Predictive model of pre-operative prognostic nutrition index for biochemical recurrence in patients undergoing robot-assisted laparoscopic radical prostatectomy: a retrospective clinical study

Abstract

Objective: To investigate the predictive value of pre-operative prognostic nutritional index (PNI) in biochemical recurrence (BCR) in patients with robot-assistedlaparoscopic radical prostatectomy (RARP) and to establish a BCR risk score model based on PNI. Methods: The clinical data of 157 patients treated with RARP in the Department of Urology, the First Affiliated Hospital of Soochow University were retrospectively analyzed. The endpoint of observation was BCR. The area under the receiver operating characteristic (ROC) curve was evaluated to determine the optimal cutoff value for PNI. Kaplan-Meier analysis and Cox regression analysis were used to evaluate the correlation between PNI and BCR. 157 patients were divided into a training group and a validation group by a ratio of 7:3. By univariate and multivariate Cox regression analysis, independent prognostic factors were screened from the relevant clinicopathological factors, a BCR prediction model and nomogramwere established, then verified its value. Results: According to the ROC curve, the optimal cutoff value of PNI for 157 patients in this study was 47.425. According to multivariate Cox regression analysis, PNI and prostate-specific antigen (PSA) were identified as independent prognostic factors for predicting BCR in patients treated with RARP. A BCR prediction model formula was established based on PNI and PSA. It was proved to have good predictive value in both the training group and the validation group. Nomogram was constructed to predict the BCR of patients treated with RARP at 6-, 12-, and 24-months after surgery. The results of the calibration plots showed that the nomogram performed well in the training group and the validation group. Conclusion: PNI is an independent prognostic factor for predicting BCR in patients treated with RARP. The scoring model and nomogram based on PNI and PSA can effectively predict the risk of BCR in patients treated with RARP.