Risk Stratification of Postoperative Enteral Feeding Intolerance in Oral Cancer Patients

A single-center retrospective study of 752 patients undergoing radical resection with free flap reconstruction for oral cancer between 2017 and 2025. The dataset, created by Baolin Jia, includes 35 perioperative variables used to predict feeding intolerance, which occurred in 36.04% of patients.

Use Cases

• Training machine learning models for postoperative feeding intolerance risk based on 35 perioperative variables.
• Investigating the influence of metabolic-inflammatory factors like fasting glucose and ALI on surgical outcomes.
• Developing clinical decision support tools for individualized nutritional management after oral cancer reconstruction.
• Validating feature selection methods like LASSO regression on clinical datasets.
• Interpreting model predictions using SHAP analysis to identify key risk factors with nonlinear threshold effects.

Strengths

• Includes data from 752 patients, a substantial cohort for model development.
• Model achieved an AUC of 0.889 in validation, indicating strong predictive performance.
• Dataset is focused on a specific, high-risk surgical cohort (oral cancer free flap reconstruction).
• Uses 35 perioperative variables, suggesting a multi-factorial feature set.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single-center retrospective study, which may limit generalizability.

Provenance

Source

figshare

Collection Method

Single-center retrospective study.

Time Range

2017 to 2025

Freshness

Last updated 2026-06-04 04:27:20; freshness should be verified.