The present review discusses restrictive perioperative fluid protocols within enhanced recovery after surgery (ERAS) pathways. Standardized definitions of a restrictive or liberal fluid regimen are lacking since they depend on conflicting evidence, institutional protocols, and personal preferences. Challenges related to restrictive fluid protocols are related to proper patient selection within standardized ERAS protocols. On the other hand, invasive goal-directed fluid therapy (GDFT) is reserved for more challenging disease presentations and polymorbid and frail patients. While the perfusion rate (mL/kg/h) appears less predictive for postoperative outcomes, the authors identified critical thresholds related to total intravenous fluids and weight gain. These thresholds are discussed within the available evidence. The authors aim to introduce their institutional approach to standardized practice.

Background: The aim of the present study was to apply machine learning (ML) techniques to define clusters relating patient demographics, compliance, and outcome variables in colorectal enhanced recovery after surgery (ERAS) patients and improve data-driven, predictive decision-making. Methods: To uncover inherent patient subgroups from the data without pre-defined labels, the unsupervised K-means clustering algorithm was utilized. This technique was selected for its effectiveness in partitioning patients into distinct groups by iteratively assigning them to the nearest cluster mean, thereby minimizing within-cluster variance across key variables. The top five recovery goals and the top 10 clinical outcome variables were defined based on clinical considerations (incidence and importance). In a second step, the cluster transition was traced by monitoring the transitions between clusters from demographic through compliance to outcome variables. Results: A total of 1381 patients were available for final analysis, revealing three clusters (low risk, n = 490, 36%; intermediate risk, n = 157, 11%; and high risk, n = 734, 53%) for demographic, two clusters (high compliance, n = 1011, 73%, and low compliance n = 370, 27%) for perioperative, and two clusters (good and poor outcomes) for the top five recovery goals and the top 10 clinical outcomes, respectively. The cluster transition for the top five recovery goals and the top 10 clinical outcomes revealed that most patients (488/490, 99.6%) of the low-risk demographic cluster had high perioperative compliance, and over 90% of them had favorable functional and clinical outcomes. Of the 2/3 of intermediate risk patients who had poor perioperative compliance, over 40% had a poor functional recovery, whereas 83% had good clinical outcomes. Of the high-risk demographic group, 100% (734/734) had low perioperative compliance, and over 40% of them had poor functional recovery. Conclusions: This ML-based analysis of demographic, compliance, and recovery clusters and associated cluster transition allowed us to identify patient clusters as a first step to tailored ERAS protocols aiming to improve compliance and outcomes.

Background: The aim of the present study was to identify machine learning (ML)-driven risk constellations to predict specific postoperative outcomes and recovery targets to support the development of targeted interventions, optimized to individual risk profiles. Methods: This retrospective institutional cohort study included consecutive patients who underwent elective colorectal surgery over an 11-year study period focusing on baseline patient characteristics, intraoperative details, and postoperative complications. Five supervised classification algorithms for specific complications and recovery targets were used: logistic regression, support vector machine (SVM), XGBoost, decision tree, and gradient boosting. The dataset was randomly divided into training (80%) and testing (20%) subsets. Model performance was assessed using F1-score for classification task, and for each prediction target; the best-performing model and hyperparameter combination were selected based on validation results. To interpret the contribution of individual features to model predictions, SHAP (Shapley Additive exPlanations) analysis was applied to the best-performing model identified for each prediction target. Results: Logistic regression was the best-performing model for six of the seven postoperative outcomes, whereas SVM performed best for postoperative ileus, indicating that optimal model selection was outcome-specific. Overall, however, none of the tested models clearly outperformed the others in their predictive potential. The discriminative power of the models, as measured by ROC-AUC (receiver operating characteristics–area under the curve), was consistently higher than the baseline of 0.50, peaking at 0.93 for renal dysfunction. In terms of classification accuracy, the prediction of postoperative ileus was the most robust, yielding an F1-score of 0.87. For mobilization, the model showed balanced performance across F1-score (0.72), precision (0.74), and recall (0.71). Given the limited performance across all selected models, SHAP analysis was used to improve the interpretability of the predictions to better understand the contributions of each clinical variable. Conclusions: Complex models in the setting of a structured perioperative dataset only slightly outperform simpler approaches, highlighting the importance of data quality and feature richness rather than just increased model complexity. Future work on improving ML-based risk prediction strategies should thus ensure data quality and focus on integrating dynamic perioperative and postoperative measurements, exploring more flexible modeling frameworks, and validating model performance in larger external cohorts.