Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment
Background and study aims Efficacy and adverse events probabilities influence decisions regarding the best options to manage patients with gastric superficial lesions. We aimed at developing a Bayesian model to individualize the prediction of outcomes after gastric endoscopic submucosal dissection (...
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Georg Thieme Verlag KG
2017-06-01
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doaj-bf6a41df19914f28ba4d529d96f534802020-11-25T01:20:25ZengGeorg Thieme Verlag KGEndoscopy International Open2364-37222196-97362017-06-010507E563E57210.1055/s-0043-106576Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessmentDiogo Libânio0Mário Dinis-Ribeiro1Pedro Pimentel-Nunes2Cláudia Camila Dias3Pedro Pereira Rodrigues4Gastroenterology Department, Instituto Português de Oncologia do Porto, Porto, PortugalGastroenterology Department, Instituto Português de Oncologia do Porto, Porto, PortugalGastroenterology Department, Instituto Português de Oncologia do Porto, Porto, PortugalCINTESIS - Center for Health Technology and Services Research, Faculty of Medicine of the University of Porto, Porto, PortugalCINTESIS - Center for Health Technology and Services Research, Faculty of Medicine of the University of Porto, Porto, PortugalBackground and study aims Efficacy and adverse events probabilities influence decisions regarding the best options to manage patients with gastric superficial lesions. We aimed at developing a Bayesian model to individualize the prediction of outcomes after gastric endoscopic submucosal dissection (ESD). Patients and methods Data from 245 gastric ESD were collected, including patient and lesion factors. The two endpoints were curative resection and post-procedural bleeding (PPB). Logistic regression and Bayesian networks were built for each outcome; their predictive value was evaluated in-sample and validated through leave-one-out and cross-validation. Clinical decision support was enhanced by the definition of risk matrices, direct use of Bayesian inference software and by a developed online platform. Results ESD was curative in 85.3 % and PPB occurred in 7.7 % of patients. In univariate analysis, male sex, ASA status, carcinoma histology, polypoid or depressed morphology, and lesion size ≥ 20 mm were associated with non-curative resection, while ASA status, antithrombotics and lesion size ≥ 20 mm were associated with PPB. Naïve Bayesian models presented AUROCs of ~80 % in the derivation cohort and ≥ 74 % in cross-validation for both outcomes. Risk matrices were computed, showing that lesions with cancer at biopsies, ≥ 20 mm, proximal or in the middle third, and polypoid are more prone to non-curative resection. PPB risk was < 5 % in lesions < 20 mm in the absence of antithrombotics. Conclusions The derived Bayesian model presented good discriminative power in the prediction of ESD outcomes and can be used to predict individualized probabilities, improving patient information and supporting clinical and management decisions.http://www.thieme-connect.de/DOI/DOI?10.1055/s-0043-106576 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Diogo Libânio Mário Dinis-Ribeiro Pedro Pimentel-Nunes Cláudia Camila Dias Pedro Pereira Rodrigues |
spellingShingle |
Diogo Libânio Mário Dinis-Ribeiro Pedro Pimentel-Nunes Cláudia Camila Dias Pedro Pereira Rodrigues Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment Endoscopy International Open |
author_facet |
Diogo Libânio Mário Dinis-Ribeiro Pedro Pimentel-Nunes Cláudia Camila Dias Pedro Pereira Rodrigues |
author_sort |
Diogo Libânio |
title |
Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment |
title_short |
Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment |
title_full |
Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment |
title_fullStr |
Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment |
title_full_unstemmed |
Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment |
title_sort |
predicting outcomes of gastric endoscopic submucosal dissection using a bayesian approach: a step for individualized risk assessment |
publisher |
Georg Thieme Verlag KG |
series |
Endoscopy International Open |
issn |
2364-3722 2196-9736 |
publishDate |
2017-06-01 |
description |
Background and study aims Efficacy and adverse events probabilities influence decisions regarding the best options to manage patients with gastric superficial lesions. We aimed at developing a Bayesian model to individualize the prediction of outcomes after gastric endoscopic submucosal dissection (ESD).
Patients and methods Data from 245 gastric ESD were collected, including patient and lesion factors. The two endpoints were curative resection and post-procedural bleeding (PPB). Logistic regression and Bayesian networks were built for each outcome; their predictive value was evaluated in-sample and validated through leave-one-out and cross-validation. Clinical decision support was enhanced by the definition of risk matrices, direct use of Bayesian inference software and by a developed online platform.
Results ESD was curative in 85.3 % and PPB occurred in 7.7 % of patients. In univariate analysis, male sex, ASA status, carcinoma histology, polypoid or depressed morphology, and lesion size ≥ 20 mm were associated with non-curative resection, while ASA status, antithrombotics and lesion size ≥ 20 mm were associated with PPB. Naïve Bayesian models presented AUROCs of ~80 % in the derivation cohort and ≥ 74 % in cross-validation for both outcomes. Risk matrices were computed, showing that lesions with cancer at biopsies, ≥ 20 mm, proximal or in the middle third, and polypoid are more prone to non-curative resection. PPB risk was < 5 % in lesions < 20 mm in the absence of antithrombotics.
Conclusions The derived Bayesian model presented good discriminative power in the prediction of ESD outcomes and can be used to predict individualized probabilities, improving patient information and supporting clinical and management decisions. |
url |
http://www.thieme-connect.de/DOI/DOI?10.1055/s-0043-106576 |
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