Understanding the predictive values of symptoms, prescriptions, and investigation patterns for cancer and non-neoplastic disease in primary care consultees

Date of Approval
Application Number
18_299
Technical Summary

The overall research objective is to produce population-based evidence about the likelihood of cancer and serious non-neoplastic diseases* among primary care consultees, taking into account their symptoms and, where applicable, their history of repeat consultations, prescriptions, and investigations. We will estimate the predictive values of:
- Different symptomatic presentations for cancer (overall and by major tumour site) and serious non-neoplastic diseases diagnosed within a year after presentation.
- Symptoms combined with information on pre-diagnostic events (such as use of investigations or prescriptions).

This evidence is needed to support clinical decisions about either specialist referral/investigation, or active monitoring (also known as 'safety netting') for patients 'at low but not no-risk'.

We will perform a cohort study, including patients aged 30 years or older with one or more pre-specified symptoms of interest recorded in CPRD between 2007 and 2016. Using primary and secondary care data linked to cancer registration data we will estimate the predictive values (and 95% confidence intervals) of each selected symptom, both for cancer and non-neoplastic disease. Positive predictive values will correspond to the proportion of patients with a given symptom that are diagnosed with a specific outcome (one of the cancers or non-neoplastic diseases of interest) within 1 year since first symptomatic presentation. Similarly, predictive values of different symptom-prescription-investigation combinations will be estimated. We will also examine the role of covariates (socio-demographic factors, comorbidities) possibly influencing predictive values.

*Hereafter we define serious non-neoplastic disease as disease that requires treatment and/or is progressive. An example is inflammatory bowel disease.

Health Outcomes to be Measured

The outcomes will be: diagnosis of cancer; diagnosis of serious non-neoplastic disease.

Collaborators

Georgios Lyratzopoulos - Chief Investigator - University College London ( UCL )
Annie Herbert - Corresponding Applicant - University of Bristol
Arturo Gonzalez-Izquierdo - Collaborator - University College London ( UCL )
Bethany Wickramsinghe - Collaborator - University College London ( UCL )
Cristina Renzi - Collaborator - University College London ( UCL )
Edmund Njeru Njagi - Collaborator - University College London ( UCL )
Emma Whitfield - Collaborator - University College London ( UCL )
Gary Abel - Collaborator - University of Exeter
Irene Petersen - Collaborator - University College London ( UCL )
Jessica Kurland - Collaborator - University College London ( UCL )
Kathy Pritchard-Jones - Collaborator - University College London ( UCL )
Matthew Barclay - Collaborator - University College London ( UCL )
Meenakshi (Meena) Rafiq - Collaborator - UCL Hospital
Monica Koo - 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 )
Ruth Swann - Collaborator - University College London ( UCL )
Sara Benitez Majano - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Sarah Price - Collaborator - University of Exeter
Tra Pham - Collaborator - University College London ( UCL )
William Hamilton - Collaborator - University of Exeter

Linkages

HES Admitted Patient Care;HES Diagnostic Imaging Dataset;HES Outpatient;NCRAS Cancer Registration Data;Patient Level Index of Multiple Deprivation