Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement

Area of Science:

• Urology
• Medical Informatics

Background:

• Benign prostatic enlargement (BPE) affects over half of men over 50.
• Patient BPE phenotypes are diverse, influencing treatment decisions.
• Surgical options include monopolar transurethral resection of the prostate (mTUR-P), bipolar transurethral resection of the prostate (bTUR-P), and bipolar transurethral vaporization of the prostate (bTUVis).

Purpose of the Study:

• To analyze patient pre-operative characteristics for BPE.
• To compare clustering-based surgical technique assignment with actual clinical practice.
• To investigate the utility of k-means clustering in optimizing BPE surgical management.

Main Methods:

• Clustering analysis using the k-means algorithm was performed on pre-operative patient data.
• Patient characteristics were analyzed to identify distinct subgroups.
• The study compared the k-means-derived technique assignments with the surgical techniques actually employed.

Main Results:

• The k-means algorithm identified patient clusters based on pre-operative characteristics.
• A comparison was made between the theoretically assigned surgical techniques per cluster and the techniques utilized in real-world scenarios.
• Findings will inform the refinement of surgical technique selection for BPE.

Conclusions:

• Patient pre-operative characteristics can be clustered to potentially guide surgical technique selection for BPE.
• Clustering analysis offers a data-driven approach to complement surgeon expertise and patient factors in choosing between mTUR-P, bTUR-P, or bTUVis.
• Further research can validate and integrate this clustering approach into clinical decision-making for BPE management.