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      The impact of the HEART risk score in the early assessment of patients with acute chest pain: design of a stepped wedge, cluster randomised trial

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          Abstract

          Background

          Chest pain remains a diagnostic challenge: physicians do not want to miss an acute coronary syndrome (ACS), but, they also wish to avoid unnecessary additional diagnostic procedures. In approximately 75% of the patients presenting with chest pain at the emergency department (ED) there is no underlying cardiac cause. Therefore, diagnostic strategies focus on identifying patients in whom an ACS can be safely ruled out based on findings from history, physical examination and early cardiac marker measurement. The HEART score, a clinical prediction rule, was developed to provide the clinician with a simple, early and reliable predictor of cardiac risk. We set out to quantify the impact of the use of the HEART score in daily practice on patient outcomes and costs.

          Methods/Design

          We designed a prospective, multi-centre, stepped wedge, cluster randomised trial. Our aim is to include a total of 6600 unselected chest pain patients presenting at the ED in 10 Dutch hospitals during an 11-month period. All clusters (i.e. hospitals) start with a period of ‘usual care’ and are randomised in their timing when to switch to ‘intervention care’. The latter involves the calculation of the HEART score in each patient to guide clinical decision; notably reassurance and discharge of patients with low scores and intensive monitoring and early intervention in patients with high HEART scores. Primary outcome is occurrence of major adverse cardiac events (MACE), including acute myocardial infarction, revascularisation or death within 6 weeks after presentation. Secondary outcomes include occurrence of MACE in low-risk patients, quality of life, use of health care resources and costs.

          Discussion

          Stepped wedge designs are increasingly used to evaluate the real-life effectiveness of non-pharmacological interventions because of the following potential advantages: (a) each hospital has both a usual care and an intervention period, therefore, outcomes can be compared within and across hospitals; (b) each hospital will have an intervention period which enhances participation in case of a promising intervention; (c) all hospitals generate data about potential implementation problems. This large impact trial will generate evidence whether the anticipated benefits (in terms of safety and cost-effectiveness) of using the HEART score will indeed be achieved in real-life clinical practice.

          Trial registration

          ClinicalTrials.gov 80-82310-97-12154.

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          Most cited references14

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          Assessing the generalizability of prognostic information.

          Physicians are often asked to make prognostic assessments but often worry that their assessments will prove inaccurate. Prognostic systems were developed to enhance the accuracy of such assessments. This paper describes an approach for evaluating prognostic systems based on the accuracy (calibration and discrimination) and generalizability (reproducibility and transportability) of the system's predictions. Reproducibility is the ability to produce accurate predictions among patients not included in the development of the system but from the same population. Transportability is the ability to produce accurate predictions among patients drawn from a different but plausibly related population. On the basis of the observation that the generalizability of a prognostic system is commonly limited to a single historical period, geographic location, methodologic approach, disease spectrum, or follow-up interval, we describe a working hierarchy of the cumulative generalizability of prognostic systems. This approach is illustrated in a structured review of the Dukes and Jass staging systems for colon and rectal cancer and applied to a young man with colon cancer. Because it treats the development of the system as a "black box" and evaluates only the performance of the predictions, the approach can be applied to any system that generates predicted probabilities. Although the Dukes and Jass staging systems are discrete, the approach can also be applied to systems that generate continuous predictions and, with some modification, to systems that predict over multiple time periods. Like any scientific hypothesis, the generalizability of a prognostic system is established by being tested and being found accurate across increasingly diverse settings. The more numerous and diverse the settings in which the system is tested and found accurate, the more likely it will generalize to an untested setting.
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            ACC/AHA 2007 guidelines for the management of patients with unstable angina/non-ST-Elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the 2002 Guidelines for the Management of Patients With Unstable Angina/Non-ST-Elevation Myocardial Infarction) developed in collaboration with the American College of Emergency Physicians, the Society for Cardiovascular Angiography and Interventions, and the Society of Thoracic Surgeons endorsed by the American Association of Cardiovascular and Pulmonary Rehabilitation and the Society for Academic Emergency Medicine.

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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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                Author and article information

                Contributors
                Journal
                BMC Cardiovasc Disord
                BMC Cardiovasc Disord
                BMC Cardiovascular Disorders
                BioMed Central
                1471-2261
                2013
                26 September 2013
                : 13
                : 77
                Affiliations
                [1 ]Julius Center for Health Sciences and Primary care, University Medical Center, Stratenum 6.131, PO box 85500, 3508AB, Utrecht, the Netherlands
                [2 ]Department of Cardiology, Zuwe Hofpoort Hospital, Woerden, the Netherlands
                [3 ]Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
                Article
                1471-2261-13-77
                10.1186/1471-2261-13-77
                3849098
                24070098
                4af831db-a724-4e71-b3a8-9b31c1b44e59
                Copyright © 2013 Poldervaart et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 July 2013
                : 17 September 2013
                Categories
                Study Protocol

                Cardiovascular Medicine
                heart score,chest pain,clinical prediction rule,risk score implementation,impact,stepped wedge design,cluster randomised trial

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