The utility of liver function tests for mortality prediction within one year in primary care using the Algorithm for Liver Function Investigations (ALFI)

David J McLernon, John F Dillon, Frank M Sullivan, Paul Roderick, William M Rosenberg, Stephen D Ryder, Peter T Donnan

Research output: Contribution to journalArticle

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Abstract

Background: 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.
Original languageEnglish
Article numbere50965
Number of pages11
JournalPloS ONE
Volume7
Issue number12
DOIs
Publication statusPublished - 14 Dec 2012

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Liver Function Tests
liver function
Liver
Primary Health Care
prediction
Mortality
Liver Diseases
liver diseases
testing
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Secondary Care
statistics
gamma-Glutamyltransferase
Kidney Neoplasms
Scotland
kidney neoplasms
Transaminases
Bilirubin
general practitioners
Biochemistry

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The utility of liver function tests for mortality prediction within one year in primary care using the Algorithm for Liver Function Investigations (ALFI). / McLernon, David J; Dillon, John F; Sullivan, Frank M; Roderick, Paul; Rosenberg, William M; Ryder, Stephen D; Donnan, Peter T.

In: PloS ONE, Vol. 7, No. 12, e50965, 14.12.2012.

Research output: Contribution to journalArticle

McLernon, David J ; Dillon, John F ; Sullivan, Frank M ; Roderick, Paul ; Rosenberg, William M ; Ryder, Stephen D ; Donnan, Peter T. / The utility of liver function tests for mortality prediction within one year in primary care using the Algorithm for Liver Function Investigations (ALFI). In: PloS ONE. 2012 ; Vol. 7, No. 12.
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abstract = "Background: 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.",
author = "McLernon, {David J} and Dillon, {John F} and Sullivan, {Frank M} and Paul Roderick and Rosenberg, {William M} and Ryder, {Stephen D} and Donnan, {Peter T}",
note = "Acknowledgments The authors wish to thank Alison Bell from HIC for extracting and anonymising all the datasets used. We thank the Primary Care Clinical Informatics Unit for providing the Practice Team Initiative cohort for external validation of our model. Department of Health Disclaimer: The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Department of Health.",
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T1 - The utility of liver function tests for mortality prediction within one year in primary care using the Algorithm for Liver Function Investigations (ALFI)

AU - McLernon, David J

AU - Dillon, John F

AU - Sullivan, Frank M

AU - Roderick, Paul

AU - Rosenberg, William M

AU - Ryder, Stephen D

AU - Donnan, Peter T

N1 - Acknowledgments The authors wish to thank Alison Bell from HIC for extracting and anonymising all the datasets used. We thank the Primary Care Clinical Informatics Unit for providing the Practice Team Initiative cohort for external validation of our model. Department of Health Disclaimer: The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Department of Health.

PY - 2012/12/14

Y1 - 2012/12/14

N2 - Background: 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.

AB - Background: 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.

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