Factors predicting 30-day readmission after laparoscopic colorectal cancer surgery within an enhanced recovery programme.

AIM: Hospital readmission within 30 days of surgery has become a marker of poor quality patient care. This study aimed to investigate factors predictive of 30-day readmission after laparoscopic colorectal cancer surgery within an enhanced recovery after surgery (ERAS) programme. METHOD: Consecutive patients undergoing laparoscopic surgery for colorectal cancer within an ERAS programme between 2002 and 2009 were included. Data were collected relating to patient demographics, neoadjuvant chemoradiotherapy, ERAS compliance, and operative and postoperative outcomes. A logistic regression model was used to identify factors associated with readmissions after adjusting for the potential effect of covariables simultaneously. RESULTS: In all, 268 cancer patients underwent laparoscopic colorectal surgery (108 rectal resections), of whom 34 (12.7%) were readmitted due most commonly to bowel obstruction (29%) and surgical site infection (18%). The use of neoadjuvant therapy (odds ratio 4.49, 95% CI 1.41-14.35; P = 0.011) and ERAS compliance above 93% (odds ratio 0.38, 95% CI 0.18-0.84; P = 0.016) were independent predictors of readmission. CONCLUSION: Poor ERAS compliance and preoperative chemoradiotherapy were significant predictors of readmission following laparoscopic colorectal cancer surgery. Further research is required to expand the scope of ERAS beyond hospital discharge.

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

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.