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dc.contributor.authorJani, Bhautesh Dinesh
dc.contributor.authorSullivan, Michael K
dc.contributor.authorHanlon, Peter
dc.contributor.authorNicholl, Barbara I
dc.contributor.authorLees, Jennifer S
dc.contributor.authorBrown, Lamorna
dc.contributor.authorMacDonald, Sara
dc.contributor.authorMark, Patrick B
dc.contributor.authorMair, Frances S
dc.contributor.authorSullivan, Frank M.
dc.identifier.citationJani , B D , Sullivan , M K , Hanlon , P , Nicholl , B I , Lees , J S , Brown , L , MacDonald , S , Mark , P B , Mair , F S & Sullivan , F M 2023 , ' Personalised lung cancer risk stratification and lung cancer screening : do general practice electronic medical records have a role? ' , British Journal of Cancer , vol. First Online .
dc.identifier.otherJisc: 1451672
dc.identifier.otherpii: 10.1038/s41416-023-02467-9
dc.identifier.otherORCID: /0000-0002-6206-8196/work/146960642
dc.identifier.otherORCID: /0000-0002-6623-4964/work/146964858
dc.descriptionDr. Bhautesh Dinesh Jani’s time was partly funded by a research grant from the British Medical Association. Chief Scientist Office (CSO, Scotland) funded SAIL data access costs (PCL/18/03). JSL was funded by CSO Postdoctoral Lectureship Award (PCL/20/10). The Medical Research Council fund MKS (MR/V001671/1) and PH (MR/S021949/1).en
dc.description.abstractBackground In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility. Methods The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination. Results Age 55–75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots). Discussion A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.
dc.relation.ispartofBritish Journal of Canceren
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titlePersonalised lung cancer risk stratification and lung cancer screening : do general practice electronic medical records have a role?en
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Population and Behavioural Science Divisionen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.description.statusPeer revieweden

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