Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model?

The use of intelligent analysis (IA) in determining the tactics of treating patients with nephrolithiasis

INTRODUCTION

The use of modern information technologies allows to increase confidence in the choice of a surgical treatment method of kidney stones, as well as to improve the quality of treatment due to an adequate combination of therapeutic techniques.

MATERIALS AND METHODS

In our study we analyzed the treatment results of 625 patients with kidney stones. We created a register with the information on more than 50 parameters for each patient. Each example had an output parameter representing a predefined treatment strategy (extracorporeal shock-wave lithotripsy [ESWL]-1, percutaneous nephrolithotomy [PCNL]-2, pyelolithotomy or nephrolithotomy-3). The initial database served as the basis for training the neural network estimation technique. The aim of our study was to assess the possibility of using neural network algorithms in choosing a method for surgical treatment of urolithiasis.

RESULTS

A prospective study was conducted to assess the clinical effectiveness of implementing the recommendations of the system. The average number of sessions in the group using the neural network assessment technique was 1.4. Residual fragments remained at the time of discharge in seven (15.6%) patients: four in the kidney, three in the lower third of the ureter "stone path." Inversion of therapeutic tactics-PCNL-was performed in four cases. The efficiency of the ESWL was 91.1%. The indicators of the ESWL in the comparison groups differed statistically significantly: in the second group, the efficiency was higher due to more stone fragmentation, with lower energy costs (the average number of sessions was 0.4 less).

CONCLUSION

The presented technique can be of help for a practicing urologist to choose the optimal treatment method for each patient, thereby minimizing the risk of early postoperative complications.

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Objective

To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.

Methods

A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language.

Results

A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.

Conclusion

AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.

Theranostic roles of machine learning in clinical management of kidney stone disease

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.

Clinical Applications of Machine Learning for Urolithiasis and Benign Prostatic Hyperplasia: A Systematic Review

INTRODUCTION

Previous systematic reviews related to machine learning (ML) in urology often overlooked the literature related to endourology. Therefore, we aim to conduct a more focused systematic review examining the use of ML algorithms for benign prostatic hyperplasia (BPH) or urolithiasis. In addition, we are the first group to evaluate these articles using the STREAM-URO framework.

METHODS

Searches of MEDLINE, Embase, and the Cochrane CENTRAL databases were conducted from inception through July 12, 2021. Keywords included those related to ML, endourology, urolithiasis, and BPH. Two reviewers screened the citations that were eligible for title, abstract and full-text screening, with conflicts resolved by a third reviewer. Two reviewers extracted information from the studies, with discrepancies resolved by a third reviewer. The data collected was then qualitatively synthesized by consensus. Two reviewers evaluated each article according to the STREAM-URO checklist with discrepancies resolved by a third reviewer.

RESULTS

After identifying 459 unique citations, 63 articles were retained for data extraction. Most articles consisted of tabular (n=32) and computer vision (n=23) tasks. The two most common problem types were classification (n=40) and regression (n=12). In general, most studies utilized neural networks as their ML algorithm (n=36). Among the 63 studies retrieved, 58 were related to urolithiasis and five focused on BPH. The urolithiasis studies were designed for outcome prediction (n=20), stone classification (n=18), diagnostics (n=17), and therapeutics (n=3). The BPH studies were designed for outcome prediction (n=2), diagnostics (n=2), and therapeutics (n=1). On average, the urolithiasis and BPH articles met 13.8 (SD 2.6), and 13.4 (4.1) of the 26 STREAM-URO framework criteria, respectively.

CONCLUSIONS

The majority of the retrieved studies successfully helped with outcome prediction, diagnostics, and therapeutics for both urolithiasis and BPH. While ML shows great promise in improving patient care, it is important to adhere to the recently developed STREAM-URO framework to ensure the development of high-quality ML studies.

Artificial intelligence in endourology: emerging technology for individualized care

PURPOSE OF REVIEW

Artificial intelligence in medicine has allowed for efficient processing of large datasets to perform cognitive tasks that facilitate clinical decision-making, and it is an emerging area of research. This review aims to highlight the most pertinent and recent research in artificial intelligence in endourology, where it has been used to optimize stone diagnosis, support decision-making regarding management, predict stone recurrence, and provide new tools for bioinformatics research within endourology.

RECENT FINDINGS

Artificial neural networks (ANN) and machine learning approaches have demonstrated high accuracy in predicting stone diagnoses, stone composition, and outcomes of spontaneous stone passage, shockwave lithotripsy (SWL), or percutaneous nephrolithotomy (PCNL); some of these models outperform more traditional predictive models and existing nomograms. In addition, these approaches have been used to predict stone recurrence, quality of life scores, and provide novel methods of mining the electronic medical record for research.

SUMMARY

Artificial intelligence can be used to enhance existing approaches to stone diagnosis, management, and prevention to provide a more individualized approach to endourologic care. Moreover, it may support an emerging area of bioinformatics research within endourology. However, despite high accuracy, many of the published algorithms lack external validity and require further study before they are more widely adopted.

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades

Purpose of Review

To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist.

Recent Findings

This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it.

Summary

The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

BACKGROUND

Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.

METHODS

The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.

RESULTS

The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.

CONCLUSION

ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

Artificial intelligence in stone disease

PURPOSE OF REVIEW

Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients.

RECENT FINDINGS

AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms.

SUMMARY

The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.

Machine learning applications to enhance patient specific care for urologic surgery

PURPOSE

As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care.

METHODS

We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms "urologic surgery" as well as "artificial intelligence", "machine learning", "neural network", and "automation" were used.

RESULTS

The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy.

CONCLUSION

Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.

Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis

We performed a systematic review and meta-analysis to investigate the use of machine learning techniques for predicting stone-free rates following Shockwave Lithotripsy (SWL). Eight papers (3264 patients) were included. Two studies used decision-tree approaches, five studies utilised Artificial Neural Networks (ANN), and one study combined a variety of approaches. The summary true positive rate was 79%, summary false positive rate was 14%, and Receiver Operator Characteristic (ROC) was 0.90 for machine learning approaches. Machine learning algorithms were at least as good as standard approaches. Further prospective evidence is needed to routinely apply machine learning algorithms in clinical practice.

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.

Artificial intelligence and neural networks in urology: current clinical applications

INTRODUCTION

As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in urology.

EVIDENCE ACQUISITION

A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology.

EVIDENCE SYNTHESIS

The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non-oncological diseases like stones and functional urology.

CONCLUSIONS

AI technologies are growing their role in health care; but, up to now, their "real-life" implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.

External validation of Imamura nomogram as a tool to predict preoperatively laser semi-rigid ureterolithotripsy outcomes

BACKGROUND

We aimed to validate Imamura nomogram for prediction of stone free rate in patients undergoing ureterolithotripsy (ULT).

METHODS

From January 2013 to June 2016, patients undergoing laser semi-rigid ULT were prospectively enrolled at our center. All patients were preoperatively assessed with clinical history, blood samples, uranalysis and non-contrast enhanced computed tomography (CT). Treatment efficacy was assessed 1 month later by non-contrast enhanced CT. ROC curve was used to evaluate the performance characteristics of Imamura nomogram.

RESULTS

Overall, we enrolled 275 patients. Median age was 55 years (IQR: 46/64), median length of stone was 9.8 mm (IQR: 7.5/12). Pyuria was detected in 6/275 (2.1%) patients. Stones were located at ureteropelvic junction in 55/275 (19%) patients, proximal ureter in 74/275 (26%) patients, middle and distal ureter in 66/275 (24%) patients and 82/275 (30%) patients, respectively. At 1-month follow-up, 209/275 (76%) patients were stone free. Imamura nomogram presented an AUC of 0.67 (95% CI: 0.580-0.761) for the prediction of stone free rate. At the best cut-off value of 75%, sensitivity was 76%, specificity was 55%, positive predictive value (PPV) was 83% and negative predictive value was 45%.

CONCLUSIONS

We firstly validated Imamura nomogram in a European cohort study. It proved a reasonable accuracy (area under curve: 0.67) and a good PPV (83%). Further studies should confirm our results to support the routine clinical use of Imamura nomogram as a tool to predict ULT outcomes.

Factors Affecting the Outcome of Extracorporeal Shockwave Lithotripsy in Urinary Stone Treatment

Objectives

We sought to evaluate the factors affecting the outcome of extracorporeal shockwave lithotripsy (ESWL) in urinary stone treatment.

Methods

We conducted a retrospective review of 235 adult patients treated with ESWL, for radiopaque renal or ureteric stones between January 2015 and December 2016. Patient's age, sex, stone size, laterality, location, density, skin-to-stone distance (SSD), and presence of double J stent were studied as potential predictors. At the end of three months, the patients were divided into success and failure groups and the significance was determined.

Results

Of the 235 patients (188 males and 47 females) analyzed, ESWL was successful in 79.1%. Univariate analysis of both groups revealed no significant difference in patient's age and stone laterality. Statistically significant differences in gender, stone size, stone site, stone density, SSD, and patients with stents were observed. Statistically significant factors in multivariate logistic regression analysis were sex and stent. Females had three-times higher risk for ESWL failure than males (odds ratio (OR) = 3.213; 95% confidence interval (CI): 1.194-8.645; p = 0.021) and a higher failure rate when a stent was used (OR = 6.358; 95% CI: 2.228-18.143; p = 0.001).

Conclusions

This study revealed that ESWL can treat renal and ureteric stones successfully with an inverse association between outcome and predictors such as stone size and density, SSD, and stent presence. These factors can help us in improving patient selection and ensure better results at lower cost.

A neural network - based algorithm for predicting stone - free status after ESWL therapy

OBJECTIVE

The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones.

MATERIALS AND METHODS

Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data.

RESULTS

Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group.

CONCLUSIONS

Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.

External Validation of Triple D Score in an Elderly (≥65 Years) Population for Prediction of Success Following Shockwave Lithotripsy

INTRODUCTION

Triple D score was recently developed for prediction of extracorporeal shockwave lithotripsy (SWL) outcomes. However, it has not been validated. SWL in elderly patients results in lower success and higher complication rates. We aimed at externally validating Triple D score in a population ≥65 years of age.

PATIENTS AND METHODS

We retrospectively analyzed the data of 182 patients ≥65 years of age who underwent SWL for renal or ureteral stones and were evaluated with non-contrast computed tomography before SWL. Stone volume (SV), skin-to-stone distance (SSD), and stone density were measured, and cutoff values were determined with receiver operator characteristic analysis. Triple D scores were calculated, and success rates were determined for each score.

RESULTS

Mean SV, SSD, and stone density values were significantly higher in patients with failed outcomes compared with those with successful outcomes in both renal and ureteral cases. Cutoff values of 187.5 mm(3), 10.5 cm, and 675 HU for renal stones and of 185 mm(3), 11.5 cm, and 785 HU for ureteral stones were detected. Success rates of 95.5% and 95% were detected for patients with a Triple D score of 3 in the renal and ureteral stone groups, respectively. Success rates of patients with a Triple D score of 0 were 20% and 25% in the renal and ureteral stone groups, respectively.

CONCLUSIONS

Triple D score correlated well with SWL outcomes in patients ≥65 years of age, and it is externally validated. Various factors may deal with cutoff levels of involved parameters. Therefore, we suggest that each institution determines its unique cutoff levels for SV, SSD, and stone density parameters and calculates the Triple D score for its patients with respect to these cutoff levels to predict the success after SWL and aid in decision making.

Nonlinear logistic regression model for outcomes after endourologic procedures: a novel predictor

The purpose of this study was to design a thorough and practical nonlinear logistic regression model that can be used for outcome prediction after various forms of endourologic intervention. Input variables and outcome data from 382 renal units endourologically treated at a single institution were used to build and cross-validate an independently designed nonlinear logistic regression model. Model outcomes were stone-free status and need for a secondary procedure. The model predicted stone-free status with sensitivity 75.3% and specificity 60.4%, yielding a positive predictive value (PPV) of 75.3% and negative predictive value (NPV) of 60.4%, with classification accuracy of 69.6%. Receiver operating characteristic area under the curve (ROC AUC) was 0.749. The model predicted the need for a secondary procedure with sensitivity 30% and specificity 98.3%, yielding a PPV of 60% and NPV of 94.2%. ROC AUC was 0.863. The model had equivalent predictive value to a traditional logistic regression model for the secondary procedure outcome. This study is proof-of-concept that a nonlinear regression model adequately predicts key clinical outcomes after shockwave lithotripsy, ureteroscopic lithotripsy, and percutaneous nephrolithotomy. This model holds promise for further optimization via dataset expansion, preferably with multi-institutional data, and could be developed into a predictive nomogram in the future.

Double J stent reduces the efficacy of extracorporeal shock wave lithotripsy in the treatment of lumbar ureteral stones

Introduction

We evaluated the effect of the presence of a double J stent on the efficacy of extracorporeal shock wave lithotripsy (ESWL) in the treatment of lumbar ureteral stones.

Material and methods

Between January 2007 and February 2012, we performed a retrospective cohort study. Forty–four patients were treated by ESWL for lumbar ureteral stones and included into two groups for the analysis: group 1, non–stented (n = 27) and group 2, stented patients (n = 17). Treatment efficacy was evaluated by abdominal X–ray or CT–scan at 1 month. Stone–free patients and those with a residual stone ≤4 mm were considered to be cured.

Results

Mean stone size and density in groups 1 and 2 were 8.2mm/831HU, and 9.7 mm/986HU respectively. Both groups were comparable for age, BMI, stone size and density, number, and power of ESWL shots given. The success rates in groups 1 and 2 where 81.5% and 47.1%, respectively (p = 0.017). There was no difference between the groups for stones measuring 8 mm or less (p = 0.574). For stones >8 mm, the success rates were respectively 76% and 22.2% for groups 1 and 2 (p = 0.030). Logistic regression analysis revealed a higher failure rate when a double J stent was associated with a stone >8 mm (p = 0.033).

Conclusions

The presence of a double J stent affects the efficacy of ESWL in the treatment of lumbar ureteral stones. This effect is significant for stones >8 mm. Ureteroscopy should be considered as the first–line treatment in such patients.

Does previous failed ESWL have a negative impact of on the outcome of ureterorenoscopy? A matched pair analysis

This study aims to evaluate the outcome of ureteroscopy/ureterorenoscopy (URS) as a salvage procedure for stones resistant to extracorporeal shock wave lithotripsy (ESWL). Between January 2009 and January 2012, 313 patients with upper tract lithiasis were treated by URS. Among them, 87 (27.8 %) had undergone URS after prior ESWL failed to achieve stone clearance (Salvage group). These patients were matched with a group of patients who underwent URS as first-line modality (Primary group). Stone-free rates and adjuvant procedures represented the primary points for comparison. Secondary points for comparison included complications, procedure duration, total laser energy used and length of hospitalization. Matching was possible in all cases. Stone clearance rates were 73.6 and 82.8 % for the Salvage and Primary group, respectively. The difference in stone clearance rates between the two groups was not statistically significant (p = 0.186). A total of 11 patients (12.6 %) in the Primary group and 18 patients (20.7 %) in the Salvage group underwent an adjuvant procedure (p = 0.154). No statistically significant differences were noted in terms of complications, procedure duration and length of hospitalization. In the Primary group, the laser energy used for stone fragmentation was higher (p = 0.043). The rate of ureteric stenting at the end of the procedure was higher for the Salvage group (p = 0.030). Previous failed ESWL is not a predictor for unfavorable outcome of URS. Salvage URS is associated, however, with an increased need for ureteric stenting at the end of the procedure.

Development of a nomogram for predicting the stone-free rate after transurethral ureterolithotripsy using semi-rigid ureteroscope

OBJECTIVES

To develop and to internally validate a novel nomogram for predicting the stone-free rate after transurethral ureterolithotripsy.

METHODS

A total of 412 patients with 534 ureteral stones were treated with transurethral ureterolithotripsy using semi-rigid ureteroscopes. Treatment efficacy was evaluated 3 months after the procedure. Multivariate stepwise logistic regression analysis was used to identify independent predictors of being stone-free in the model-building set. A total of 427 stones (80% of 534) were randomly allocated for identification and statistical analysis to build the model, and the remaining 107 (20%) were used for cross-validation. A nomogram for the stone-free rate was developed based on the final logistic regression model.

RESULTS

Stone length, number of stones, stone location and the presence of pyuria were independent factors related to the stone-free rate after transurethral ureterolithotripsy treatment, and these were used to develop a nomogram. In this nomogram, the area under the receiver operating characteristic curve was 0.7432 for the nomogram, 0.5641 for stone size, 0.5908 for the number of stones, 0.6594 for stone location and 0.6076 for pyuria. Validation using 20% of the data also achieved a reasonable predictive accuracy (area under the receiver operating characteristic curve = 0.682).

CONCLUSIONS

The first nomogram for predicting the stone-free rate after transurethral ureterolithotripsy was developed. It has a reasonable predictive accuracy, and in combination with extracorporeal shock wave lithotripsy nomograms, it might be useful for deciding treatment methods.

Shock wave Lithotripsy in the Elderly: Our Experience Related to Literature Review

Background

The more and more common use of abdominal ultrasonography and of other imaging techniques, the increase of the life expectancy and therapies for calcium metabolism, has led to a higher diagnosis rate of renal stones in the elderly. At the moment, extracorporeal shock wave lithotripsy is considered the first-line therapy in the majority of reno-ureteral stones.

Objectives

To prove the efficacy and safety of extracorporeal shock wave lithotripsy also in the elderly population.

Materials and Methods

We proceeded to a retrospective study on patients aged over 70 years, who underwent SWL at our division from January 1996 to April 2005, with Storz Modulith SLX electromagnetic lithotripter. We defined as 'stone-free those patients who did not show any stone fragment in the following ultrasonography and abdomen X-ray control. In addition, we performed a medium/long-term follow-up. We adopted as “control group” 115 patients aged less than 60 years, who underwent lithotripsy with the same lithotripter from June 2007 to January 2008.

Results

In the short-term follow-up, at the end of the single treatment or of the course of treatments (1-3 months after treatment), we observed: 72.1% (83/115) stone-free subjects, 20% (23/115) of cases with stone fragments that could be eliminated (<4 mm), 3.5% (4/115) with stone fragments >4 mm, 4.3% (5/115) unchanged cases; 2 of these (1.7%) underwent endoscopic lithotripsy and one percutaneous lithotripsy (0.9%). Concerning the medium/long-term follow-up (mean 59.2 months, range 7 mo-108 mo), we observed: 59.8% (67/112) stone-free cases, 25.9% (29/112) recurring stones, 11.6% (13/112) re-growth, 2,7% (3/112) unchanged cases. In the short-term follow-up, comparing the study group with the control one we observed: – No statistically significant difference regarding the treatment side effects in the two groups; – A lower stone-free percentage in caliceal stones in the elderly than in the younger patients (SFR = 62.5% vs 70.3%) – A stone-free percentage for non-caliceal stones similar in the older and the young patients (SFR = 79.1% vs 80.4%).

Conclusions

Shock wave lithotripsy proves to be effective in the first-line treatment of renal stones in the elderly, yielding good results with no increase of side effects.

Is shock wave lithotripsy efficient for the elderly stone formers? Results of a matched-pair analysis

The aim of the study was to evaluate the impact of age on the efficacy of extracorporeal shock wave lithotripsy (SWL), in a comparative study based on the principles of matched-pair analysis. Over a period of 4 years, 2,311 patients were treated with SWL in a tertiary referral center. Patient and stone data were recorded in a prospective electronic database. Among these patients, 115 (4.97%) were older than 70 years of age and fulfilled the criteria for inclusion in the study (Group A). For the purposes of the comparative analysis, Group A patients were matched for gender and stone parameters (side, location of stone, and diameter ±2 mm) with a control group of patients under the age of 70 (Group B). Following matching, the patients' electronic medical records were reviewed, to identify SWL success rates at 3 months and McNemar's test was used to compare the efficacy of SWL between the two groups. Matching was possible in all cases. The results indicate that there were no statistically significant differences in the mean number of SWL sessions or in the mean number of impulses per session between the two groups. The overall stone clearance rate achieved by SWL alone was 71.3% for Group A and 73.9% for group B. Discordant pairs were found in 37 cases (in 17 pairs only patients in Group A became stone-free, while in 20 pairs only patients in Group B became stone-free). By using McNemar's test, the difference in stone clearance rates between the two groups was not found to be statistically significant (p = 0.742). A total of 22 patients (19.1%) in Group A and 17 patients (14.7%) in Group B underwent an adjuvant procedure to achieve stone clearance. McNemar's test also revealed the absence of any statistically significant difference in SWL success rates between older and younger patients in the subgroups of patients presenting with either ureteric or renal stones (p = 0.727 and p = 0.571, respectively). In conclusion, SWL is still considered one of the first-line tools for geriatric patients suffering from urolithiasis, as increased age alone does not seem to adversely affect the efficacy of SWL.

Extracorporeal shock wave lithotripsy of lower ureteric stones: Outcome and criteria for success

Objective

To evaluate the efficacy of extracorporeal shock wave lithotripsy (ESWL) for distal ureteric calculi (DUC) and to determine variables that could affect the outcome results.

Patients and methods

Between April 2004 and February 2008, 100 patients with a solitary DUC were treated with in situ ESWL using a lithotripter (Lithostar Plus, Siemens, Erlangen, Germany). The outcome of treatment was evaluated after 3 months. The patients’ clinical and radiological findings, as well as stone characteristics, were reviewed and correlated with the stone-free rate (SFR).

Results

After in situ ESWL, 84 patients (84%) were stone-free (after one session in 57 and after two in 27). From a univariate analysis only three factors had a significant impact on the SFR, i.e. the body mass index (BMI), stone length and stone width. The SFR was significantly lower in obese patients than in normal and overweight patients (P = 0.019). Stone width ⩾8 mm was associated with a SFR of 64% (14/22), compared with 89.7% (70/78) for those with a stone width of <8 mm (P = 0.007). The SFR was 86.8% (66/76) for a stone length of ⩽10 mm and 71% (17/24) for a stone length of >10 mm (P = 0.016). On multivariate analysis, BMI, stone width and stone length maintained their statistical significance.

Conclusion

Primary in situ ESWL remains an effective and safe form of treatment for DUC. The length and transverse diameter of the stone, together with the BMI of the patient, were the only significant predictors of the overall success of ESWL.

Shock-wave lithotripsy in the elderly: Safety, efficacy and special considerations

Purpose

Shock-wave lithotripsy (SWL) for elderly patients can be challenging. Patients often have a long-standing complex stone burden and significant comorbidities. We report a cohort of patients aged ⩾70 years who were treated by SWL, with special attention to treatment outcomes, complications and the need for adjuvant procedures.

Patients and methods

Over a period of 4 years, 2311 patients were treated with SWL in a tertiary referral centre. Among these patients, 137 were aged ⩾70 years (5.9%). Patient and stone data were obtained from an electronic database and the patients’ electronic medical records were reviewed.

Results

During the pre-procedural assessment, 29 patients (21.2%) were considered to be at high anaesthetic risk, due their comorbidities (American Society of Anesthesiology score 3+). In terms of stone burden, 16 stones (11.7%) were located in the distal ureter (mean stone diameter 7.9 mm) and 28 (20.4%) were in the proximal ureter (mean diameter 10.1 mm). In the kidney, 54 stones (39.4%) were in the renal pelvis, upper or mid calyx (mean diameter 10.6 mm), while 39 stones (28.5%) were in the lower calyx (mean diameter 10.1 mm). The median (range) number of SWL sessions per patient was 2.0 (1–3). The overall stone-free rate achieved by SWL alone was 63.5% (65.9% for ureteric stones and 62.4% for renal stones). In total, 38 patients (27.7%) had an adjuvant procedure to achieve stone clearance (ureteroscopy in 23, PCNL in 14 and laparoscopic ureterolithotomy in one case). Apart from six cases (4.3%) of ureteric obstruction due to steinstrasse, there were no severe complications noted.

Conclusions

The management of elderly patients presenting with urolithiasis is challenging, due to the presence of significant comorbidities. Careful assessment of an integrated management plan for geriatric patients with urolithiasis is essential, and SWL still remains a safe and efficient first-line tool in well-selected cases.

A passive acoustic device for real-time monitoring of the efficacy of shockwave lithotripsy treatment

Extracorporeal shockwave lithotripsy (ESWL) is the preferred modality for the treatment of renal and ureteric stone disease. Currently X-ray or ultrasound B-scan imaging are used to locate the stone and to check that it remains targeted at the focus of the lithotripter during treatment. Neither imaging modality is particularly effective in allowing the efficacy of treatment to be judged during the treatment session. A new device is described that, when placed on the patient's skin, can passively monitor the acoustic signals that propagate through the body after each lithotripter shock, and which can provide useful information on the effectiveness of targeting. These acoustic time histories are analyzed in real time to extract the two main characteristic peak amplitudes (m(1) and m(2)) and the time between these peaks (t(c)). A set of rules based on the acoustic parameters was developed during a clinical study in which a complete set of acoustic and clinical data was obtained for 30 of the 118 subjects recruited. The rules, which complied with earlier computational fluid dynamics (CFD) modeling and in vitro tests, allow each shock to be classified as "effective" or "ineffective." These clinically-derived rules were then applied in a second clinical study in which complete datasets were obtained for 49 of the 85 subjects recruited. This second clinical study demonstrated almost perfect agreement (kappa = 0.94) between the number of successful treatments, defined as >50% fragmentation as determined by X-ray at the follow-up appointment, and a device-derived global treatment score, TS(0), a figure derived from the total number of effective shocks in any treatment. The acoustic system is shown to provide a test of the success of the treatment that has a sensitivity of 91.7% and a specificity of 100%. In addition to the predictive capability, the device provides valuable real-time feedback to the lithotripter operator by indicating the effectiveness of each shock, plus an indication TS(t) of the cumulative effectiveness of the shocks given so far in any treatment, and trends in key parameters. This feedback would allow targeting adjustments to be made during treatment. An example is given of its application to mistargeting because of respiration.

Is extracorporeal shock wave lithotripsy the preferred treatment option for elderly patients with urinary stone? A multivariate analysis of the effect of patient age on treatment outcome

OBJECTIVES

To investigate the effect of patient age on the stone-free rate (SFR) in patients with urinary calculi treated by extracorporeal shockwave lithotripsy (ESWL).

PATIENTS AND METHODS

In all, 2192 solitary radio-opaque urinary stones of 5-15 mm were identified in adult patients receiving primary ESWL. Patients were divided into three age groups, i.e. < or = 40, 41-60 and >60 years (579, 1026 and 587 patients, respectively). Multiple logistic regression was used to assess the effect of age and other possible predicting factors (gender, stone characteristics, e.g. side, site and size, and the type of lithotripter used) on the SFR at 3 months after treatment.

RESULTS

The overall adjusted odds ratios (95% confidence interval) for the SFR for those aged 41-60 and >60 years (taking those aged < or= 40 years as the reference) were 0.708 (0.573-0.875; P = 0.001) and 0.643 (0.506-0.818; P < 0.001). However, if the patients were divided into those with renal or ureteric stones, only the SFR of the former was affected by age, and the adjusted odds ratios were 0.665 (0.512-0.864; P = 0.002) and 0.629 (0.470-0.841; P = 0.002), respectively. Ageing had no effect on the SFR for ureteric stones.

CONCLUSION

The SFR after ESWL for renal stones, but not ureteric stones, was significantly lower in older patients. Further studies on the effects of ageing on renal stone clearance after ESWL are needed to improve stone management in the elderly population.

Limitations of extracorporeal shock wave lithotripsy

PURPOSE OF REVIEW

Extracorporeal shock wave lithotripsy (ESWL) is the preferred modality for the treatment of renal and upper ureteric calculi. The present review focuses on the limitations of ESWL, where recent developments have tried to identify patients who are unlikely to succeed with ESWL and where improvements in shock wave delivery may increase successful stone fragmentation.

RECENT FINDINGS

Evaluation of patients prior to ESWL is especially important, and the use of imaging in the decision process, with the use of computed tomography attenuation values and skin-to-stone distance, can help improve our ability to identify suitable patients for shock wave treatment. Continued research into the methods of shock wave delivery techniques and lithotripter designs will help achieve better stone fragmentation rates with reduced side effects.

SUMMARY

The importance of traditional factors in predicting ESWL success, such as stone size, location, composition and renal anatomy, are well known. More recently, authors have created nomograms to predict stone-free outcome after ESWL. Others have used the information obtained from computed tomography to predict stone comminution. In addition, modifications in shock wave delivery by altering shock rate and voltage have been researched in an effort to improve shock wave efficacy.

A novel approach for accurate prediction of spontaneous passage of ureteral stones: Support vector machines

The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR) and accurately trained artificial neural networks (ANN) to those obtained with SVM, that is, the standard SVM, and the linear programming SVM (LP-SVM); the latter techniques show an improved performance. Moreover, we rank the prediction factors according to their importance using Fisher scores and the LP-SVM feature weights. A data set of 1163 patients affected by renal colic has been analyzed and restricted to single out a statistically coherent subset of 402 patients. Nine clinical factors are used as inputs for the classification algorithms, to predict one binary output. The algorithms are cross-validated by training and testing on randomly selected train- and test-set partitions of the data and reporting the average performance on the test sets. The SVM-based approaches obtained a sensitivity of 84.5% and a specificity of 86.9%. The feature ranking based on LP-SVM gives the highest importance to stone size, stone position and symptom duration before check-up. We propose a statistically correct way of employing LR, ANN and SVM for the prediction of spontaneous passage of ureteral stones in patients with renal colic. SVM outperformed ANN, as well as LR. This study will soon be translated into a practical software toolbox for actual clinical usage.

Artificial neural networks in urolithiasis

PURPOSE OF REVIEW

The management of urolithiasis is a clinical challenge worldwide which may result in difficulty in diagnosis, treatment and prevention of recurrence. Artificial neural networks (ANNs) are well described adjuncts to many aspects of clinical urological practice. We review literature published in on-line Medline-citable English language journals to assess whether ANNs are useful in clinician-led decision-making processes in urolithiasis.

RECENT FINDINGS

Studies have examined the role of ANNs in prediction of stone presence and composition, spontaneous passage, clearance and regrowth after treatment. These reports suggest that ANNs can identify important predictive variables and accurately predict treatment outcome.

SUMMARY

Although well described in general urological practice, there is comparatively little research into the role of ANNs in urolithiasis. Initial results appear promising; however, further prospective studies are necessary to determine if this mode of analysis is superior to standard statistical predictive methods.