ArticlePloS one2012
The utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI).
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Review and Recommendations for the Component Tests in the Liver Function Test Profile.Indian journal of clinical biochemistry : IJCB · 2016Review
- Atherosclerosis and Liver Function Tests in Coronary Angiography Patients.The West Indian medical journal · 2015Article
- Reduced red blood cell count predicts poor survival after surgery in patients with primary liver cancer.Medicine · 2015Observational
- Ethnicity and the diagnosis gap in liver disease: a population-based study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2014Article
- Observational
- What is the best strategy for investigating abnormal liver function tests in primary care? Implications from a prospective study.BMJ open · 2013Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundAlthough 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.
methodsIn 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.
resultsFrom 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%.
conclusionsThe 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.
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