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Beyond compliance: Patient risk, ERAS adherence, and postoperative outcomes through explainable machine learning.

J Ripollés-Melchor1, Á V Espinosa2, A Abad-Motos3

  • 1Department of Anaesthesia and Critical Care, Infanta Leonor University Hospital, Madrid, Spain; Complutense University of Madrid, Madrid, Spain; Fluid Therapy and Hemodynamic Monitoring Working Group, Spanish Society of Anaesthesia and Critical Care, Madrid, Spain.

Revista Espanola De Anestesiologia Y Reanimacion
|January 9, 2026
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Summary

Enhanced Recovery After Surgery (ERAS) protocols show variable adherence. Machine learning identified patient frailty and specific ERAS items as key predictors of complications, enabling tailored surgical recovery strategies.

Keywords:
Aprendizaje automatizadoCirugía colorrectalColorectal surgeryComplicaciones postoperatoriasEnhanced recovery after surgeryFragilidadFrailtyMachine learningMejora de la recuperación tras la cirugíaPostoperative complications

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Area of Science:

  • Colorectal Surgery
  • Surgical Outcomes
  • Machine Learning in Medicine

Background:

  • Enhanced Recovery After Surgery (ERAS) protocols are crucial for improving outcomes in colorectal surgery.
  • Variable adherence to ERAS protocols and patient-specific risk factors can impact surgical outcomes.
  • Traditional compliance metrics lack the granularity to analyze the interplay between ERAS adherence and patient risk.

Purpose of the Study:

  • To quantify the impact of individual ERAS components and clinical factors on postoperative complications using interpretable machine learning.
  • To identify distinct patient phenotypes based on ERAS adherence and clinical characteristics.
  • To develop data-driven insights for optimizing ERAS strategies in colorectal surgery.

Main Methods:

  • Secondary analysis of the prospective EuroPOWER cohort (NCT04889798) involving 2,841 adult patients undergoing elective colorectal surgery.
  • Development and interpretation of two Extreme Gradient Boosting models using Shapley Additive Explanations (SHAP) to predict in-hospital complications.
  • Clustering of SHAP matrices from a comprehensive model (clinical variables + ERAS items) to derive patient phenotypes.

Main Results:

  • The comprehensive model demonstrated good predictive performance (AUC 0.627).
  • Key predictors of complications included patient frailty, ASA class, BMI, and age, alongside ERAS components like early mobilization and nutritional care.
  • Three distinct patient phenotypes (robust, intermediate, frail) were identified with varying complication rates (17.7% to 41.1%).

Conclusions:

  • ERAS adherence's effectiveness is influenced by patient baseline vulnerability and protocol implementation.
  • SHAP-based models offer transparent risk assessment and enable the identification of patient phenotypes.
  • These findings support the development of personalized ERAS strategies and automated quality monitoring tools for surgical care.