Biomedical Informatics Insights

Enzo Grossi¹, Riccardo Marmo², Marco Intraligi³ and Massimo Buscema³

¹Medical Department Bracco Milano, Italy; Centro Diagnostico Italiano, Milano, Italy. ²Division of Gastroenterology, L.Curto Hospital, Polla, Sant’Arsenio, Italy. ³Semeion Research Centre, Rome, Italy.

Abstract

Background: Mortality for non variceal upper gastrointestinal bleeding (UGIB) is clinically relevant in the first 12–24 hours of the onset of haemorrhage and therefore identification of clinical factors predictive of the risk of death before endoscopic examination may allow for early corrective therapeutic intervention.

Aim: 1) Identify simple and early clinical variables predictive of the risk of death in patients with non variceal UGIB; 2) assess previsional gain of a predictive model developed with conventional statistics vs. that developed with artificial neural networks (ANNs).

Methods and results: Analysis was performed on 807 patients with nonvariceal UGIB (527 males, 280 females), as a part of a multicentre Italian study. The mortality was considered “bleeding-related” if occurred within 30 days from the index bleeding episode. A total of 50 independent variables were analysed, 49 of which clinico-anamnestic, all collected prior to endoscopic examination plus the haemoglobin value measured on admission in the emergency department. Death occurred in 42 (5.2%). Conventional statistical techniques (linear discriminant analysis) were compared with ANNs (Twist® system-Semeion) adopting the same result validation protocol with random allocation of the sample in training and testing subsets and subsequent cross-over. ANNs resulted to be significantly more accurate than LDA with an overall accuracy rate near to 90%.

Conclusion: Artificial neural networks technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of death for UGIB.

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