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Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data
OBJECTIVES: Most patients are unaware they have liver cirrhosis until they present with a decompensating event. We therefore aimed to develop and validate an algorithm to predict advanced liver disease (AdvLD) using data widely available in primary care. DESIGN, SETTING AND PARTICIPANTS: Logistic re...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925927/ https://www.ncbi.nlm.nih.gov/pubmed/33574154 http://dx.doi.org/10.1136/bmjopen-2020-044952 |
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author | Hydes, Theresa Moore, Michael Stuart, Beth Kim, Miranda Su, Fangzhong Newell, Colin Cable, David Hales, Alan Sheron, Nick |
author_facet | Hydes, Theresa Moore, Michael Stuart, Beth Kim, Miranda Su, Fangzhong Newell, Colin Cable, David Hales, Alan Sheron, Nick |
author_sort | Hydes, Theresa |
collection | PubMed |
description | OBJECTIVES: Most patients are unaware they have liver cirrhosis until they present with a decompensating event. We therefore aimed to develop and validate an algorithm to predict advanced liver disease (AdvLD) using data widely available in primary care. DESIGN, SETTING AND PARTICIPANTS: Logistic regression was performed on routinely collected blood result data from the University Hospital Southampton (UHS) information systems for 16 967 individuals who underwent an upper gastrointestinal endoscopy (2005–2016). Data were used to create a model aimed at detecting AdvLD: ‘CIRRhosis Using Standard tests’ (CIRRUS). Prediction of a first serious liver event (SLE) was then validated in two cohorts of 394 253 (UHS: primary and secondary care) and 183 045 individuals (Care and Health Information Exchange (CHIE): primary care). PRIMARY OUTCOME MEASURES: Model creation dataset: cirrhosis or portal hypertension. Validation datasets: SLE (gastro-oesophageal varices, liver-related ascites or cirrhosis). RESULTS: In the model creation dataset, 931 SLEs were recorded (5.5%). CIRRUS detected cirrhosis or portal hypertension with an area under the curve (AUC) of 0.90 (95% CI 0.88 to 0.92). Overall, 3044 (0.8%) and 1170 (0.6%) SLEs were recorded in the UHS and CHIE validation cohorts, respectively. In the UHS cohort, CIRRUS predicted a first SLE within 5 years with an AUC of 0.90 (0.89 to 0.91) continuous, 0.88 (0.87 to 0.89) categorised (crimson, red, amber, green grades); and AUC 0.84 (0.82 to 0.86) and 0.83 (0.81 to 0.85) for the CHIE cohort. In patients with a specified liver risk factor (alcohol, diabetes, viral hepatitis), a crimson/red cut-off predicted a first SLE with a sensitivity of 72%/59%, specificity 87%/93%, positive predictive value 26%/18% and negative predictive value 98%/99% for the UHS/CHIE validation cohorts, respectively. CONCLUSION: Identification of individuals at risk of AdvLD within primary care using routinely available data may provide an opportunity for earlier intervention and prevention of liver-related morbidity and mortality. |
format | Online Article Text |
id | pubmed-7925927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-79259272021-03-19 Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data Hydes, Theresa Moore, Michael Stuart, Beth Kim, Miranda Su, Fangzhong Newell, Colin Cable, David Hales, Alan Sheron, Nick BMJ Open Gastroenterology and Hepatology OBJECTIVES: Most patients are unaware they have liver cirrhosis until they present with a decompensating event. We therefore aimed to develop and validate an algorithm to predict advanced liver disease (AdvLD) using data widely available in primary care. DESIGN, SETTING AND PARTICIPANTS: Logistic regression was performed on routinely collected blood result data from the University Hospital Southampton (UHS) information systems for 16 967 individuals who underwent an upper gastrointestinal endoscopy (2005–2016). Data were used to create a model aimed at detecting AdvLD: ‘CIRRhosis Using Standard tests’ (CIRRUS). Prediction of a first serious liver event (SLE) was then validated in two cohorts of 394 253 (UHS: primary and secondary care) and 183 045 individuals (Care and Health Information Exchange (CHIE): primary care). PRIMARY OUTCOME MEASURES: Model creation dataset: cirrhosis or portal hypertension. Validation datasets: SLE (gastro-oesophageal varices, liver-related ascites or cirrhosis). RESULTS: In the model creation dataset, 931 SLEs were recorded (5.5%). CIRRUS detected cirrhosis or portal hypertension with an area under the curve (AUC) of 0.90 (95% CI 0.88 to 0.92). Overall, 3044 (0.8%) and 1170 (0.6%) SLEs were recorded in the UHS and CHIE validation cohorts, respectively. In the UHS cohort, CIRRUS predicted a first SLE within 5 years with an AUC of 0.90 (0.89 to 0.91) continuous, 0.88 (0.87 to 0.89) categorised (crimson, red, amber, green grades); and AUC 0.84 (0.82 to 0.86) and 0.83 (0.81 to 0.85) for the CHIE cohort. In patients with a specified liver risk factor (alcohol, diabetes, viral hepatitis), a crimson/red cut-off predicted a first SLE with a sensitivity of 72%/59%, specificity 87%/93%, positive predictive value 26%/18% and negative predictive value 98%/99% for the UHS/CHIE validation cohorts, respectively. CONCLUSION: Identification of individuals at risk of AdvLD within primary care using routinely available data may provide an opportunity for earlier intervention and prevention of liver-related morbidity and mortality. BMJ Publishing Group 2021-02-11 /pmc/articles/PMC7925927/ /pubmed/33574154 http://dx.doi.org/10.1136/bmjopen-2020-044952 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Gastroenterology and Hepatology Hydes, Theresa Moore, Michael Stuart, Beth Kim, Miranda Su, Fangzhong Newell, Colin Cable, David Hales, Alan Sheron, Nick Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data |
title | Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data |
title_full | Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data |
title_fullStr | Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data |
title_full_unstemmed | Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data |
title_short | Can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using UK primary and secondary care data |
title_sort | can routine blood tests be modelled to detect advanced liver disease in the community: model derivation and validation using uk primary and secondary care data |
topic | Gastroenterology and Hepatology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925927/ https://www.ncbi.nlm.nih.gov/pubmed/33574154 http://dx.doi.org/10.1136/bmjopen-2020-044952 |
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