Literature DB >> 20970283

Evaluation of Global Registry of Acute Cardiac Events and Thrombolysis in Myocardial Infarction scores in patients with suspected acute coronary syndrome.

Steve W Goodacre1, Mike Bradburn, Abdikudus Mohamed, Alasdair Gray.   

Abstract

PURPOSES: We aimed to evaluate the Global Registry of Acute Cardiac Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) scores in patients with suspected but not proven acute coronary syndrome (ACS). BASIC PROCEDURES: We conducted a secondary analysis of data from the RATPAC trial. Standardized data were collected from 2263 patients presenting to 6 emergency departments with suspected but not proven ACS. Patients were followed up by record review and postal questionnaire at 30 and 90 days after recruitment to identify major adverse events, defined as death, emergency revascularization, life-threatening arrhythmia, hospitalization for ACS, or nonfatal acute myocardial infarction (AMI). MAIN
FINDINGS: Data were available for 2243 patients (mean age, 54.5 years; 58% male). The major adverse event rate was 43 (2%) of 2243 after 30 days and 62 (3%) of 2243 after 90 days. The c statistics for 30-day events were 0.717 (95% confidence interval [CI], 0.698-0.735) for GRACE and 0.682 (95% CI, 0.662-0.701) for TIMI. The corresponding 90-day c statistics were 0.726 (95% CI, 0.707-0.745) for GRACE and 0.693 (95% CI, 0.674-0.712) for TIMI. The c statistic for patient age alone was 0.656 for 30-day events and 0.689 for 90-day events. PRINCIPAL
CONCLUSIONS: The GRACE and TIMI scores are little better than age alone as predictors of major adverse events in patients with suspected but not proven ACS, and thus add little to prognostic assessment of such patients. Crown
Copyright © 2012. Published by Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20970283     DOI: 10.1016/j.ajem.2010.09.013

Source DB:  PubMed          Journal:  Am J Emerg Med        ISSN: 0735-6757            Impact factor:   2.469


  4 in total

1.  Machine learning for risk prediction of acute coronary syndrome.

Authors:  Jacob P VanHouten; John M Starmer; Nancy M Lorenzi; David J Maron; Thomas A Lasko
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

2.  Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification.

Authors:  Saarang Panchavati; Carson Lam; Nicole S Zelin; Emily Pellegrini; Gina Barnes; Jana Hoffman; Anurag Garikipati; Jacob Calvert; Qingqing Mao; Ritankar Das
Journal:  Healthc Technol Lett       Date:  2021-08-31

3.  30 day predicted outcome in undifferentiated chest pain: multicenter validation of the HEART score in Tunisian population.

Authors:  Mohamed Hassene Khalil; Adel Sekma; Hajer Yaakoubi; Khaoula Bel Haj Ali; Mohamed Amine Msolli; Kaouthar Beltaief; Mohamed Habib Grissa; Hamdi Boubaker; Mohamed Sassi; Hamadi Chouchene; Youssef Hassen; Houda Ben Soltane; Zied Mezgar; Riadh Boukef; Wahid Bouida; Semir Nouira
Journal:  BMC Cardiovasc Disord       Date:  2021-11-19       Impact factor: 2.298

4.  Assessment of the 2016 National Institute for Health and Care Excellence high-sensitivity troponin rule-out strategy.

Authors:  Edward Watts Carlton; John William Pickering; Jaimi Greenslade; Louise Cullen; Martin Than; Jason Kendall; Richard Body; William A Parsonage; Ahmed Khattab; Kim Greaves
Journal:  Heart       Date:  2017-09-01       Impact factor: 5.994

  4 in total

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