Diagnostic Nomogram for Complicated Appendicitis Based on Blood Inflammatory Markers

A retrospective study of 533 patients with acute appendicitis admitted between January 2018 and December 2024. The dataset was used to develop and validate a diagnostic nomogram based on peripheral blood composite inflammatory markers to distinguish complicated from simple appendicitis. The model was created by author Menghao Li and published on figshare under a CC-BY-4.0 license.

Use Cases

• Validating the diagnostic performance of the Systemic Inflammation Response Index (SIRI) for complicated appendicitis.
• Training machine learning models to predict appendicitis severity based on the Neutrophil-to-Albumin Ratio (NAR).
• Benchmarking new composite inflammatory markers against the Prognostic Nutritional Index (PNI) for clinical utility.
• Reproducing the reported nomogram model which showed an AUC of 0.758 in the training set.

Strengths

• Dataset includes 533 patient records, split into training (429) and validation (104) sets.
• Model performance is quantitatively reported with AUC values (e.g., 0.758), confidence intervals, and calibration metrics (MAE of 0.022).
• Independent predictors (SIRI, NAR, PNI) are identified with odds ratios and p-values from multivariate logistic regression.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying patient data is unknown, which may limit suitability assessment.
• Data may reflect temporal and institutional bias inherent to a single retrospective study.

Provenance

Source

figshare

Collection Method

Retrospective study of patient medical records.

Time Range

January 2018 to December 2024

Freshness

Last updated 2026-04-30 05:41:31; freshness should be verified.