PWE-266 The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery. (22nd June 2015)

• Record Type: Journal Article
• Authors: Francis, N; Luther, A; Salib, E; Allanby, L; Messenger, D; Allison, A; Smart, N; Ockrim, J
• Is Part Of: Gut. Volume 64(2015)Supplement 1
• Page Start: A329
• Page End: A329
• Publication Date: 2015-06-22
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2015-309861.712
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Introduction: Artificial neural networks (ANNs) can be used to develop predictive tools to enable the clinical decision-making process. This study aimed to investigate the use of an ANN in predicting the outcomes from enhanced recovery after colorectal cancer surgery. Method: Data was obtained from consecutive colorectal cancer patients undergoing laparoscopic surgery within the ERAS programme between 2002 and 2009 in a single centre. The primary outcomes assessed were delayed discharge and readmission within a 30 day period. The data was analysed using a Multi Layered Perceptron Neural Network (MLPNN), and a prediction tools were created for each outcomes. The results were compared with a conventional statistical method using Logistic regression analysis. Results: A total of 275 cancer patients were included in the study. The median length of stay was 6 days (range: 2–49 days) with 67 patients (24.4%) staying longer than 7 days. Thirty four patients (12.5%) were readmitted within 30 days. Important factors predicting delayed discharge were related to failure in compliance with ERAS, particularly with the postoperative elements in the first 48 h. The MLPNN for delayed discharge had an area under a receiver-operator characteristic curve (AUROC) of 0.817, compared with an AUROC of 0.807 for the predictive tool developed from logistic regression analysis. Factors predicting 30 day readmission included overall compliance with the ERAS pathway and receiving neoadjuvant treatment for rectal cancer. The MLPNN for readmission had an AUROC of 0.68. Conclusion: These results may plausibly suggest that ANN can be used to develop reliable outcome predictive tools in multi-factorial intervention such as ERAS. Compliance with ERAS can reliably predict both delayed discharge and 30 day readmission following laparoscopic colorectal cancer surgery. Disclosure of interest: None Declared. References: Ramesh AN, Kambhampati C, Monson JRT, Drew PJ, Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86:334–338 Bottaci L, Drew PJ, Hartley JE, Hadfield MB et al . Artficial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 1997;350 :470–2