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      External Validation of the STONE Score, a Clinical Prediction Rule for Ureteral Stone: An Observational Multi-institutional Study

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          Abstract

          Study objective

          The STONE score is a clinical decision rule that classifies patients with suspected nephrolithiasis into low-, moderate-, and high-score groups, with corresponding probabilities of ureteral stone. We evaluate the STONE score in a multi-institutional cohort compared with physician gestalt and hypothesize that it has a sufficiently high specificity to allow clinicians to defer computed tomography (CT) scan in patients with suspected nephrolithiasis.

          Methods

          We assessed the STONE score with data from a randomized trial for participants with suspected nephrolithiasis who enrolled at 9 emergency departments between October 2011 and February 2013. In accordance with STONE predictors, we categorized participants into low-, moderate-, or high-score groups. We determined the performance of the STONE score and physician gestalt for ureteral stone.

          Results

          Eight hundred forty-five participants were included for analysis; 331 (39%) had a ureteral stone. The global performance of the STONE score was superior to physician gestalt (area under the receiver operating characteristic curve=0.78 [95% confidence interval {CI} 0.74 to 0.81] versus 0.68 [95% CI 0.64 to 0.71]). The prevalence of ureteral stone on CT scan ranged from 14% (95% CI 9% to 19%) to 73% (95% CI 67% to 78%) in the low-, moderate-, and high-score groups. The sensitivity and specificity of a high score were 53% (95% CI 48% to 59%) and 87% (95% CI 84% to 90%), respectively.

          Conclusion

          The STONE score can successfully aggregate patients into low-, medium-, and high-risk groups and predicts ureteral stone with a higher specificity than physician gestalt. However, in its present form, the STONE score lacks sufficient accuracy to allow clinicians to defer CT scan for suspected ureteral stone.

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          Most cited references26

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          Applied Logistic Regression

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            Translating clinical research into clinical practice: impact of using prediction rules to make decisions.

            Clinical prediction rules, sometimes called clinical decision rules, have proliferated in recent years. However, very few have undergone formal impact analysis, the standard of evidence to assess their impact on patient care. Without impact analysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful. This paper reviews standards of evidence for developing and evaluating prediction rules; important differences between prediction rules and decision rules; how to assess the potential clinical impact of a prediction rule before translating it into a decision rule; methodologic issues critical to successful impact analysis, including defining outcome measures and estimating sample size; the importance of close collaboration between clinical investigators and practicing clinicians before, during, and after impact analysis; and the need to measure both efficacy and effectiveness when analyzing a decision rule's clinical impact. These considerations should inform future development, evaluation, and use of all clinical prediction or decision rules.
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              Validation, updating and impact of clinical prediction rules: a review.

              To provide an overview of the research steps that need to follow the development of diagnostic or prognostic prediction rules. These steps include validity assessment, updating (if necessary), and impact assessment of clinical prediction rules. Narrative review covering methodological and empirical prediction studies from primary and secondary care. In general, three types of validation of previously developed prediction rules can be distinguished: temporal, geographical, and domain validations. In case of poor validation, the validation data can be used to update or adjust the previously developed prediction rule to the new circumstances. These update methods differ in extensiveness, with the easiest method a change in model intercept to the outcome occurrence at hand. Prediction rules -- with or without updating -- showing good performance in (various) validation studies may subsequently be subjected to an impact study, to demonstrate whether they change physicians' decisions, improve clinically relevant process parameters, patient outcome, or reduce costs. Finally, whether a prediction rule is implemented successfully in clinical practice depends on several potential barriers to the use of the rule. The development of a diagnostic or prognostic prediction rule is just a first step. We reviewed important aspects of the subsequent steps in prediction research.
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                Author and article information

                Journal
                8002646
                557
                Ann Emerg Med
                Ann Emerg Med
                Annals of emergency medicine
                0196-0644
                1097-6760
                9 November 2015
                03 October 2015
                April 2016
                01 April 2016
                : 67
                : 4
                : 423-432.e2
                Affiliations
                Author notes
                [* ]Corresponding Author. ralph.wang@ 123456ucsf.edu
                Article
                NIHMS736143
                10.1016/j.annemergmed.2015.08.019
                4808407
                26440490
                6f3074b3-9040-456d-a769-ffb6551aba5e

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Emergency medicine & Trauma
                Emergency medicine & Trauma

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