Development and validation of machine
learning models to predict the need
for haemostatic therapy in acute upper
gastrointestinal bleeding
learning models to predict the need
for haemostatic therapy in acute upper
gastrointestinal bleeding
Author(s)
Type
Journal Article
Abstract
Background:
Acute upper gastrointestinal bleeding (AUGIB) is a major cause of morbidity and mortality. This presentation however is not universally high risk as only 20–30% of bleeds require urgent haemostatic therapy. Nevertheless, the current standard of care is for all patients admitted to an inpatient bed to undergo endoscopy within 24 h for risk stratification which is invasive, costly and difficult to achieve in routine clinical practice.
Objectives:
To develop novel non-endoscopic machine learning models for AUGIB to predict the need for haemostatic therapy by endoscopic, radiological or surgical intervention.
Design:
A retrospective cohort study
Method:
We analysed data from patients admitted with AUGIB to hospitals from 2015 to 2020 (n = 970). Machine learning models were internally validated to predict the need for haemostatic therapy. The performance of the models was compared to the Glasgow-Blatchford score (GBS) using the area under receiver operating characteristic (AUROC) curves.
Results:
The random forest classifier [AUROC 0.84 (0.80–0.87)] had the best performance and was superior to the GBS [AUROC 0.75 (0.72–0.78), p < 0.001] in predicting the need for haemostatic therapy in patients with AUGIB. A GBS cut-off of ⩾12 was associated with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.74, 0.49, 0.81, 0.41 and 0.85, respectively. The Random Forrest model performed better with an accuracy, sensitivity, specificity, PPV and NPV of 0.82, 0.54, 0.90, 0.60 and 0.88, respectively.
Conclusion:
We developed and validated a machine learning algorithm with high accuracy and specificity in predicting the need for haemostatic therapy in AUGIB. This could be used to risk stratify high-risk patients to urgent endoscopy.
Acute upper gastrointestinal bleeding (AUGIB) is a major cause of morbidity and mortality. This presentation however is not universally high risk as only 20–30% of bleeds require urgent haemostatic therapy. Nevertheless, the current standard of care is for all patients admitted to an inpatient bed to undergo endoscopy within 24 h for risk stratification which is invasive, costly and difficult to achieve in routine clinical practice.
Objectives:
To develop novel non-endoscopic machine learning models for AUGIB to predict the need for haemostatic therapy by endoscopic, radiological or surgical intervention.
Design:
A retrospective cohort study
Method:
We analysed data from patients admitted with AUGIB to hospitals from 2015 to 2020 (n = 970). Machine learning models were internally validated to predict the need for haemostatic therapy. The performance of the models was compared to the Glasgow-Blatchford score (GBS) using the area under receiver operating characteristic (AUROC) curves.
Results:
The random forest classifier [AUROC 0.84 (0.80–0.87)] had the best performance and was superior to the GBS [AUROC 0.75 (0.72–0.78), p < 0.001] in predicting the need for haemostatic therapy in patients with AUGIB. A GBS cut-off of ⩾12 was associated with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.74, 0.49, 0.81, 0.41 and 0.85, respectively. The Random Forrest model performed better with an accuracy, sensitivity, specificity, PPV and NPV of 0.82, 0.54, 0.90, 0.60 and 0.88, respectively.
Conclusion:
We developed and validated a machine learning algorithm with high accuracy and specificity in predicting the need for haemostatic therapy in AUGIB. This could be used to risk stratify high-risk patients to urgent endoscopy.
Date Issued
2024
Date Acceptance
2024-03-27
Citation
Therapeutic Advances in Gastrointestinal Endoscopy, 2024, 17
ISSN
2631-7745
Publisher
SAGE Publishing
Journal / Book Title
Therapeutic Advances in Gastrointestinal Endoscopy
Volume
17
Copyright Statement
© The Author(s), 2024.
Article reuse guidelines:
sagepub.com/journals-permissions. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License
(https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission
provided the original work is attributed as specified on the Sage and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Article reuse guidelines:
sagepub.com/journals-permissions. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License
(https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission
provided the original work is attributed as specified on the Sage and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
License URL
Identifier
https://journals.sagepub.com/doi/10.1177/26317745241246899
Publication Status
Published
Date Publish Online
2024-05-05