کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3899027 1250313 2015 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of Single Procedure Success Rate Using S.T.O.N.E. Nephrolithometry Surgical Classification System With Strict Criteria for Surgical Outcome
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی بیماری‌های کلیوی
پیش نمایش صفحه اول مقاله
Prediction of Single Procedure Success Rate Using S.T.O.N.E. Nephrolithometry Surgical Classification System With Strict Criteria for Surgical Outcome
چکیده انگلیسی

ObjectiveTo evaluate the S.T.O.N.E. nephrolithometry scoring system for percutaneous nephrolithotomy using computerized tomography (CT) imaging with strict criteria for stone clearance.Materials and MethodsWe analyzed a cohort of 122 patients who consecutively underwent primary percutaneous nephrolithotomy from July 2010 to March 2012 at our university-based referral hospital. All patients routinely have preoperative and postoperative CT imaging for stone burden determination. Primary outcome (residual stone) was scored as 0-2, 3-4, and >4 mm. All S.T.O.N.E. nephrolithometry parameters were recorded and scored as per published definition. The t test was used for continuous variables, and the chi-square testing or the Fisher exact test (when counts were small) was used for categorical covariates. S.T.O.N.E. score correlation with stone-free status was analyzed by logistic regression.ResultsNephrolithometry score ranged from 5 to 13 with a mean of 9.5. Postoperative CT for residual stone showed 67 (54.9%), 26 (21.3%), and 29 (23.8%) patients had 0-2, 3-4, and >4 mm residual stone, respectively. Mean nephrolithometry scores for residual stone of 0-2, 3-4, and >4 mm were 8.87, 9.73, and 10.79 respectively (P <.0001). There were 11 (9.8%) complications.ConclusionWith use of strict CT imaging criteria for assessment of residual stone status, the S.T.O.N.E. scoring system is reproducible and predictive of treatment success. Further investigation is required to both validate this model and to determine if other predictive parameters will improve it as a predictive model.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Urology - Volume 85, Issue 1, January 2015, Pages 69–73
نویسندگان
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