O'Dowd, Emma L, Ten Haaf, Kevin, Kaur, Jaspreet, Duffy, Stephen W, Hamilton, William, Hubbard, Richard B, Field, John K 
ORCID: 0000-0003-3951-6365, Callister, Matthew Ej, Janes, Sam M, de Koning, Harry J et al (show 2 more authors)
(2022)
Selection of eligible participants for screening for lung cancer using primary care data.
THORAX, 77 (9).
pp. 882-890.
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Selection of eligible participants for screening for lung cancer using primary care data - Final word version.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution Non-commercial. Download (445kB) | Preview |
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.<h4>Method</h4>The Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLP<sub>v2</sub>) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCO<sub>m2012</sub>) models. Lung cancer occurrence over 5-6 years was measured in ever-smokers aged 50-80 years and compared with 5-year (LLP<sub>v2</sub>) and 6-year (PLCO<sub>m2012</sub>) predicted risk.<h4>Results</h4>Over 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLP<sub>V2</sub> produced a c-statistic of 0.700 (0.694-0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCO<sub>m2012</sub> showed similar performance (c-statistic: 0.679 (0.673-0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLP<sub>v2</sub>) and 0.15% (PLCO<sub>m2012</sub>), 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.<h4>Conclusion</h4>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.
| Item Type: | Article |
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| Uncontrolled Keywords: | imaging, CT MRI etc, lung cancer |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 02 Nov 2021 08:16 |
| Last Modified: | 18 Jan 2023 21:25 |
| DOI: | 10.1136/thoraxjnl-2021-217142 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3142412 |
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