A decision support system to facilitate management of patients with acute gastrointestinal bleeding
Introduction
Acute gastrointestinal bleeding (GIB) continues to be a significant healthcare problem due to rising NSAID use and an aging population [1]. Further reductions in mortality will most likely require introduction of novel strategies to aid identification of the cohort requiring aggressive resuscitation and endoscopic intervention to prevent complications and death from ongoing bleeding [2], [3]. Delays in intervention usually result from failure to adequately recognize the source and severity of the bleed. Although several models and scores have been developed to risk-stratify patients, no single model aids the identification of the subgroup of patients presenting with acute upper or lower GIB that would most likely benefit from urgent intervention [4], [5]. Our goal was to utilize mathematical models to formulate a decision support system utilizing clinical and laboratory information available within a few hours of patient presentation to predict the source, need for intervention and disposition in patients with acute upper, mid- and lower GIB.
There are a number of well-known classification tools for analyzing data, including artificial neural networks (ANN), k-nearest neighbor (kNN), decision trees, support vector machines (SVM), and shrunken centroid (SC) [6], [7]. These classification tools are supervised learning methods where the algorithm learns from a training set and establishes a prediction rule to classify new samples using statistical approaches for class prediction. Logistic regression, an alternative to these classification methods, and Fisher's linear discriminant analysis (LDA) are both parametric approaches. Logistic regression and LDA do not differ in functional form, and differ only in the estimation of coefficients; LDA assumes normal distribution of the explanatory variables, while logistic regression does not. If the Gaussian assumptions are met, then LDA is a more powerful and efficient model than logistic regression. Standard statistical model building relies on an a priori collection of predictor variables for identifying outcomes of clinical interest. Often it may be computationally impossible for standard models to overcome the complexities of problems with large dimensionality. Support vector machines (SVM), introduced by Vapnik [6] can overcome the high dimensionality problem computationally and is consistently a good classifier and hence widely utilized as a classification method. Following the recent introduction of ensemble-voting approaches, two ensemble voting methods, boosting and bagging, have also gained wide popularity [8], [9]. An ensemble uses the predictions of multiple base classifiers through majority voting. Boosting, a meta-classifier, combines weak classifiers and takes a weighted majority vote of their predictors. Breiman [10] developed the random forest (RF) method by combining classification tree predictors. The bagging algorithm in RF uses bootstrap samples to build base trees. Each bootstrap sample is formed by randomly sampling, with replacement, the same number of observations as the training set. The final classification produced by the ensemble of these base classifiers is obtained using equal weight voting.
The study objective was to develop and compare the performance of eight classification models as described above to predict clinical outcomes in patients presenting with acute GIB.
Section snippets
Definitions
Upper GIB refers to gastrointestinal blood loss whose origin is proximal to the ligament of Treitz; mid-GIB whose source was below the ligament of Trietz but proximal to the ileocecal valve and lower GIB referred to gastrointestinal blood loss emanating from a source distal to the ileocecal valve [11]. Acute GIB was defined as bleeding of less than 5 days duration. Acute upper GIB was diagnosed if there was hematemesis, “coffee ground” colored emesis, the return of red blood via a nasogastric
Models
Table 1, Table 2, Table 3, Table 4 summarize the results for each outcome prediction variable for the evaluation step. Eight models were run (only six of the eight models were utilized to predict source, since logistic regression and boosting can be only used for two-way classification problems (while output source included three outcomes-upper, mid and lower) using the primary approach. Figure 2, Figure 3, Figure 4, Figure 5 depict the accuracies obtained from each individual model and each
Discussion and conclusion
Although superior healthcare outcomes may be expected if gastroenterologists manage all patients with acute GIB [32], it is logistically impossible for every patient with acute GIB to be emergently evaluated and treated by a gastroenterologist, as the onset of acute GIB is unpredictable. It is also impractical and economically unjustifiable to subject every patient with acute GIB to intensive resuscitation and urgent endoscopy as only 20% of patients with acute GIB may require urgent
Acknowledgement
The study was funded by 2005 Research and Outcomes Effectiveness Awards of the American Society of Gastrointestinal Endoscopy (ASGE) to Atul Kumar.
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These authors contributed equally to this study.