Clinician Versus Nomogram Predicted Estimates of Kidney Stone Recurrence Risk

2020 ◽  
Author(s):  
Connor M. Forbes ◽  
Allison B. McCoy ◽  
Ryan S. Hsi
Keyword(s):  
Kidney Stone ◽  
Recurrence Risk ◽  
Stone Recurrence

scholarly journals Dietary vinegar prevents kidney stone recurrence via epigenetic regulations

EBioMedicine ◽  
2019 ◽  
Vol 45 ◽  
pp. 231-250 ◽  
Author(s):  
Wei Zhu ◽  
Yang Liu ◽  
Yu Lan ◽  
Xiaohang Li ◽  
Lianmin Luo ◽  
...  
Keyword(s):  
Kidney Stone ◽  
Stone Recurrence ◽  
Epigenetic Regulations

The Association Between 24-Hour Urine and Stone Recurrence Among High Risk Kidney Stone Formers: A Population Level Assessment

Urology ◽  
2020 ◽  
Vol 144 ◽  
pp. 71-76
Author(s):  
Patrick C. Samson ◽  
Sarah K. Holt ◽  
Ryan S. Hsi ◽  
Mathew D. Sorensen ◽  
Jonathan D. Harper
Keyword(s):  
High Risk ◽  
Kidney Stone ◽  
Population Level ◽  
Stone Recurrence ◽  
Stone Formers

scholarly journals A Nomogram for the Prediction of Kidney Stone Recurrence

2014 ◽  
Vol 25 (12) ◽  
pp. 2685-2687 ◽  
Author(s):  
Brian H. Eisner ◽  
David S. Goldfarb
Keyword(s):  
Kidney Stone ◽  
Stone Recurrence

MP43-04 24-HOUR URINE ANALYSES AND KIDNEY STONE RECURRENCE IN CHILDREN AND ADOLESCENTS

2016 ◽  
Vol 195 (4S) ◽  
Author(s):  
Gregory Tasian ◽  
Abdo Kabarriti ◽  
Angela Kalmus ◽  
Susan Furth
Keyword(s):  
Children And Adolescents ◽  
Kidney Stone ◽  
Stone Recurrence

scholarly journals Kidney Stone Recurrence among Children and Adolescents

2017 ◽  
Vol 197 (1) ◽  
pp. 246-252 ◽  
Author(s):  
Gregory E. Tasian ◽  
Abdo E. Kabarriti ◽  
Angela Kalmus ◽  
Susan L. Furth
Keyword(s):  
Children And Adolescents ◽  
Kidney Stone ◽  
Stone Recurrence

scholarly journals MP54-11 5-YEAR RECURRENCE RISK STRATIFICATION USING THE RECURRENCE OF KIDNEY STONE (ROKS) NOMOGRAM

2021 ◽  
Vol 206 (Supplement 3) ◽  
Author(s):  
Nicholas Kavoussi ◽  
Alexandre Da Silva ◽  
Allison McCoy ◽  
Tatsuki Koyama ◽  
Ryan Hsi
Keyword(s):  
Risk Stratification ◽  
Kidney Stone ◽  
Recurrence Risk

scholarly journals PD35-02 APPLICATION OF ARTIFICIAL INTELLIGENCE TOOL TO IDENTIFY PATIENTS AT HIGH RISK FOR SYMPTOMATIC KIDNEY STONE RECURRENCE

2020 ◽  
Vol 203 ◽  
pp. e717-e718
Author(s):  
Reza Goharderakhshan* ◽  
Drew Clausen ◽  
Oleg Shvarts ◽  
Michelle West ◽  
Ronald Loo
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Kidney Stone ◽  
Stone Recurrence

scholarly journals P0175DETERMINANTS OF RENAL PAPILLARY APPEARANCE IN RENAL STONE FORMERS: AN IN-DEPTH EXAMINATION

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Matteo Bargagli ◽  
Rossella De Leonardis ◽  
Mauro Ragonese ◽  
Angelo Totaro ◽  
Francesco Pinto ◽  
...  
Keyword(s):  
Kidney Stones ◽  
Kidney Stone ◽  
Medical Condition ◽  
Stone Formation ◽  
Blood Parameters ◽  
Stone Disease ◽  
Stone Composition ◽  
Stone Recurrence ◽  
Stone Formers

Abstract Background and Aims Nephrolithiasis is a medical condition characterized by high prevalence among the general population both in Europe and in the U.S. and it is responsible for high costs reaching up to $10 billion per year. It is associated with specific comorbidities such as obesity, arterial hypertension, diabetes mellitus, metabolic syndrome and chronic kidney disease. Kidney stones development is believed to start either from Randall’s plaques or from stone plugs. Both these lesions can be seen on renal papillary surfaces, but what promotes the formation of plaques and plugs is not entirely understood. The aim of this study is to investigate the association between the urinary metabolic milieu and a published endoscopic papillary evaluation score (PPLA). We also evaluated the correlation of PPLA score with kidney stone recurrence during follow-up. Method We prospectively enrolled 31 stone forming patients who undergone retrograde intrarenal surgery procedures. Visual inspection of the accessible renal papillae was performed in order to calculate the PPLA score based on the appearance of ductal plugging, surface pitting, loss of papillary contour and Randall’s plaque extension. Demographic information, blood samples, 24h urine collections and kidney stone events during follow-up were collected. Stone composition was analyzed using infrared-spectroscopy. Relative urinary supersaturations (RSS) for calcium oxalate (CaOx), calcium phosphate (CaPi) and uric acid (UA) were calculated using the Equil2 software. PPLA score > 3 was defined as high. Results Median follow-up period was 11 (min/max 5, 34) months. PPLA score was inversely correlated with BMI (rho = −0.39, p = 0.035) and history of recurrent kidney stones (median PPLA 5.0 vs 2.5, p = 0.029), these results were confirmed when PPLA was considered as a categorical variable (median BMI 27 vs 24, recurrent stone disease 12 vs 62%, p= 0.006). Furthermore, high PPLA score was associated with lower odds of new kidney stone events during follow-up (OR 0.154, 95% confidence interval 0.024, 0.998, p = 0.05). No significant correlations were found between PPLA score, stone composition, blood parameters, 24h urine solute excretions and RSS for CaOx, CaPi and UA. Conclusion Different papillary abnormalities seem to be linked to specific mechanisms of stone formation. Although data regarding PPLA score are inconsistent, it may be a valid asset for both medical and surgical management of nephrolithiasis. Larger, long-term prospective clinical studies need to be conducted to assess the validity of PPLA score system in evaluating risk of stone recurrence.

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