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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)

The utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI)
The utility of liver function tests for mortality prediction within one year in primary care using the algorithm for liver function investigations (ALFI)
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.
1932-6203
e50965
McLernon, D.J.
ce62b6ef-cafc-4c67-b49e-c516447e57b4
Dillon, J.F.
016932d3-aa65-48cb-bfb7-4aefa2dce589
Sullivan, F.M.
387fe33d-6c51-4d0a-9a2a-ee6f93404a59
Roderick, P.
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Rosenberg, W.M.
8558a866-4b74-4f3f-9802-774a8a82d82a
Ryder, S.D.
9f81c6dd-a5ca-45a3-9357-06d8462c89d1
Donnan, P.T.
16213cea-03c2-42ff-b625-ef93914af08b
McLernon, D.J.
ce62b6ef-cafc-4c67-b49e-c516447e57b4
Dillon, J.F.
016932d3-aa65-48cb-bfb7-4aefa2dce589
Sullivan, F.M.
387fe33d-6c51-4d0a-9a2a-ee6f93404a59
Roderick, P.
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Rosenberg, W.M.
8558a866-4b74-4f3f-9802-774a8a82d82a
Ryder, S.D.
9f81c6dd-a5ca-45a3-9357-06d8462c89d1
Donnan, P.T.
16213cea-03c2-42ff-b625-ef93914af08b

McLernon, D.J., Dillon, J.F., Sullivan, F.M., Roderick, P., Rosenberg, W.M., Ryder, S.D. and 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, 7 (12), e50965. (doi:10.1371/journal.pone.0050965). (PMID:23272082)

Record type: Article

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.

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Published date: 14 December 2012
Organisations: Primary Care & Population Sciences

Identifiers

Local EPrints ID: 350106
URI: http://eprints.soton.ac.uk/id/eprint/350106
ISSN: 1932-6203
PURE UUID: 72c8142c-c3db-42ef-908c-d953f25e97be
ORCID for P. Roderick: ORCID iD orcid.org/0000-0001-9475-6850

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Date deposited: 18 Mar 2013 15:13
Last modified: 21 Nov 2021 02:42

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Contributors

Author: D.J. McLernon
Author: J.F. Dillon
Author: F.M. Sullivan
Author: P. Roderick ORCID iD
Author: W.M. Rosenberg
Author: S.D. Ryder
Author: P.T. Donnan

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