Data-driven Clustering in Hemorrhoid Surgery: Retrospective Monocentric Study for the Identification of Clinical Phenotypes

Condition(s) studied

Investigational drug(s) / intervention(s)

Any surgical procedure for hemorrhoidal disease: standard hemorrhoidectomy, advanced hemorrhoidectomy, prolapsectomy, Doppler-guided procedures, or combined techniques

Study summary

This retrospective, single-center observational study will use routinely collected perioperative data from adults undergoing surgery for symptomatic hemorrhoidal disease to identify data-driven clinical phenotypes. Unsupervised machine learning will be applied to characterize clusters of patients based on demographic, clinical, anatomical, and surgical variables. The study will explore whether the resulting phenotypes differ in operative complexity and postoperative course, and will generate hypotheses to inform future predictive models and personalized surgical planning.

Eligibility

Primary outcome measure(s)

• Internal validity of the unsupervised clustering solution (silhouette coefficient) — From completion of dataset extraction/cleaning through completion of clustering analysis (retrospective analysis of surgeries performed December 2024 to June 2025)
  Silhouette coefficient of the final k-means clustering solution derived from t-SNE-reduced perioperative data. The silhouette coefficient will be used as the primary internal validity metric to quantify cluster cohesion and separation for the selected number of clusters.

Trial sites (1)

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