To be clinically and cost effective, low radiation dose computed tomography (LDCT) screening for lung cancer needs to be offered to people at high risk of the disease. This study will develop new mathematical predictive models for selection of people for LDCT screening based on primary care data and compare these with existing models and recommendations. If better models are developed, the identification of individuals who will benefit most from screening, and of those unlikely to benefit, will improve, increasing the effectiveness and cost-effectiveness of the programs likely to be approved in the UK and Europe soon. This approach maximises benefit by using electronic primary care datasets available in the UK, but it is likely that the principles will be transferrable to other countries where electronic healthcare data are available. In particular, the value of adding information on symptoms will be clarified and could lead to improved selection models that can be used in other countries. The ultimate aim will be to develop a model that can be used to create a primary care-based tool embedded in primary care systems that accurately selects patients eligible for CT screening for lung cancer, and provides an opportunity for primary care based invitation.
Objectives:
The overall aim of this project is to determine if primary care data can be used to develop improved risk prediction models for selecting individuals for low dose CT screening for lung cancer? Specific research objectives are:
1. To develop a series of mathematical models using data from patients one, two and three years prior to lung cancer diagnosis.
2. To examine for differences between models, including analysis of variation when stratifying lung cancer into early and late stage, pathological subtype and by route to diagnosis.
3. To compare the predictive performance in the external validation datasets and the eligibility rates between the new model(s), the PLCOM2012 and the LLPv2 models over a range of risk thresholds.
4. To assess the cost effectiveness of the models at varying risk thresholds.
Methods and data analysis:
a. A case-control cohort will be identified using all cases of lung cancer linked to data from NCRAS in CPRD and 10 matched controls.
b. Patient demographic features, all symptoms, diagnoses, investigations and drug prescriptions will be identified and used to produce a risk prediction model. This will involve both traditional statistical techniques and a range of machine learning methods.
c. This model will be externally validated and cost effectiveness analysis will be performed using microsimulation modelling.
Primary:
Can primary care data be used to develop improved risk prediction models for selecting individuals for low dose CT screening for lung cancer?
Secondary:
How do these models compare with existing models and currently recommended criteria for screening in terms of sensitivity, specificity, predictive value and cost effectiveness?
What proportion of patients diagnosed with lung cancer would have been eligible for screening based on data one or more years prior to their diagnosis?
What is the cost effectiveness of selecting individuals for screening based on the application of these models?
David Baldwin - Chief Investigator - Nottingham University Hospitals
David Baldwin - Corresponding Applicant - Nottingham University Hospitals
david brown - Collaborator - Nottingham Trent University
Emily Peach - Collaborator - University of Nottingham
Emma O'Dowd - Collaborator - University of Nottingham
Graham Ball - Collaborator - Nottingham Trent University
Harry de Konig - Collaborator - Erasmus University Medical Center ( EMC )
Helen Morgan - Collaborator - University of Nottingham
Jaspreet Kaur - Collaborator - University of Nottingham
John Field - Collaborator - University of Liverpool
Jun He - Collaborator - Nottingham Trent University
Kevin ten Haaf - Collaborator - Erasmus University Medical Center ( EMC )
Libby Ellis - Collaborator - University of Nottingham
Matthew Callister - Collaborator - Leeds Teaching Hospitals NHS Trust
Mufti Mahmud - Collaborator - Nottingham Trent University
Muhammad Rahman - Collaborator - Nottingham Trent University
Rachael Murray - Collaborator - University of Nottingham
Richard Hubbard - Collaborator - University of Nottingham
Sam Janes - Collaborator - University College London ( UCL )
William Hamilton - Collaborator - University of Exeter
Aamir Khakwani - Collaborator - University of Nottingham
HES Admitted Patient Care;HES Diagnostic Imaging Dataset;HES Outpatient;NCRAS Cancer Registration Data;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation