Selection of Eligible People for Lung Cancer Screening using Electronic Primary Care Data (SELECT)
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. The significance of this research is that 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 the unique electronic primary care datasets available in the UK, but it is highly likely that the principles will be transferrable to other countries where electronic healthcare data are available.
The primary care model (SELECT model) when developed, will be externally validated in NLST dataset for its performance.
. Primary aim is to develop a primary care derived risk prediction model
. Externally validate the SELECT model in NLST dataset
. To show the SELECT model is more economically viable compared with the existing risk prediction models like PLCOm2012 in identify high risk individuals using GP records and the data they collect which may not have the extreme detailed patient information used in PLCOm2012 model for its performance.
Dr David Baldwin (Consultant Respiratory Physician & Honorary Professor)
Dr Emma L O'Dowd (Consultant Respiratory Physician & Honorary Associate Professor)
Professor Richard Hubbard (MD, FRCP)
John K Field (PhD, FRCPath)
Matthew E Callister (PhD, FRCP)
Samuel M Janes (PhD, FRCP)
Willie Hamilton (MD, FRCP, FRCGP)
Harry J de koning (MD, PhD)
Kevin Ten Haaf (MSc, PhD)