Creation of an effective colorectal anastomotic leak early detection tool using an artificial neural network

International Journal of Colorectal Disease
Katie Adams, Savvas Papagrigoriadis

Abstract

Anastomotic leaks greatly increase both morbidity and mortality amongst colorectal patients. Earlier detection of leaks leads to improved patient outcomes; however, diagnosis often proves difficult due to heterogeneous presentation and varied differential diagnosis. The purpose of the study was to create an artificial neural network (ANN) capable of accurately identifying patients at risk of developing a post-operative colorectal anastomotic leak. A genetic ANN was trained and validated on a retrospective patient cohort. Two comparative groups were identified: those with anastomotic leaks confirmed at re-operation with a control group of patients with a post-operative delayed recovery, but in whom leak was excluded and no re-operation required. Seventy-six patients were identified: 20 confirmed leaks and 56 controls. No significant difference in the baseline features between leak and control groups in terms of age (leaks 65.9 years [SD 9.29] controls 58.3 years [SD 17.0)], P = 0.054). Utilising backwards variable selection, ANN maintained 19 input variables. Internal validation of the ANN produced a sensitivity of 85.0 %, specificity of 82.1 %, and AUC of 0.89 for correct identification of clinical anastomotic leaks. Of the 20 ...Continue Reading

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Citations

Jul 7, 2020·Colorectal Disease : the Official Journal of the Association of Coloproctology of Great Britain and Ireland·UNKNOWN PelvEx Collaborative

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