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Lung cancer
Original research
Selection of eligible participants for screening for lung cancer using primary care data
  1. Emma L O'Dowd1,
  2. Kevin ten Haaf2,
  3. Jaspreet Kaur3,
  4. Stephen W Duffy4,
  5. William Hamilton5,
  6. Richard B Hubbard3,
  7. John K Field6,
  8. Matthew EJ Callister7,
  9. Sam M Janes8,
  10. Harry J de Koning9,
  11. Janette Rawlinson10,
  12. David R Baldwin11
  1. 1 Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK
  2. 2 Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
  3. 3 Department of Epidemiology, University of Nottingham School of Medicine, Nottingham, UK
  4. 4 Wolfson Institute of Preventive Medicine, Barts and London, London, UK
  5. 5 Primary Care Diagnostics, University of Exeter, Exeter, UK
  6. 6 Department of Molecular and Clinical Cancer Medicine, Institute of Systems, University of Liverpool, Liverpool, UK
  7. 7 Department of Respiratory Medicine, Leeds Teaching Hospitals, Leeds, UK
  8. 8 Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
  9. 9 Public Health, Erasmus MC, Rotterdam, The Netherlands
  10. 10 European Lung Foundation, Birmingham, UK
  11. 11 City Campus, Nottingham University Hospitals NHS Trust, Nottingham, UK
  1. Correspondence to Dr David R Baldwin, City Campus, Nottingham University Hospitals NHS Trust, Nottingham, Nottingham, UK; david.baldwin{at}nuh.nhs.uk

Abstract

Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.

Method The Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLPv2) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCOm2012) models. Lung cancer occurrence over 5–6 years was measured in ever-smokers aged 50–80 years and compared with 5-year (LLPv2) and 6-year (PLCOm2012) predicted risk.

Results Over 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLPV2 produced a c-statistic of 0.700 (0.694–0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCOm2012 showed similar performance (c-statistic: 0.679 (0.673–0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLPv2) and 0.15% (PLCOm2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers.

Conclusion Risk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.

  • imaging/CT MRI etc
  • lung cancer

Data availability statement

Data may be obtained from a third party and are not publicly available. Data available through CPRD and NCRAS.

https://doi.org/10.1136/thoraxjnl-2021-217142

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    Data availability statement

    Data may be obtained from a third party and are not publicly available. Data available through CPRD and NCRAS.

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    Footnotes

    • Correction notice This article has been corrected since it was published Online First. The funding statement and competing interests statements have been updated.

    • Contributors All authors contributed to the research concept and design. All authors edited the manuscript and approved the final version. Analysis was conducted by JK and EO and further refined by DRB, SWD, KtH and RH. All these latter authors verified the underlying data. DRB is guarantor for the work.

    • Funding This research was funded by Cancer Research UK C35238/A26388. This research is linked to the CanTest Collaborative, which is funded by Cancer Research UK [C8640/A23385].

    • Competing interests KtH reports grants from Cancer Research UK, during the conduct of the study; grants from European Union (Horizon 2020), grants from University of Zurich, Switzerland, non-financial support from International Association for the Study of Lung Cancer, non-financial support from International Association for the Study of Lung Cancer, non-financial support from Russian Society of Clinical Oncology, non-financial support and other from Biomedical Research In Endstage And Obstructive Lung Disease Hannover (BREATH), grants from NIH/National Cancer Institute, outside the submitted work. WH is Co-PI of CanTest Collaborative, funded by Cancer Research UK. RBH reports personal fees from Galapagos, outside the submitted work. SMJ reports grants from GRAIL, personal fees from AstraZeneca, personal fees from BARD1 Bioscience, personal fees from Achilles Therapeutics, grants from Owlstone, other from Optellum, personal fees from Johnson and Johnson, other from AstraZeneca, outside the submitted work. HJdK reports grants from Cancer Research UK, during the conduct of the study; grants from European Union (Horizon 2020), personal fees from University of Zurich, Switzerland / MSD, personal fees from IPSOS London, grants from NIH/National Cancer Institute, personal fees from Teva, Copenhagen, Denmark, outside the submitted work. DRB reports grants from Cancer Research UK, during the conduct of the study; personal fees from Roche, personal fees from AstraZeneca, personal fees from MSD, personal fees from BMS, outside the submitted work.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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