Liverpool Lung Project lung cancer risk stratification model: calibration and prospective validation

BackgroundEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.MethodsVersion 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-cont...

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Published inThorax Vol. 76; no. 2; pp. 161 - 168
Main Authors Field, John K, Vulkan, Daniel, Davies, Michael P A, Duffy, Stephen W, Gabe, Rhian
Format Journal Article
LanguageEnglish
Published London BMJ Publishing Group Ltd and British Thoracic Society 01.02.2021
BMJ Publishing Group LTD
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Abstract BackgroundEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.MethodsVersion 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-control data in Liverpool and further adapted when applied for selection of subjects for the UK Lung Screening Trial. The objective was to produce version 3 (LLPv3) of the model, by calibration to national figures for 2017. We validated both LLPv2 and LLPv3 using questionnaire data from 75 958 individuals, followed up for lung cancer over 5 years. We validated both discrimination, using receiver operating characteristic (ROC) analysis, and absolute incidence, by comparing deciles of predicted incidence with observed incidence. We calculated proportionate difference as the percentage excess or deficit of observed cancers compared with those predicted. We also carried out Hosmer-Lemeshow tests.ResultsThere were 599 lung cancers diagnosed over 5 years. The discrimination of both LLPv2 and LLPv3 was significant with an area under the ROC curve of 0.81 (95% CI 0.79 to 0.82). However, LLPv2 overestimated absolute risk in the population. The proportionate difference was −58.3% (95% CI −61.6% to −54.8%), that is, the actual number of cancers was only 42% of the number predicted.In LLPv3, calibrated to national 2017 figures, the proportionate difference was −22.0% (95% CI −28.1% to −15.5%).ConclusionsWhile LLPv2 and LLPv3 have the same discriminatory power, LLPv3 improves the absolute lung cancer risk prediction and should be considered for use in further UK implementation studies.
AbstractList Early detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.BACKGROUNDEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.Version 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-control data in Liverpool and further adapted when applied for selection of subjects for the UK Lung Screening Trial. The objective was to produce version 3 (LLPv3) of the model, by calibration to national figures for 2017. We validated both LLPv2 and LLPv3 using questionnaire data from 75 958 individuals, followed up for lung cancer over 5 years. We validated both discrimination, using receiver operating characteristic (ROC) analysis, and absolute incidence, by comparing deciles of predicted incidence with observed incidence. We calculated proportionate difference as the percentage excess or deficit of observed cancers compared with those predicted. We also carried out Hosmer-Lemeshow tests.METHODSVersion 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-control data in Liverpool and further adapted when applied for selection of subjects for the UK Lung Screening Trial. The objective was to produce version 3 (LLPv3) of the model, by calibration to national figures for 2017. We validated both LLPv2 and LLPv3 using questionnaire data from 75 958 individuals, followed up for lung cancer over 5 years. We validated both discrimination, using receiver operating characteristic (ROC) analysis, and absolute incidence, by comparing deciles of predicted incidence with observed incidence. We calculated proportionate difference as the percentage excess or deficit of observed cancers compared with those predicted. We also carried out Hosmer-Lemeshow tests.There were 599 lung cancers diagnosed over 5 years. The discrimination of both LLPv2 and LLPv3 was significant with an area under the ROC curve of 0.81 (95% CI 0.79 to 0.82). However, LLPv2 overestimated absolute risk in the population. The proportionate difference was -58.3% (95% CI -61.6% to -54.8%), that is, the actual number of cancers was only 42% of the number predicted.In LLPv3, calibrated to national 2017 figures, the proportionate difference was -22.0% (95% CI -28.1% to -15.5%).RESULTSThere were 599 lung cancers diagnosed over 5 years. The discrimination of both LLPv2 and LLPv3 was significant with an area under the ROC curve of 0.81 (95% CI 0.79 to 0.82). However, LLPv2 overestimated absolute risk in the population. The proportionate difference was -58.3% (95% CI -61.6% to -54.8%), that is, the actual number of cancers was only 42% of the number predicted.In LLPv3, calibrated to national 2017 figures, the proportionate difference was -22.0% (95% CI -28.1% to -15.5%).While LLPv2 and LLPv3 have the same discriminatory power, LLPv3 improves the absolute lung cancer risk prediction and should be considered for use in further UK implementation studies.CONCLUSIONSWhile LLPv2 and LLPv3 have the same discriminatory power, LLPv3 improves the absolute lung cancer risk prediction and should be considered for use in further UK implementation studies.
BackgroundEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our ability to identify those most at risk.MethodsVersion 2 of the Liverpool Lung Project risk score (LLPv2) was developed from case-control data in Liverpool and further adapted when applied for selection of subjects for the UK Lung Screening Trial. The objective was to produce version 3 (LLPv3) of the model, by calibration to national figures for 2017. We validated both LLPv2 and LLPv3 using questionnaire data from 75 958 individuals, followed up for lung cancer over 5 years. We validated both discrimination, using receiver operating characteristic (ROC) analysis, and absolute incidence, by comparing deciles of predicted incidence with observed incidence. We calculated proportionate difference as the percentage excess or deficit of observed cancers compared with those predicted. We also carried out Hosmer-Lemeshow tests.ResultsThere were 599 lung cancers diagnosed over 5 years. The discrimination of both LLPv2 and LLPv3 was significant with an area under the ROC curve of 0.81 (95% CI 0.79 to 0.82). However, LLPv2 overestimated absolute risk in the population. The proportionate difference was −58.3% (95% CI −61.6% to −54.8%), that is, the actual number of cancers was only 42% of the number predicted.In LLPv3, calibrated to national 2017 figures, the proportionate difference was −22.0% (95% CI −28.1% to −15.5%).ConclusionsWhile LLPv2 and LLPv3 have the same discriminatory power, LLPv3 improves the absolute lung cancer risk prediction and should be considered for use in further UK implementation studies.
Author Vulkan, Daniel
Davies, Michael P A
Duffy, Stephen W
Gabe, Rhian
Field, John K
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Copyright Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.
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Snippet BackgroundEarly detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends...
Early detection of lung cancer saves lives, as demonstrated by the two largest published low-dose CT screening trials. Optimal implementation depends on our...
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StartPage 161
SubjectTerms Age
Calibration
Family medical history
imaging/CT MRI etc
Lung cancer
Lung diseases
Medical screening
Pneumonia
Population
Questionnaires
Smoking
Title Liverpool Lung Project lung cancer risk stratification model: calibration and prospective validation
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