title: The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery.
abstract: 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 colorectal cancer surgery.
  
  
  METHODS
  Data were obtained from consecutive colorectal cancer patients 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 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.
  
  
  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 colorectal cancer surgery.
authors.affiliation: Department of General Surgery, Yeovil District Hospital, Higher Kingston, Yeovil, BA21 4AT, UK, nader.francis@ydh.nhs.uk.
authors.forename: N K
authors.initials: NK
authors.lastname: Francis
authors.forename: A
authors.initials: A
authors.lastname: Luther
authors.forename: E
authors.initials: E
authors.lastname: Salib
authors.forename: L
authors.initials: L
authors.lastname: Allanby
authors.forename: D
authors.initials: D
authors.lastname: Messenger
authors.forename: A S
authors.initials: AS
authors.lastname: Allison
authors.forename: N J
authors.initials: NJ
authors.lastname: Smart
authors.forename: J B
authors.initials: JB
authors.lastname: Ockrim
mesh_terms: D000359:Aftercare / Q000379:methods / Q000706:statistics & numerical data*; D019540:Area Under Curve; D003082:Colectomy / Q000009:adverse effects* / Q000379:methods / Q000534:rehabilitation; D015179:Colorectal Neoplasms / Q000601:surgery*; D005260:Female; D006801:Humans; D010535:Laparoscopy / Q000009:adverse effects* / Q000379:methods / Q000534:rehabilitation; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D020360:Neoadjuvant Therapy / Q000009:adverse effects / Q000706:statistics & numerical data; D016571:Neural Networks, Computer; D010349:Patient Compliance / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011446:Prospective Studies; D012189:Retrospective Studies; D013997:Time Factors