A predictive Bayesian network that risk stratifies patients undergoing Barrett's surveillance for personalized risk of developing malignancy.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2020
2020
Historique:
received:
19
03
2020
accepted:
29
09
2020
entrez:
12
10
2020
pubmed:
13
10
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Barrett's esophagus is strongly associated with esophageal adenocarcinoma. Considering costs and risks associated with invasive surveillance endoscopies better methods of risk stratification are required to assist decision-making and move toward more personalised tailoring of Barrett's surveillance. A Bayesian network was created by synthesizing data from published studies analysing risk factors for developing adenocarcinoma in Barrett's oesophagus through a two-stage weighting process. Data was synthesized from 114 studies (n = 394,827) to create the Bayesian network, which was validated against a prospectively maintained institutional database (n = 571). Version 1 contained 10 variables (dysplasia, gender, age, Barrett's segment length, statin use, proton pump inhibitor use, BMI, smoking, aspirin and NSAID use) and achieved AUC of 0.61. Version 2 contained 4 variables with the strongest evidence of association with the development of adenocarcinoma in Barrett's (dysplasia, gender, age, Barrett's segment length) and achieved an AUC 0.90. This Bayesian network is unique in the way it utilizes published data to translate the existing empirical evidence surrounding the risk of developing adenocarcinoma in Barrett's esophagus to make personalized risk predictions. Further work is required but this tool marks a vital step towards delivering a more personalized approach to Barrett's surveillance.
Sections du résumé
BACKGROUND
Barrett's esophagus is strongly associated with esophageal adenocarcinoma. Considering costs and risks associated with invasive surveillance endoscopies better methods of risk stratification are required to assist decision-making and move toward more personalised tailoring of Barrett's surveillance.
METHODS
A Bayesian network was created by synthesizing data from published studies analysing risk factors for developing adenocarcinoma in Barrett's oesophagus through a two-stage weighting process.
RESULTS
Data was synthesized from 114 studies (n = 394,827) to create the Bayesian network, which was validated against a prospectively maintained institutional database (n = 571). Version 1 contained 10 variables (dysplasia, gender, age, Barrett's segment length, statin use, proton pump inhibitor use, BMI, smoking, aspirin and NSAID use) and achieved AUC of 0.61. Version 2 contained 4 variables with the strongest evidence of association with the development of adenocarcinoma in Barrett's (dysplasia, gender, age, Barrett's segment length) and achieved an AUC 0.90.
CONCLUSION
This Bayesian network is unique in the way it utilizes published data to translate the existing empirical evidence surrounding the risk of developing adenocarcinoma in Barrett's esophagus to make personalized risk predictions. Further work is required but this tool marks a vital step towards delivering a more personalized approach to Barrett's surveillance.
Identifiants
pubmed: 33045017
doi: 10.1371/journal.pone.0240620
pii: PONE-D-20-06612
pmc: PMC7549831
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0240620Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Am J Epidemiol. 2008 Aug 1;168(3):237-49
pubmed: 18550563
Child Dev. 2014 May-Jun;85(3):842-860
pubmed: 24116396
Curr Probl Surg. 2012 Dec;49(12):731-95
pubmed: 23131540
Gastroenterology. 1996 Feb;110(2):614-21
pubmed: 8566611
PLoS One. 2019 Sep 9;14(9):e0222270
pubmed: 31498836
Dysphagia. 1993;8(3):276-88
pubmed: 8359051
Am J Gastroenterol. 2016 Jan;111(1):30-50; quiz 51
pubmed: 26526079
Risk Anal. 2013 Jul;33(7):1293-311
pubmed: 23106231
JAMA. 2017 Nov 7;318(17):1649-1650
pubmed: 29052713
J Biomed Inform. 2007 Dec;40(6):609-18
pubmed: 17704008
JAMA Oncol. 2015 Oct;1(7):879-80
pubmed: 26181886
J Biomed Inform. 2011 Oct;44(5):859-68
pubmed: 21642013
N Engl J Med. 2017 Sep 28;377(13):1209-1211
pubmed: 28953443
Open Med. 2009;3(3):e123-30
pubmed: 21603045
BMC Gastroenterol. 2019 Jun 27;19(1):109
pubmed: 31248371
JAMA. 2016 Oct 25;316(16):1659-1660
pubmed: 27669484