Data Sheet 1_Development and validation of a CT-based body composition model for predictin

270 patient records from a retrospective cohort study at the First Hospital of Shanxi Medical University between January 2018 and December 2024. The dataset was used to develop and validate a multidimensional nomogram predicting postoperative sepsis, ICU admission, and complications in small bowel obstruction patients. It was authored by Yanan Shi and last updated on June 1, 2026.

Use Cases

• Predicting postoperative sepsis risk based on CT-derived skeletal muscle density and other clinical factors.
• Identifying patients at high risk for ICU admission based on body composition parameters and operative duration.
• Stratifying risk for postoperative complications using a model integrating muscle quality, inflammation markers, and nutritional status.
• Validating clinical prediction models for adverse surgical outcomes using a dataset with strong discriminative performance (AUC 0.72-0.84).

Strengths

• Dataset includes 270 patient records with detailed clinical and CT-derived body composition measurements.
• Predictive models demonstrated strong discriminative ability with AUC values ranging from 0.72 to 0.84 in validation.
• Multivariable analysis identified low skeletal muscle density as a potent independent predictor across three distinct adverse outcomes.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic bias inherent to a single hospital in China.

Provenance

Source

figshare

Collection Method

Retrospective cohort study using patient data from the First Hospital of Shanxi Medical University.

Time Range

January 2018 to December 2024

Freshness

Last updated 2026-06-01 04:18:12; freshness should be verified.

Geography

First Hospital of Shanxi Medical University, China