Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting

Lakshmana Ayaru, Petros-pavlos Ypsilantis, Abigail Nanapragasam, Ryan Chang-ho Choi, Anish Thillanathan, Lee Min-ho, Giovanni Montana, John Green (Editor)

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)

Abstract

Background: There are no widely used models in clinical care to predict outcome in acute lower gastrointestinal bleeding (ALGIB). If available these could help triage patients at presentation to appropriate levels of care/intervention and improve medical resource utilisation. We aimed to apply a state-of-the-art machine learning classifier, gradient boosting (GB), to predict outcome in ALGIB using non-endoscopic measurements as predictors. Methods: Non-endoscopic variables from patients with ALGIB attending the emergency departments of two teaching hospitals were analysed retrospectively for training/internal validation (n=170) and external validation (n=130) of the GB model. The performance of the GB algorithm in predicting recurrent bleeding, clinical intervention and severe bleeding was compared to a multiple logic regression (MLR) model and two published MLR-based prediction algorithms (BLEED and Strate prediction rule). Results: The GB algorithm had the best negative predictive values for the chosen outcomes (>88%). On internal validation the accuracy of the GB algorithm for predicting recurrent bleeding, therapeutic intervention and severe bleeding were (88%, 88% and 78% respectively) and superior to the BLEED classification (64%, 68% and 63%), Strate prediction rule (78%, 78%, 67%) and conventional MLR (74%, 74% 62%). On external validation the accuracy was similar to conventional MLR for recurrent bleeding (88% vs. 83%) and therapeutic intervention (91% vs. 87%) but superior for severe bleeding (83% vs. 71%). Conclusion: The gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models. These may be useful for risk stratification of patients on presentation to the emergency department.
Original languageEnglish
Article numbere0132485
Pages (from-to)e0132485
JournalPL o S One
Volume10
Issue number7
DOIs
Publication statusPublished - 14 Jul 2015

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