BACKGROUND: 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 @entity14 surgery. METHODS: Data were obtained from consecutive @entity14 @entity1 undergoing laparoscopic surgery within the enhanced recovery after surgery (ERAS) program between 2002 and 2009 in a single center. The primary outcomes assessed were delayed discharge and readmission within a 30-day period. The data were analyzed using a multilayered perceptron neural network (MLPNN), and a prediction tools were created for each outcome. The results were compared with a conventional statistical method using logistic regression analysis. RESULTS: A total of 275 @entity5 @entity1 were included in the study. The median length of stay was 6  days (range 2-49  days) with 67 @entity1 (24.4  %) staying longer than 7  days. Thirty-four @entity1 (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 @entity5 . The MLPNN for readmission had an AUROC of 0.68. CONCLUSIONS: These results may plausibly suggest that ANN can be used to develop reliable outcome predictive tools in multifactorial intervention such as ERAS. Compliance with ERAS can reliably predict both delayed discharge and 30-day readmission following laparoscopic @entity14 surgery.

The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic XXXX surgery.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity5 :: ('MESH:D009369', 'Disease') :: ['rectal cancer', 'cancer'] @entity14 :: ('MESH:D015179', 'Disease') :: ['colorectal cancer']
@entity14 :: (MESH:D015179,Disease) :: ['colorectal cancer']
