Outcomes of more than 1 000 percutaneous nephrolithotomies and validation of Guy's stone score

Melo, Petrônio Augusto de Souza; Vicentini, Fábio Carvalho; Beraldi, Artur Agostinho; Hisano, Marcelo; Murta, Cláudio Bovolenta; Claro, Joaquim Francisco de Almeida

doi:10.1111/bju.14129

Cited by 21 publications

(27 citation statements)

References 19 publications

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“…3,13 Several case series validated the performance of this system to predict post-PCNL stonefree status (AUC = 0.69-0.79). [14][15][16] Yet despite its efficacy, GSS was originally developed by qualifying image analyses based on expert opinion, not on data-driven analyses. Obviously, there are a number of preoperative and intraoperative variables that cannot be processed by this system.…”

Section: Discussionmentioning

confidence: 99%

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

Aminsharifi

Irani

Tayebi

et al. 2020

Journal of Endourology

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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.

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Section: Discussionmentioning

confidence: 99%

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

Aminsharifi

Irani

Tayebi

et al. 2020

Journal of Endourology

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“…In the case of infection associated to an obstructive ureteral stone, decompression with a ureteral stent or a percutaneous nephrostomy or even with ureteroscopy and intracorporeal lithotripsy or stone removal with baskets in favorable cases should be performed promptly once these patients can progress to sepsis in up to 25% of the cases (41,43). External strings for double J stents that can be removed at the office or even at home should be encouraged after uneventful procedures avoiding a hospital visit (44).…”

Section: Endourologymentioning

confidence: 99%

“…Percutaneous nephrolithotomy (PCNL) is generally a bigger procedure requiring general anesthesia and intensive care especially in older patients with co-morbidities like obesity, diabetes, hypertension or other cardiac diseases or in cases of big staghorn stones (44).…”

Section: Endourologymentioning

confidence: 99%

The impact of COVID-19 in medical practice. A review focused on Urology

et al. 2021

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COVID-19 pandemic is a rapidly spreading virus that is changing the World and the way doctors are practicing medicine. The huge number of patients searching for medical care and needing intensive care beds led the health care system to a burnout status especially in places where the care system was already overloaded. In this setting, and also due to the absence of a specific treatment for the disease, health authorities had to opt for recommending or imposing social distancing to relieve the health system and reduce deaths. All other medical specialties nondirectly related to the treatment of COVID-19 had to interrupt or strongly reduce their activities in order to give room to seriously ill patients, since no one knows so far the real extent of the virus damage on human body and the consequences of doing non deferrable procedures in this pandemic era. Despite not been a urological disease, the urologist needs to be updated on how to deal with these patients and how to take care of himself and of the medical team he works with. The aim of this article is to review briefly some practical aspects of COVID-19 and its implications in the urological practice in our country.

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“…Of our patients, 38% were ≥ GSS III and 80% were ≥ GSS II. Whereas SFR may vary with the proportion and distribution of various GSS grades [18], we can confirm that GSS is accurate in a setting which applies mainly to the developing world with a majority of patients having complex-to-very complex stones. Despite this, our overall SFR was good with 89%.…”

Section: Discussionmentioning

confidence: 57%

Guy’s Stone Score as a Predictor for Stone-Free Rate and Complications in Percutaneous Nephrolithotomy: A Single-Center Report from a Stone Belt Country

Rashid

Khalid

Friad³

et al. 2020

Urol Int

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Objectives: To evaluate Guy's Stone Score (GSS) as a grading system for the complexity of renal calculi before percutaneous nephrolithotomy (PCNL) and as a predictor for stonefree rate (SFR) and potential complications in the setting of a developing country with many cases of advanced complex stone disease. Patients and Methods: We performed a prospective study on 115 patients with renal stones who had PCNL between August 2017 and October 2018. Stone complexity was classified according to GSS using preoperative imaging. The rates of perioperative complications were assessed using the Clavien grading system, and the SFR was assessed in the 3rd postoperative week by abdominal X-ray and renal ultrasound. The χ 2 and one-way ANOVA tests were used for statistical analysis. A p value ≤0.05 was considered significant. Results: The study included 115 patients (77 males) with a mean age of 50.2 (±13.3) years. Cases were classified according to GSS into 4 grades; there were 24 (20.9%) grade I, 48 (41.7%) grade II, 25 (21.7%) grade III, and 18 (15.7%) grade IV cases. There was a significant inverse correlation between GSS grade and SFR (p < 0.001). The rate of complications using the Clavien grading system were significantly higher in GSS grades III and IV (p = 0.002). So were the rates of blood transfusion, the need for > 1 access tract, and the duration of hospitalization. Conclusion: GSS is a simple and easily reproducible system to classify the complexity of renal stones. In our setting, it also proved an excellent tool to predict SFR, perioperative complications, and associated ancillary procedures. Ultimately, it can help surgeons and, above all, patients to make a more informed decision about their surgery.

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Outcomes of more than 1 000 percutaneous nephrolithotomies and validation of Guy's stone score

Abstract: In a high-volume centre, PCNL was a reliable surgical technique, with low morbidity and short hospital stay. GSS was confirmed to be a very useful tool for predicting the outcomes of PCNL, and its use should be encouraged.

Cited by 21 publications

References 19 publications

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

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

The impact of COVID-19 in medical practice. A review focused on Urology

Guy’s Stone Score as a Predictor for Stone-Free Rate and Complications in Percutaneous Nephrolithotomy: A Single-Center Report from a Stone Belt Country

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