Prediction of Single Procedure Success Rate Using STONE Nephrolithometry Surgical Classification System With Strict Criteria for Surgical Outcome

被引:41
作者
Akhavein, Arash [1 ]
Henriksen, Carl [1 ]
Syed, Jamil [1 ]
Bird, Vincent G. [1 ]
机构
[1] Univ Florida, Dept Urol, Gainesville, FL 32610 USA
关键词
CLINICAL-RESEARCH OFFICE; PERCUTANEOUS NEPHROLITHOTOMY; RESIDUAL STONES;
D O I
10.1016/j.urology.2014.09.010
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
OBJECTIVE To 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 METHODS We 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. RESULTS Nephrolithometry 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. CONCLUSION With 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. (C) 2015 Elsevier Inc.
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页码:69 / 73
页数:5
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