In:
Thorax, BMJ, Vol. 77, No. 9 ( 2022-09), p. 882-890
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 (LLP v2 ) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCO m2012 ) models. Lung cancer occurrence over 5–6 years was measured in ever-smokers aged 50–80 years and compared with 5-year (LLP v2 ) and 6-year (PLCO m2012 ) 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, LLP V2 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 m2012 showed similar performance (c-statistic: 0.679 (0.673–0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLP v2 ) and 0.15% (PLCO m2012 ), 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.
Type of Medium:
Online Resource
ISSN:
0040-6376
,
1468-3296
DOI:
10.1136/thoraxjnl-2021-217142
Language:
English
Publisher:
BMJ
Publication Date:
2022
detail.hit.zdb_id:
1481491-2
