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      Finding the optimal candidate for shock wave lithotripsy: external validation and comparison of five prediction models

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          Abstract

          We aimed to externally validate five previously published predictive models (Ng score, Triple D score, S 3HoCKwave score, Kim nomogram, Niwa nomogram) for shock wave lithotripsy (SWL) single-session outcomes in patients with a solitary stone in the upper ureter. The validation cohort included patients treated with SWL from September 2011 to December 2019 at our institution. Patient-related variables were retrospectively collected from the hospital records. Stone-related data including all measurements were retrieved from computed tomography prior to SWL. We estimated discrimination using area under the curve (AUC), calibration, and clinical net benefit based on decision curve analysis (DCA). A total of 384 patients with proximal ureter stones treated with SWL were included in the analysis. Median age was 55.5 years, and 282 (73%) of the sample were men. Median stone length was 8.0 mm. All models significantly predicted the SWL outcomes after one session. S 3HoCKwave score, Niwa, and Kim nomograms had the highest accuracy in predicting outcomes, with AUC 0.716, 0.714 and 0.701, respectively. These three models outperformed both the Ng (AUC: 0.670) and Triple D (AUC: 0.667) scoring systems, approaching statistical significance ( P = 0.05). Of all the models, the Niwa nomogram showed the strongest calibration and highest net benefit in DCA. To conclude, the models showed small differences in predictive power. The Niwa nomogram, however, demonstrated acceptable discrimination, the most accurate calibration, and the highest net benefit whilst having relatively simple design. Therefore, it could be useful for counselling patients with a solitary stone in the upper ureter.

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          Decision curve analysis: a novel method for evaluating prediction models.

          Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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            Calibration: the Achilles heel of predictive analytics

            Background The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Conclusion Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.
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              Epidemiology of stone disease across the world.

              Nephrolithiasis is a highly prevalent disease worldwide with rates ranging from 7 to 13% in North America, 5-9% in Europe, and 1-5% in Asia. Due to high rates of new and recurrent stones, management of stones is expensive and the disease has a high level of acute and chronic morbidity. The goal of this study is to review the epidemiology of stone disease in order to improve patient care. A review of the literature was conducted through a search on Pubmed®, Medline®, and Google Scholar®. This review was presented and peer-reviewed at the 3rd International Consultation on Stone Disease during the 2014 Société Internationale d'Urologie Congress in Glasgow. It represents an update of the 2008 consensus document based on expert opinion of the most relevant studies. There has been a rising incidence in stone disease throughout the world with a narrowing of the gender gap. Increased stone prevalence has been attributed to population growth and increases in obesity and diabetes. General dietary recommendations of increased fluid, decreased salt, and moderate intake of protein have not changed. However, specific recommended values have either changed or are more frequently reported. Geography and environment influenced the likelihood of stone disease and more information is needed regarding stone disease in a large portion of the world including Asia and Africa. Randomized controlled studies are lacking but are necessary to improve recommendations regarding diet and fluid intake. Understanding the impact of associated conditions that are rapidly increasing will improve the prevention of stone disease.
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                Author and article information

                Contributors
                marcin.popiolek@regionorebrolan.se
                Journal
                Urolithiasis
                Urolithiasis
                Urolithiasis
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2194-7228
                2194-7236
                7 April 2023
                7 April 2023
                2023
                : 51
                : 1
                : 66
                Affiliations
                [1 ]GRID grid.15895.30, ISNI 0000 0001 0738 8966, Department of Urology, Faculty of Medicine and Health, , Örebro University, ; 701 85 Örebro, Sweden
                [2 ]GRID grid.15895.30, ISNI 0000 0001 0738 8966, Department of Radiology, Faculty of Medicine and Health, , Örebro University, ; Örebro, Sweden
                [3 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Department of Clinical Sciences, Division of Infection Medicine, , Lund University, ; Lund, Sweden
                [4 ]GRID grid.413823.f, ISNI 0000 0004 0624 046X, Department of Urology Helsingborg Hospital, ; Helsingborg, Sweden
                Article
                1444
                10.1007/s00240-023-01444-4
                10082105
                37027057
                2dcda36f-b274-487d-ba5d-781dfe41add7
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 13 March 2023
                : 30 March 2023
                Funding
                Funded by: Örebro County Council
                Award ID: OLL-935231
                Award ID: OLL-979997
                Award Recipient :
                Funded by: Örebro University
                Categories
                Research
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                shock wave lithotripsy,ureteral stones,nomograms,validation,outcomes

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