EP546 - A prediction model for enhanced recovery after surgery (ERAS) failure following laparoscopic colectomy

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Purpose/Background: The effectiveness of the enhanced recovery after surgery (ERAS) protocol in colorectal surgery has been well documented and is now widely used. Nonetheless, some patients fail to recover despite adherence to ERAS protocol. Identifying patients at risk of ERAS failure remains a challenge. This study aimed to develop a prediction model for ERAS failure following laparoscopic colectomy.

Methods/Interventions: A retrospective analysis was conducted on data from patients who underwent laparoscopic colectomy between September 2020 and February 2023. All patients were subjected to the ERAS protocol of our institution and were divided into two groups based on success or failure. ERAS failure was defined as intolerance of a soft diet on postoperative day (POD) 2, postoperative length of stay exceeding 7 days, or readmission within 30 days after surgery. Multivariate logistic regression analysis was employed to develop the prediction model.

Results/Outcomes: The study included 401 patients, with 45 (11.2%) experiencing ERAS failure. Body mass index (BMI), operative time, and C-reactive protein-to-albumin ratio (CAR) on POD 3 were identified as independent parameters affecting ERAS failure in multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves were generated for these parameters. The area under the curve (AUC) values for BMI, operative time, and CAR were 0.650, 0.652, and 0.701, respectively. Upon integrating the three parameters and plotting the ROC curve, the AUC value improved to 0.754.

Conclusion/Discussion: While BMI, operative time, and CAR exhibited predictive potential individually, their AUC values were relatively low. Combining these variables enhanced the prediction capability for ERAS failure following laparoscopic colectomy.