Clinical Nomograms to Predict Stone-Free Rates after Shock-Wave Lithotripsy: Development and Internal-Validation

• Published in: PloS one Vol. 11; no. 2; p. e0149333
• Main Authors: Kim, Jung Kwon, Ha, Seung Beom, Jeon, Chan Hoo, Oh, Jong Jin, Cho, Sung Yong, Oh, Seung-June, Kim, Hyeon Hoe, Jeong, Chang Wook
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.02.2016; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Analysis; Biology and Life Sciences; Body mass index; Calibration; Care and treatment; CAT scans; Clinical decision making; Cohort Studies; Combined Modality Therapy; Computed tomography; Construction materials; Decision making; Female; Humans; Institutions; Kidney Calculi - diagnosis; Kidney Calculi - therapy; Kidneys; Lithotripsy; Lithotripsy - methods; Lithotripsy - standards; Male; Mathematical models; Medicine and Health Sciences; Middle Aged; Multivariate analysis; Nomograms; Patients; Physical Sciences; Prediction models; Prognosis; Prostate cancer; Regression analysis; Regression models; Reproducibility of Results; Research and Analysis Methods; Risk Factors; Stone; Studies; Success; Tomography, X-Ray Computed; Treatment Outcome; Ureter; Urinary Calculi - diagnosis; Urinary Calculi - therapy; Urinary stones; Urinary tract; Urogenital system; Urology; Variables; Wave power
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0149333

Shock-wave lithotripsy (SWL) is accepted as the first line treatment modality for uncomplicated upper urinary tract stones; however, validated prediction models with regards to stone-free rates (SFRs) are still needed. We aimed to develop nomograms predicting SFRs after the first and within the third session of SWL. Computed tomography (CT) information was also modeled for constructing nomograms. From March 2006 to December 2013, 3028 patients were treated with SWL for ureter and renal stones at our three tertiary institutions. Four cohorts were constructed: Total-development, Total-validation, CT-development, and CT-validation cohorts. The nomograms were developed using multivariate logistic regression models with selected significant variables in a univariate logistic regression model. A C-index was used to assess the discrimination accuracy of nomograms and calibration plots were used to analyze the consistency of prediction. The SFR, after the first and within the third session, was 48.3% and 68.8%, respectively. Significant variables were sex, stone location, stone number, and maximal stone diameter in the Total-development cohort, and mean Hounsfield unit (HU) and grade of hydronephrosis (HN) were additional parameters in the CT-development cohort. The C-indices were 0.712 and 0.723 for after the first and within the third session of SWL in the Total-development cohort, and 0.755 and 0.756, in the CT-development cohort, respectively. The calibration plots showed good correspondences. We constructed and validated nomograms to predict SFR after SWL. To the best of our knowledge, these are the first graphical nomograms to be modeled with CT information. These may be useful for patient counseling and treatment decision-making.