A decision support system to facilitate management of patients with acute gastrointestinal bleeding.
OBJECTIVE
To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce healthcare resources to those who need it the most.


METHODS
Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves.


RESULTS
Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model.


CONCLUSIONS
While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation.
Chu
Adrienne
A
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, United States.
Ahn
Hongshik
H
Halwan
Bhawna
B
Kalmin
Bruce
B
Artifon
Everson L A
EL
Barkun
Alan
A
Lagoudakis
Michail G
MG
Kumar
Atul
A
D000208:Acute Disease; D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D004638:Emergency Treatment; D016099:Endoscopy, Gastrointestinal; D006471:Gastrointestinal Hemorrhage / Q000175:diagnosis / Q000628:therapy; D006305:Health Status Indicators; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008954:Models, Biological; D016571:Neural Networks, Computer; D018579:Patient Selection; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment