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      Optimal Skin-to-Stone Distance Is a Positive Predictor for Successful Outcomes in Upper Ureter Calculi following Extracorporeal Shock Wave Lithotripsy: A Bayesian Model Averaging Approach

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          Abstract

          Objectives

          To investigate whether skin-to-stone distance (SSD), which remains controversial in patients with ureter stones, can be a predicting factor for one session success following extracorporeal shock wave lithotripsy (ESWL) in patients with upper ureter stones.

          Patients and Methods

          We retrospectively reviewed the medical records of 1,519 patients who underwent their first ESWL between January 2005 and December 2013. Among these patients, 492 had upper ureter stones that measured 4–20 mm and were eligible for our analyses. Maximal stone length, mean stone density (HU), and SSD were determined on pretreatment non-contrast computed tomography (NCCT). For subgroup analyses, patients were divided into four groups. Group 1 consisted of patients with SSD<25 th percentile, group 2 consisted of patients with SSD in the 25 th to 50 th percentile, group 3 patients had SSD in the 50 th to 75 th percentile, and group 4 patients had SSD≥75 th percentile.

          Results

          In analyses of group 2 patients versus others, there were no statistical differences in mean age, stone length and density. However, the one session success rate in group 2 was higher than other groups (77.9% vs. 67.0%; P = 0.032). The multivariate logistic regression model revealed that shorter stone length, lower stone density, and the group 2 SSD were positive predictors for successful outcomes in ESWL. Using the Bayesian model-averaging approach, longer stone length, lower stone density, and group 2 SSD can be also positive predictors for successful outcomes following ESWL.

          Conclusions

          Our data indicate that a group 2 SSD of approximately 10 cm is a positive predictor for success following ESWL.

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          Most cited references30

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          Bayesian clinical trials.

          Bayesian statistical methods are being used increasingly in clinical research because the Bayesian approach is ideally suited to adapting to information that accrues during a trial, potentially allowing for smaller more informative trials and for patients to receive better treatment. Accumulating results can be assessed at any time, including continually, with the possibility of modifying the design of the trial, for example, by slowing (or stopping) or expanding accrual, imbalancing randomization to favour better-performing therapies, dropping or adding treatment arms, and changing the trial population to focus on patient subsets that are responding better to the experimental therapies. Bayesian analyses use available patient-outcome information, including biomarkers that accumulating data indicate might be related to clinical outcome. They also allow for the use of historical information and for synthesizing results of relevant trials. Here, I explain the rationale underlying Bayesian clinical trials, and discuss the potential of such trials to improve the effectiveness of drug development.
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            Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data.

            Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests. We present the Bayesian model averaging (BMA) method for gene selection and classification of microarray data. Typical gene selection and classification procedures ignore model uncertainty and use a single set of relevant genes (model) to predict the class. BMA accounts for the uncertainty about the best set to choose by averaging over multiple models (sets of potentially overlapping relevant genes). We have shown that BMA selects smaller numbers of relevant genes (compared with other methods) and achieves a high prediction accuracy on three microarray datasets. Our BMA algorithm is applicable to microarray datasets with any number of classes, and outputs posterior probabilities for the selected genes and models. Our selected models typically consist of only a few genes. The combination of high accuracy, small numbers of genes and posterior probabilities for the predictions should make BMA a powerful tool for developing diagnostics from expression data. The source codes and datasets used are available from our Supplementary website.
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              A prospective multivariate analysis of factors predicting stone disintegration by extracorporeal shock wave lithotripsy: the value of high-resolution noncontrast computed tomography.

              To assess the value of noncontrast computed tomography (NCCT) as a possible predictor of renal stone disintegration by shock wave lithotripsy (SWL). The study included 120 consecutive patients (71 males, 49 females; mean age: 42.6 yr) with a solitary renal stone of 0.5-2.5 cm in length. NCCT was performed using a multidetector row CT scanner at 120 KV and 240 mA, with 1.25-mm collimation. A bone window was used to measure stone attenuation values. SWL was performed with an electromagnetic lithotripter. Failure of disintegration was defined as no fragmentation of the stone after three sessions. The impact of patients' sex, age, and body mass index (BMI) and the stones' laterality, location, volume, mean attenuation value, and the skin-to-stone distance on disintegration were evaluated by univariate and multivariate analyses. Failure of disintegration was observed in 15 patients (12.5%). BMI and stone density >1000 HU were the significant independent predictors of failure (p=0.04 and 0.02, respectively). The success rate of extracorporeal SWL at 3 mo was 87.5% (105 of 120 patients); 90 patients were stone free and 15 had residual fragments 1000 HU.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 December 2015
                2015
                : 10
                : 12
                : e0144912
                Affiliations
                [1 ]Department of Urology, Gangnam Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul, Korea
                [2 ]Department of Urology, Incheon Red Cross Hospital, Incheon, Korea
                [3 ]Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul, Korea
                [4 ]Department of Urology, Severance Check-Up, Yonsei University Health System, Seoul, Korea
                Sun Yat-sen University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KSC HDJ JYL. Performed the experiments: KSC HDJ JYL. Analyzed the data: KSC HDJ JYL DYC. Contributed reagents/materials/analysis tools: KSC HDJ JYL. Wrote the paper: KSC HDJ JYL DYC YJK WSJ JKK WSH YDC.

                Article
                PONE-D-15-30534
                10.1371/journal.pone.0144912
                4699456
                26659086
                8cf5db15-a4d3-4c53-8725-7bdbc8c26674
                © 2015 Cho et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 13 July 2015
                : 25 November 2015
                Page count
                Figures: 2, Tables: 4, Pages: 11
                Funding
                This study was supported by a faculty research grant from the Yonsei University College of Medicine for 2014 (6-2014-0156).
                Categories
                Research Article
                Custom metadata
                Data are available from the Severance Hospital and Urological Science Institutional Data Access and Ethics Committee due to patients’ privacy and research ethics. Readers can send requests for data to the corresponding author ( joouro@ 123456yuhs.ac ).

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