certain patient subgroups experience different diagnostic windows (e.g. sex, age, deprivation).
Pre-diagnostic healthcare utilisation; Diagnostic window presence; Diagnostic window length; Pre-diagnostic emergency presentations
Georgios Lyratzopoulos - Chief Investigator - University College London ( UCL )
Emma Whitfield - Corresponding Applicant - University College London ( UCL )
Arturo Gonzalez-Izquierdo - Collaborator - University College London ( UCL )
Marta Berglund - Collaborator - University College London ( UCL )
Matthew Barclay - Collaborator - University College London ( UCL )
Muhammad Qummer ul Arfeen - Collaborator - University College London ( UCL )
Nadine Zakkak - Collaborator - University College London ( UCL )
Rebecca White - Collaborator - University College London ( UCL )
Spiros Denaxas - Collaborator - University College London ( UCL )
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation