Inter-observer variability amongst surgeons and radiologists in assessment of Guy’s Stone Score and S.T.O.N.E. nephrolithometry score: A prospective evaluation

Srivastava, Aneesh; Yadav, Priyank; Madhavan, Kumar; Sureka, Sanjoy Kumar; Singh, Uday Pratap; Kapoor, Rakesh; Ansari, M.S.; Lal, Hira; Mishra, Prabhakar

doi:10.1080/2090598x.2019.1703278

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…The overall S.T.O.N.E score showed good agreement between surgeons and radiologists (Fleiss К = 0.79) the same applies to Guy's score for all grades with moderate to very good agreement (Fleiss К: Grade I = 0.91; Grade 2: 0.53; Grade 3: 0.61; Grade 4: 0.84). 22 We were the only publication in our field to externally validate our data, ensuring the accuracy and reliability of our findings. This process involved an independent third-party review to verify the methodology and results, and by taking this extra step, we were able to provide further creditability to our conclusions.…”

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confidence: 89%

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

AlAzab,

Ghammaz,

Ardah

et al. 2023

IJNRD

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The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.N.E. and Guy's stone scores. Patients and Methods: This is a retrospective study that included 320 PCNL patients. Pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each were mean bootstrap estimate, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC. Results: Out of the 320 patients who underwent PCNL, the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65-0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63-0.85], 0.759, 0.72, and 0.769, respectively. The SVM results were 0.70 [0.60-0.79], 0.725, 0.74, and 0.751, respectively. The AUC of Guy's stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81-0.92], an accuracy of 0.70, and an AUC of 0.795, While the XGBoost results were 0.84 [0.78-0.91], 0.74, and 0.84, respectively. The SVM results were 0.86 [0.80-0.91], 0.79, and 0.858, respectively. Conclusion: MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLMs we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E scores.

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confidence: 89%

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

AlAzab,

Ghammaz,

Ardah

et al. 2023

IJNRD

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“…Srivastava et al also evaluated the effect of interobserver variability between surgeons and radiologists for Guy's and S.T.O.N.E scores and studied the agreement using the Fleiss coe cients. The overall S.T.O.N.E score showed good agreement between surgeons and radiologists (Fleiss = 0.79) the same applies to Guy's score for all grades with moderate to very good agreement (Fleiss : Grade I = 0.91; Grade 2: 0.53; Grade 3: 0.61; Grade 4: 0.84) [19].…”

Section: Discussionmentioning

confidence: 61%

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Al-Azab

Ghammaz

Ardah

et al. 2023

Preprint

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The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL), and compare its performance to the S.T.O.N.E. and Guy’s stone scores. This is a retrospective study that included 320 PCNL patients. The pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each MLM, were mean bootstrap estimate with CI, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC. Out of the 320 patients who underwent PCNL the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65–0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63–0.85], 0.759, 0.72, and 0.769 respectively. The SVM results were 0.70 [0.60–0.79], 0.725, 0.74, and 0.751 respectively. The AUC of Guy’s stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81–0.92], an accuracy of 0.70, and an AUC of 0.795. While the XGBoost results were 0.84 [0.78–0.91], 0.74, and 0.84 respectively. The SVM results were 0.86 [0.80–0.91], 0.79, and 0.858 respectively. MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLM systems we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E score.

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“…Hence this score can be set as a protocol for routine scheduling PCNL and not just for research papers. Landis and Koch suggested that this score is 86% reproducible among different observers of this scoring system 20,[30][31][32] . GSS is a better way to accurately predict outcomes, especially stone clearance, whether it is calculated via ultrasound KUB, X-ray KUB, or CT-KUB 33 .…”

Section: Discussionmentioning

confidence: 96%

“…GSS IV, is worse than simple stones of GSS I. Concurrently, there was confusion in classifying GSSII and GSSIII as difficult anatomy is not equally understandable. Moreover, there was a lack of a unified definition for partial staghorn stones 30 . Despite that, the GSS is a convenient, fast, and practically possible way for grading the complexity of stones before going for PCNL.…”

Section: Discussionmentioning

confidence: 99%

Guy’s Stone Score: A Predictive Tool for Outcomes of Percutaneous Nephrolithotomy

Sharif

Ehsan

Shaikh

et al. 2021

Int. j. endorsing health sci. res.

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Background: Predicting stone clearance before intervention can be useful to plan the modality of treatment and calculate the cost of procedures. Therefore, this study aims to determine the frequency of successful stone clearance in a patient undergoing percutaneous nephrolithotomy (PCNL) with different categories of Guy's Stone Score (GSS). Methodology: A total of 115 patients undergoing standard PCNL from January to December 2018 were included in this study. According to GSS I-IV, patients were then categorized into four groups after having pre-operative Computed Tomography of Kidney, Ureter, and Bladder (KUB). All the patients received standard general anesthesia and underwent standard PCNL in a prone position. The Percutaneous nephrostomy 18-gauge needle is passed into the pelvis of the kidney, Pelvi-calyceal system opacified and confirmed using fluoroscopy, a guidewire is passed, and 30 Fr Amplatz sheath is introduced after serial dilatation. A 26 Fr nephroscope is then inserted through the working sheath, and the stone is fragmented and removed. Finally, a nephrostomy drain tube is kept at the puncture site. The outcome was accessed in terms of stone clearance rate after PCNL on the 2nd postoperative day using the radiological modality of CT-KUB. Results: Among the 115 patients here were 48(41.7%) categorized as GSS I, 26(22.6%) categorized as GSSII, 20(17.4%) categorized as GSS III, and 21(18.3%) categorized as GSS IV. The overall stone clearance observed was 64.3%, individual stone clearance was GSS I=91.66%, GSS II=53.84%, GSS III=50%, and GSS IV=28.57%. Stone clearance among the different GSS categories was significantly different (p<0.01). Conclusion: The Guy's Stone Score, based on CT scan findings, is a significant system in predicting successful stone-free rates. It is a convenient, quick, and efficient tool for pre-operative assessment, and it can be incorporated as a mandatory protocol before planning PCNL.

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Inter-observer variability amongst surgeons and radiologists in assessment of Guy’s Stone Score and S.T.O.N.E. nephrolithometry score: A prospective evaluation

Cited by 8 publications

References 25 publications

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System

Guy’s Stone Score: A Predictive Tool for Outcomes of Percutaneous Nephrolithotomy

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