Endometriosis Risk Predictors with AISI Index and Lipid Profiles, 226 Patients

A retrospective study of 226 patients from Jinshan Hospital of Fudan University between January 2023 and December 2025, investigating associations between an aggregate inflammation index (AISI) and endometriosis risk. The dataset, authored by Jing Chen and shared under CC-BY-4.0, includes baseline characteristics, lipid profiles, and blood cell counts used to build a predictive nomogram validated with NHANES data.

Use Cases

• Training logistic regression models for endometriosis risk prediction based on inflammatory and lipid markers.
• Validating clinical nomogram performance using metrics like AUC and decision curve analysis.
• Analyzing the relationship between systemic inflammation (AISI) and disease pathology.
• Benchmarking predictive models against external validation datasets like NHANES.

Strengths

• Includes 226 patient records with 104 confirmed endometriosis cases, providing a defined case-control structure.
• Model performance is quantified with an AUC of 0.869 and validated externally using NHANES data.
• Key predictors like age, BMI, AISI, and lipid profiles are identified with specific odds ratios and confidence intervals.

Limitations

• Row count is unknown for the underlying data file, 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 from a specific time period, which may limit generalizability.

Provenance

Source

Jinshan Hospital of Fudan University

Collection Method

Retrospective analysis of patient records and external validation using NHANES (2001–2006) data.

Time Range

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

Freshness

Last updated 2026-05-04 05:30:35; freshness should be verified.

Geography

China (primary data); United States (validation data from NHANES)