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Abstract Objective To compare intrarenal pressure (IRP) and irrigation flow by varying suspended water heights and hand-held pressure pumping during flexible ureteroscopy using an in vitro 3D printed kidney model. Methods A 3D-printed silicone model was used to simulate the kidney. The ureteral access sheath(UAS) was connected to the kidney model and positioned at the ureteropelvic junction. Central venous pressure tubing was used to monitor the pressure in the renal pelvis under different conditions. Sheath sizes of 12Fr and 14Fr were tested with flexible ureteroscope (fURS) sizes of 7.5, 8.5, and 9.5Fr, respectively. The irrigation was gravity-based, with suspended water heights set at 60, 90, 120, 150, and 180 cm. The manual pumping as another set of measurement is used to measure the maximum intrarenal pressure. Results Using a 12Fr sheath with a 9.5Fr fURS loading without additional accessories resulted in IRP ranging from 8.4 to 17.5 cmH2O, while manual pumping perfusion pressure exceeded 60 cmH2O. Loading a 200-um laser fiber reduced the pressure to 6.4–10.5 cmH2O, and using a stone basket decreased it to 4.0–5.0 cmH2O. Using a 14Fr sheath with a 9.5Fr fURS resulted in an IRP of 2.5-6.0 cmH2O, compared to 17 cmH2O with manual pumping. With a 12Fr sheath and a 7.5Fr fURS, the IRP ranged from 5.4 to 8.2 cmH2O, while manual pumping resulted in 25.5 cmH2O. With a 14Fr sheath and a 7.5Fr fURS, the IRP ranged from 1.5 to 4.3 cmH2O, and manual pumping resulted in 9.0 cmH2O. Conclusion When using a UAS in a flexible ureteroscopy, the IRP can be maintained within a safe range with different fURS/UAS combos with a suspended water height of less than 180 cm. However, with specific fURS/UAS(9.5Fr/12Fr) combos, the IRP exceeded the safe limit when using manual pumping. Gravity irrigation with a suspended water height of less than 180 cm is safe in this simulated clinical environment.

IntroductionThis study aims to investigate whether the transrectal ultrasound-guided combined biopsy (CB) improves the detection rates of prostate cancer (PCa) and clinically significant PCa (csPCa) in biopsy-naïve patients. We also aimed to compare the Prostate Imaging Reporting and Data System (PI-RADS v2.1) score, ADC values, and PSA density (PSAd) in predicting csPCa by the combined prostate biopsy.MethodsThis retrospective and single-center study included 389 biopsy-naïve patients with PSA level 4~20 ng/ml, of whom 197 underwent prebiopsy mpMRI of the prostate. The mpMRI-based scores (PI-RADS v2.1 scores and ADC values) and clinical parameters were collected and evaluated by logistic regression analyses. Multivariable models based on the mpMRI-based scores and clinical parameters were developed by the logistic regression analyses to forecast biopsy outcomes of CB in biopsy-naïve patients. The ROC curves measured by the AUC values, calibration plots, and DCA were performed to assess multivariable models.ResultsThe CB can detect more csPCa compared with TRUSB (32.0% vs. 53%). The Spearman correlation revealed that Gleason scores of the prostate biopsy significantly correlated with PI-RADS scores and ADC values. The multivariate logistic regression confirmed that PI-RADS scores 4, 5, and prostate volume were important predictors of csPCa. The PI-RADS+ADC+PSAd (PAP) model had the highest AUCs of 0.913 for predicting csPCa in biopsy-naïve patients with PSA level 4~20 ng/ml. When the biopsy risk threshold of the PAP model was greater than or equal to 0.10, 51% of patients could avoid an unnecessary biopsy, and only 5% of patients with csPCa were missed.ConclusionThe prebiopsy mpMRI and the combined prostate biopsy have a high CDR of csPCa in biopsy-naïve patients. A multivariable model based on the mpMRI-based scores and PSAd could provide a reference for clinicians in forecasting biopsy outcomes in biopsy-naïve patients with PSA 4~20 ng/ml and make a more comprehensive assessment during the decision-making of the prostate biopsy.

Objective: This study aims to investigate the predictive performance of machine learning in predicting the occurrence of systemic inflammatory response syndrome (SIRS) and urosepsis after percutaneous nephrolithotomy (PCNL). Methods: A retrospective analysis was conducted on patients who underwent PCNL treatment between January 2016 and July 2022. Machine learning techniques were employed to establish and select the best predictive model for postoperative systemic infection. The feasibility of using relevant risk factors as predictive markers was explored through interpretability with Machine Learning. Results: A total of 1067 PCNL patients were included in this study, with 111 (10.4 %) patients developing SIRS and 49 (4.5 %) patients developing urosepsis. In the validation set, the risk model based on the GBM protocol demonstrated a predictive power of 0.871 for SIRS and 0.854 for urosepsis. Preoperative and postoperative platelet changes were identified as the most significant predictors. Both thrombocytopenia and thrombocytosis were found to be risk factors for SIRS or urosepsis after PCNL. Furthermore, it was observed that when the change in platelet count before and after PCNL surgery exceeded 30*109/L (whether an increase or decrease), the risk of developing SIRS or urosepsis significantly increased. Conclusion: Machine learning can be effectively utilized for predicting the occurrence of SIRS or urosepsis after PCNL. The changes in platelet count before and after PCNL surgery serve as important predictors.