External validation and comparison of the scoring systems (S.T.O.N.E, GUY, CROES, S-ReSC) for predicting percutaneous nephrolithotomy outcomes for staghorn stones: A single center experience with 160 cases

Does success in percutaneous nephrolithotomy depend only on stone size? Analysis of the predictive capacity for success and complications of the current nephrolithometry scoring systems and their relationship with the stone surface

OBJECTIVE

To analyze the predictive capacity of the nephrolithometry scoring systems (GSS, STONE, CROES and S-ReSC) and stone surface regarding success and complications following percutaneous nephrolithotomy (PCNL).

METHODS

We studied 392 patients who had undergone PCNL in our center. Only patients with a non-contrast CT (n = 240) were finally included for analysis. The predictive capacities for success and complications of the different scoring systems were evaluated using ROC curves and their area under the curve (AUC).

RESULTS

Regarding success, the S-ReSC system had the highest predictive capacity with an AUC of 0.681 (95% CI 0.610-0.751), followed by the CROES with 0.667 (95% CI 0.595-0.738), the STONE with 0.654 (95% CI 0.579-0.728) and finally the GSS with 0.626 (95% CI 0.555-0.698). The stone surface as a single variable had an AUC of 0.641 (95% CI 0.565-0.718). As for complications, the S-ReSC had the highest AUC with 0.664 (95% CI 0.57-0.758), followed by STONE with 0.663 (95% CI 0.572-0.755), GSS with 0.626 (95% CI 0.555).-0.698) and CROES with 0.614 (95% CI 0.518-0.7). The stone surface alone had an AUC of 0.616 (95% CI 0.522-0.715).

CONCLUSION

The nephrolithometry scales analyzed show a moderate predictive capacity for success and complications in patients undergoing PCNL in our center. Moreover, stone surface as an independent variable demonstrates moderate predictive capacity for both outcomes.

Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.

International Alliance of Urolithiasis (IAU) guideline on staghorn calculi management

BACKGROUND

The stone burden based management strategy reported in the guidelines published by different associations is well known for a long time. Staghorn calculi, representing the largest burden and most complex stones, is one of the most challenging cases to practicing urologists in clinical practice. The International Alliance of Urolithiasis (IAU) has released a series of guidelines on the management of urolithiasis.

PURPOSE

To develop a series of recommendations for the contemporary management management of staghorn calculi and to provide a clinical framework for urologists treating patients with these complex stones.

METHODS

A comprehensive literature search for articles published in English between 01/01/1976 and 31/12/2022 in the PubMed, OVID, Embase and Medline database is performed. A series of recommendations are developed and individually graded following the review of literature and panel discussion.

RESULTS

The definition, pathogenesis, pathophysiology, preoperative evaluation, intraoperative treatment strategies and procedural advice, early postoperative management, follow up and prevention of stone recurrence are summarized in the present document.

CONCLUSION

A series of recommendations regarding the management of staghorn calculi, along with related commentary and supporting documentation offered in the present guideline is intended to provide a clinical framework for the practicing urologists in the management of staghorn calculi.

International Alliance of Urolithiasis (IAU) Guideline on percutaneous nephrolithotomy

The International Alliance of Urolithiasis (IAU) would like to release the latest guideline on percutaneous nephrolithotomy (PCNL) and to provide a clinical framework for surgeons performing PCNLs. These recommendations were collected and appraised from a systematic review and assessment of the literature covering all aspects of PCNLs from the PubMed database between January 1, 1976, and July 31, 2021. Each generated recommendation was graded using a modified GRADE methodology. The quality of the evidence was graded using a classification system modified from the Oxford Center for Evidence-Based Medicine Levels of Evidence. Forty-seven recommendations were summarized and graded, which covered the following issues, indications and contraindications, stone complexity evaluation, preoperative imaging, antibiotic strategy, management of antithrombotic therapy, anesthesia, position, puncture, tracts, dilation, lithotripsy, intraoperative evaluation of residual stones, exit strategy, postoperative imaging and stone-free status evaluation, complications. The present guideline on PCNL was the first in the IAU series of urolithiasis management guidelines. The recommendations, tips and tricks across the PCNL procedures would provide adequate guidance for urologists performing PCNLs to ensure safety and efficiency in PCNLs.

Does nephrolithometry scoring systems predict success and complications in miniPCNL?

OBJECTIVE

Auxiliary nephrolithometric scoring systems (NSSs) have been developed to predict complications and treatment success of conventional percutaneous nephrolithotomy (PCNL). However, to our knowledge, there is no study comparing these NSSs in patients undergoing miniPCNL. This study aimed to compare the NSSs in terms of their ability to predict miniPCNL-related complications and treatment success.

METHODS

The data of patients undergoing PCNL between September 2016 and May 2018 were retrospectively reviewed through the electronic medical record system, and 140 patients were included in our study. Stone-free status was evaluated using non-contrast computed tomography between 1 and 3 months after the procedure. PCNL was considered successful if the patient was completely stone free. The postsurgical complications were classified according to the modified Clavien-Dindo classification system.

RESULTS

The Clinical Research Office of the Endourological Society (CROES) and STONE NSSs significantly predicted miniPCNL treatment success (p = 0.043, p = 0.018). However, the Guy's NSS did not significantly predict the treatment success (p = 0.415). Guy's, CROES and STONE NSSs were not found to significantly predict postsurgical complications (p = 0.584, p = 0.823, p = 0.189).

CONCLUSION

To the best of our knowledge, our study is the first of its kind to investigate the ability of NSSs to predict treatment success and postsurgical complications in patients undergoing miniPCNL. The study found that STONE and CROES NSSs are independent parameters for predicting stone-free status after miniPCNL. In addition, our study found that none of the NSSs were useful in predicting postsurgical complications in patients undergoing miniPCNL.

Comparing different kidney stone scoring systems for predicting percutaneous nephrolithotomy outcomes: A multicenter retrospective cohort study

OBJECTIVE

To compare the predictive performance of five previously described scoring systems (i.e., S.T.O.N.E., Guy's, Clinical Research Office of the Endourological Society (CROES), the Seoul National University Renal Stone Complexity (S-RESC) and the new Stone Kidney Size (SKS) score) for postoperative outcomes regarding stone-free rate (SFR) and complications in adult patients.

METHODS

Data from 349 patients who underwent percutaneous nephrolithotomy (PCNL) in three urology departments were analyzed. SKS, S.T.O.N.E., S-ReSC, CROES and Guy's nephrolithometry scoring systems were used to retrospectively calculate predictions for each patient. Univariate and multivariate analyses were performed to evaluate factors associated with SFR and complication rates. Receiver operating characteristic (ROC) curves were generated and areas under curves (AUC) were compared to identify the method with the highest predictive value.

RESULTS

Median SKS, S.T.O.N.E., S-ReSC, CROES and Guy's scores were 4, 7, 3, 170.8 and 2, respectively. Overall, SFR was 67.0% (234/349) with a complications rate of 36.7% (128/349). AUCs of each method for predicting stone-free status, highlighted reasonable predictive capabilities with 0.709, 0.806, 0 0.869, 0.207, and 0.735, respectively; however, the S-ReSC scoring system had the best discriminative performance. According to multivariate logistic regression and AUC results, none were effectively capable of predicting complications.

CONCLUSIONS

All scoring systems correlated significantly with stone-free status; although, S-ReSC appears to have the greatest predictive ability. This method is also relatively easy to implement and highly reproducible. However, none of the methods analyzed are able to accurately predict postoperative complications.

Morphometry scores: Clinical implications in the management of staghorn calculi

Due to their large size, rapid growth, and attendant morbidity, staghorn calculi are complex clinical entities that impose significant treatment-related challenges. Moreover, their relative heterogeneity-in terms of both total stone burden and anatomic distribution-limits the ability to standardize their characterization and the reporting of surgical outcomes. Several morphometry systems currently exist to define the volumetric distribution of renal stones, in general, and to predict the outcomes of percutaneous nephrolithotomy; however, they fall short in their applicability to staghorn stones. In this review, we aim to discuss the clinical utility of morphometry systems and the influence of pelvicalyceal anatomy on the management of these complex calculi.

Percutaneous Nephrolithotomy Outcomes Based on S.T.O.N.E., GUY, CROES, and S-ReSC Scoring Systems: The First Prospective Study

Objective: To evaluate predictive capability and clinical applicability of the current nephrolithometric scoring systems of S.T.O.N.E. score, Guy's scoring system (GSS), CROES (Clinical Research Office of the Endourological Society) nomogram, and S-ReSC (Seoul National University Renal Stone Complexity) score for percutaneous nephrolithotomy (PCNL) outcomes in the same cohort in a prospective study. Methods: Consecutive patients undergoing PCNL between 2015 and 2018 were included calculating the four scores in the same cohort. Stone-free status (SFS), complications, operative time (OT), estimated blood loss (EBL), fluoroscopy time, and length of hospital stay were investigated. Receiver operator characteristic (ROC) curves for predictive accuracy and regression analysis for predictors of SFS were performed. Results: In all, 162 PCNLs were accomplished and analyzed. Overall, SFS was 75.9% and complication rate was 30.9%. The mean acquisition time of scores was 52.9 ± 0.5 seconds for GSS, 05.1 ± 0.3 seconds for S.T.O.N.E. score, 224 ± 3.1 seconds for CROES, and 102.6 ± 3.5 seconds for S-ReSC score. SFS had the best association with CROES grade. Clavien grade was associated with S.T.O.N.E. score. Moreover, EBL and OT had best association with S-ReSC score. All scores had comparable predictive accuracy on ROC curves regarding SFS. Stone essence and tract length are not different in cases with residual stones. Number of involved calyces, single vs multiple stones and renal pelvic obstruction were significant predictors of SFS in regression analysis. Conclusion: The four scoring systems had comparable predictive accuracy for SFS. However, S.T.O.N.E. and S-ReSC scores were easily applicable and provided better association with EBL and OT compared with the GSS score. Number of involved calyces, stone multiplicity, and renal pelvic obstruction were significant predictors of SFS; hence, further studies are needed to invent a universally agreeable scoring system covering reported shortcomings in the currently used scores.

Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram

Purpose: To validate the output of a machine learning-based software as an intelligible interface for predicting multiple outcomes after percutaneous nephrolithotomy (PCNL). We compared the performance of this system with Guy's stone score (GSS) and the Clinical Research Office of Endourological Society (CROES) nomogram. Patients and Methods: Data from 146 adult patients (87 males, 59%) who underwent PCNL at our institute were used. To validate the system, accuracy of the software for predicting each postoperative outcome was compared with the actual outcome. Similarly, preoperative data were analyzed with GSS and CROES nomograms to determine stone-free status as predicted by these nomograms. A receiver operating characteristic (ROC) curve was generated for each scoring system, and the area under the ROC curve (AUC) was calculated and used to assess the predictive performance of all three models. Results: Overall stone-free rate was 72.6% (106/146). Forty of 146 patients (27.4%) were scheduled for 42 ancillary procedures (extracorporeal shockwave lithotripsy [SWL] [n = 31] or repeat PCNL [n = 11]) to manage residual renal stones. Overall, the machine learning system predicted the PCNL outcomes with an accuracy ranging between 80% and 95.1%. For predicting the stone-free status, the AUC for the software (0.915) was significantly larger than the AUC for GSS (0.615) or CROES nomograms (0.621) (p < 0.001). Conclusion: At the internal institutional level, the machine learning-based software was a promising tool for recording, processing, and predicting outcomes after PCNL. Validation of this system against an external dataset is highly recommended before its widespread application.

Predictability and Practicality of Image-Based Scoring Systems for Patient Assessment and Outcome Stratification During Percutaneous Nephrolithotomy: a Contemporary Update

Preoperative nomograms offer systematic and quantitative methods to assess patient- and stone-related characteristics and their impact on successful treatment and potential risk of complication. Discrepancies in the correlation of perioperative variables to patient outcomes have led to the individual development, validation, and application of four independent scoring systems for the percutaneous nephrolithotomy: Guy's stone score, S.T.O.N.E. nephrolithometry, Clinical Research Office of the Endourology Society nomogram, and Seoul National University Renal Stone Complexity. The optimal nomogram should have high predictive ability, be practically integrated into clinical use, and be widely applicable to urinary stone disease. Herein, we seek to provide a contemporary evaluation of the advantages, disadvantages, and commonalities of each scoring system. While the current data is insufficient to conclude which scoring system is destined to become the gold standard, it is crucial that a nephrolithometric scoring system be incorporated into common practice to improve surgical planning, patient counseling, and outcome assessment.