Heart Rate Variability as a Novel Indicator for Predicting Postoperative Urinary Retention in Benign Prostatic Hyperplasia: Development and Internal Validation of a Predictive Nomogram.

OBJECTIVE

To develop and internally validate a heart rate variability (HRV)-based predictive model for estimating the risk of postoperative urinary retention (POUR) in patients with benign prostatic hyperplasia (BPH).

METHODS

We retrospectively reviewed clinical data from 237 patients with confirmed BPH who received surgical treatment. Among them, 36 patients (15.2%) developed POUR. Variables showing statistical significance (p < 0.05) in univariate analysis were subsequently entered into a multivariate logistic regression to determine factors independently associated with POUR. Based on the corresponding regression coefficients, a graphical risk prediction tool (nomogram) was constructed. The predictive capability of the model was evaluated through receiver operating characteristic (ROC) analysis, calibration assessment, and decision curve analysis (DCA), and its robustness was further tested using bootstrap-based internal validation.

RESULTS

Multivariate analysis identified age, prostate volume, standard deviation of normal-to-normal intervals (SDNN), and root mean square of successive differences (RMSSD) as independent predictors of POUR. The HRV-based nomogram exhibited strong discriminative performance, achieving an area under the ROC curve (AUC) of 0.894 (95% CI: 0.833-0.956), with sensitivity and specificity of 0.861 and 0.806, respectively. Internal validation showed a comparable AUC of 0.884, indicating good model stability. The calibration curve indicated close alignment between predicted and actual outcomes (χ2 = 11.801) and a Brier score of 0.075, confirming precise calibration. DCA demonstrated that the model provided a favourable net clinical benefit over a broad range of probability thresholds.

CONCLUSIONS

The HRV-based nomogram established in this study accurately predicts POUR in patients with BPH. By integrating autonomic function indicators with clinical parameters, the model demonstrates strong predictive power and clinical utility, offering an effective tool for early identification and individualised management of patients.