Endometriosis Risk Prediction with Inflammatory Index and Clinical Data

A retrospective study of 226 patients from Jinshan Hospital of Fudan University between January 2023 and December 2025. The dataset, authored by Jing Chen and published on figshare in 2026, investigates the association between the Aggregate Index of Systemic Inflammation (AISI) and endometriosis risk, used to develop a predictive nomogram.

Use Cases

• Predicting endometriosis risk based on the Aggregate Index of Systemic Inflammation (AISI) and lipid profiles.
• Training logistic regression models using clinical features like age, BMI, and age at menarche.
• Validating clinical nomograms for early identification of high-risk individuals.
• Comparing model performance metrics such as AUC, calibration, and decision curve analysis.

Strengths

• 226 patient records with confirmed pathological diagnosis.
• Model performance validated externally using NHANES (2001–2006) data.
• Multivariate analysis identified 7 independent predictors with odds ratios and confidence intervals.
• Nomogram achieved an AUC of 0.869, outperforming sub-models.

Limitations

• Row count for the underlying data is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data reflects a single hospital's patient population, which may introduce geographic or demographic bias.

Provenance

Source

Jinshan Hospital of Fudan University, with external validation from NHANES.

Collection Method

Retrospective analysis of patient records and laboratory results.

Time Range

Primary data: January 2023 to December 2025. Validation data: 2001–2006.

Freshness

Last updated 2026-05-04 05:30:36; freshness should be verified.

Geography

Primary data from a hospital in China; validation data from the United States (NHANES).