World journal of surgery
October 1, 2026 · via Europe PMC
Development of a Predictive Model for Patient Compliance in Enhanced Recovery After Surgery (ERAS) Programs for Colorectal Surgery.
Taha A, Mohandes B, Honaker MD, +5 more, Cattin PC.
AI summary — for doctors, generated from the abstract only
A machine-learning model predicted adherence to ERAS pathways in colorectal surgery, with surgical approach, ASA class and operative time as top predictors.
What was studied
A retrospective analysis of 1,119 colorectal surgery patients, comparing CatBoost, XGBoost and gradient boosting models for predicting compliance.
Key findings
- Overall ERAS compliance was 78%
- Minimally invasive surgery had higher compliance (83%) than open (67%)
- CatBoost performed best
- BMI, age and PONV prophylaxis were further predictors
Clinical relevance
Identifying patients at risk of low adherence may allow tailored perioperative support.
Who should care
Limitations
A single-setting retrospective model with internal cross-validation only and no linkage to clinical outcomes.
Expect lower ERAS compliance after open and prolonged operations, and support these patients proactively.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Enhanced recovery after surgery (ERAS) programs optimize perioperative care to reduce surgical stress and improve recovery. Compliance with ERAS pathways is essential for achieving favorable outcomes, yet predictors of adherence remain poorly understood. Using demographic, clinical, and procedural factors, this study aims to develop and validate a predictive model for perioperative compliance within ERAS protocols for colorectal surgery.
Methods
A retrospective analysis was performed, including all patients who were managed under the ERAS framework. The compliance model was built based on feature engineering, which involved scaling, polynomial interaction expansion, and Lasso-based variable selection. Three regression algorithms (CatBoost, XGBoost, and histogram-based gradient boosting) were compared using ten-fold cross-validation. Predictive performance was evaluated by root mean squared error (RMSE) and mean absolute error (MAE).
Results
Analysis was performed on 1119 patients who had colorectal surgery under ERAS pathways. The average hospital stay was 9.2 days, and the average operating time was 221.5 minutes. The ERAS compliance rate was 78% overall, with minimally invasive procedures having a higher compliance rate (83%) than open surgery (67%). The CatBoost model had the best performance with good precision. The strongest predictors of compliance, according to feature importance analysis, were surgical approach, ASA class, and operative duration. These were followed by BMI, age, and ERAS process measures such as PONV prophylaxis.
Conclusion
The newly developed compliance model will enable early identification of patients at risk of low adherence, thus supporting personalized perioperative management and improved recovery outcomes.
DOI
10.1002/wjs.70580
PMID
42817924
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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