Heart Rate Variability as a Novel Indicator for Predicting Postoperative Urinary Retention in Benign Prostatic...

Area of Science:

• Urology
• Cardiology
• Medical Informatics

Background:

• Postoperative urinary retention (POUR) is a common complication following surgery for benign prostatic hyperplasia (BPH).
• Accurate risk prediction is crucial for proactive patient management and optimizing surgical outcomes.
• Current predictive models may not fully capture the physiological factors influencing POUR risk.

Purpose of the Study:

• To develop and validate a heart rate variability (HRV)-based predictive model for estimating POUR risk in BPH patients.
• To integrate autonomic function indicators with clinical parameters for enhanced predictive accuracy.
• To create a user-friendly nomogram for clinical application.

Main Methods:

• Retrospective review of clinical data from 237 BPH patients undergoing surgical treatment.
• Multivariate logistic regression analysis to identify independent predictors of POUR.
• Construction of an HRV-based nomogram and evaluation using ROC analysis, calibration, and decision curve analysis (DCA).
• Internal validation using bootstrap methods.

Main Results:

• Age, prostate volume, SDNN, and RMSSD were identified as independent predictors of POUR.
• The HRV-based nomogram demonstrated strong discriminative performance with an AUC of 0.894.
• The model showed excellent calibration (Brier score 0.075) and favorable clinical utility via DCA.
• Internal validation confirmed good model stability with a comparable AUC of 0.884.

Conclusions:

• The developed HRV-based nomogram accurately predicts POUR in BPH patients.
• The model effectively integrates autonomic function (HRV) with clinical data, offering significant predictive power.
• This tool facilitates early identification and personalized management of patients at risk for POUR.