Enhanced Recovery After Surgery (ERAS) Pathway in Esophagectomy: Is a Reasonable Prediction of Hospital Stay Possible?

Ann Surg. 2019 Jul;270(1):77-83. doi: 10.1097/SLA.0000000000002775.


Objective: To assess whether perioperative variables or deviation from enhanced recovery after surgery (ERAS) items could be associated with delayed discharge after esophagectomy, and to convert them into a scoring system to predict it.

Summary background data: ERAS perioperative pathways have been recently applied to esophageal resections. However, low adherence to ERAS items and high rates of protocol deviations are often reported.

Methods: All patients who underwent esophagectomy between April 2012 and March 2017 were managed with a standardized perioperative pathway according to ERAS principles. The target length of stay was set at eighth postoperative day (POD). All significant variables at bivariate analysis were entered into a logistic regression to produce a predictive score. An initial validation of the score accuracy was carried out on a separate patient sample.

Results: Two hundred eighty-six patients were included in the study. Multivariate regression analysis showed that American Society of Anesthesiology score ≥ 3, surgery duration > 255 min, "nonhybrid" esophagectomy, and failure to mobilize patients within 24 h from surgery were associated with delayed discharge. The logistic regression model was statistically significant (P < 0.001) and correctly classified 81.9% of cases. The sensitivity was 96.6%, and the specificity was 17.6%. The prediction score applied to 23 patients correctly identified 100% of those discharged after eighth POD.

Conclusions: The results of this study seem to be clinically meaningful and in line with those from other studies. The initial validation revealed good predictive properties.

MeSH terms

  • Adult
  • Aged
  • Algorithms
  • Clinical Decision Rules*
  • Enhanced Recovery After Surgery / standards*
  • Esophagectomy*
  • Female
  • Guideline Adherence / statistics & numerical data*
  • Humans
  • Length of Stay / statistics & numerical data*
  • Logistic Models
  • Male
  • Middle Aged
  • Practice Guidelines as Topic
  • Retrospective Studies
  • Sensitivity and Specificity