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dc.contributor.authorMcLernon, David J.
dc.contributor.authorDillon, John F.
dc.contributor.authorSullivan, Frank M.
dc.contributor.authorRoderick, Paul
dc.contributor.authorRosenberg, William M.
dc.contributor.authorRyder, Stephen D.
dc.contributor.authorDonnan, Peter T.
dc.identifier.citationMcLernon , D J , Dillon , J F , Sullivan , F M , Roderick , P , Rosenberg , W M , Ryder , S D & Donnan , P T 2012 , ' The utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI) ' , PLoS ONE , vol. 7 , no. 12 , e50965 .
dc.identifier.otherPURE: 256233491
dc.identifier.otherPURE UUID: 9af46eeb-7981-459d-98d5-38da284a7aa5
dc.identifier.otherScopus: 84871301445
dc.identifier.otherPubMed: 23272082
dc.descriptionThis work was supported by the National Health Service Research & Development Programme Health Technology Assessment Programme (project number 03/38/02) and the Backett Weir Russell Career Development Fellowship.en
dc.description.abstractBackground: Although liver function tests (LFTs) are routinely measured in primary care, raised levels in patients with no obvious liver disease may trigger a range of subsequent expensive and unnecessary management plans. The aim of this study was to develop and validate a prediction model to guide decision-making by general practitioners, which estimates risk of one year all-cause mortality in patients with no obvious liver disease. Methods: In this population-based historical cohort study, biochemistry data from patients in Tayside, Scotland, with LFTs performed in primary care were record-linked to secondary care and prescription databases to ascertain baseline characteristics, and to mortality data. Using this derivation cohort a survival model was developed to predict mortality. The model was assessed for calibration, discrimination (using the C-statistic) and performance, and validated using a separate cohort of Scottish primary care practices. Results: From the derivation cohort (n = 95 977), 2.7% died within one year. Predictors of mortality included: age; male gender; social deprivation; history of cancer, renal disease, stroke, ischaemic heart disease or respiratory disease; statin use; and LFTs (albumin, transaminase, alkaline phosphatase, bilirubin, and gamma-glutamyltransferase). The C-statistic for the final model was 0.82 (95% CI 0.80-0.84), and was similar in the validation cohort (n = 11 653) 0.86 (0.79-0.90). As an example of performance, for a 10% predicted probability cut-off, sensitivity = 52.8%, specificity = 94.0%, PPV = 21.0%, NPV = 98.5%. For the model without LFTs the respective values were 43.8%, 92.8%, 15.6%, 98.1%. Conclusions: The Algorithm for Liver Function Investigations (ALFI) is the first model to successfully estimate the probability of all-cause mortality in patients with no apparent liver disease having LFTs in primary care. While LFTs added to the model's discrimination and sensitivity, the clinical utility of ALFI remains to be established since LFTs did not improve an already high NPV for short term mortality and only modestly improved a very low PPV.
dc.relation.ispartofPLoS ONEen
dc.rightsCopyright © 2012 McLernon et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectRA Public aspects of medicineen
dc.subjectBiochemistry, Genetics and Molecular Biology(all)en
dc.titleThe utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI)en
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Population and Behavioural Science Divisionen
dc.description.statusPeer revieweden

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