Literature DB >> 17499864

Potential demographic and baselines variables for risk stratification of high-risk post-myocardial infarction patients in the era of implantable cardioverter-defibrillator--a prognostic indicator.

Yee Guan Yap1, Trinh Duong, Martin Bland, Marek Malik, Christian Torp-Pedersen, Lars Køber, Stuart J Connolly, Mark M Gallagher, A John Camm.   

Abstract

BACKGROUND: Risk stratification after myocardial infarction (MI) remains expensive and disappointing. We designed a prognostic indicator using demographic information to select patients at risk of dying after MI. METHOD AND
RESULTS: We combined individual patient data from the placebo arms of EMIAT, CAMIAT, TRACE and DIAMOND-MI with LVEF <or=40% or ventricular arrhythmias (i.e. >10 ventricular premature beats/hour or a run of ventricular tachycardia). Risk factors for mortality beginning at day 45 post-MI up to 2 years were examined using Cox regression analysis. Risk scores were derived from the equation of a Cox regression model containing only significant variables. The prognostic index was the sum of the individual contribution from the risk factors. 2707 patients were pooled (age: 66 (23-92) years, 78.8% M) with 480 deaths at 2-years (44% arrhythmic and 35.6% non-arrhythmic cardiac deaths). Variables predicting mortality were age, sex, previous MI or angina, hypertension, diabetes, systolic blood pressure, heart rate, NYHA functional class and non-Q wave infarct on electrocardiogram. Distinct survival curves were obtained for 3 risk groups based on the median and inter-quartile range for the prognostic index. In the high-risk group, up to 40% of patients died (all-cause mortality), 19.1% died of arrhythmic and 18.2% died of non-arrhythmic cardiac causes at 2-years.
CONCLUSION: In post-MI patients with LVEF <or=40% or frequent ventricular premature beats, the additional use of a simple prognostic indicator based on demographic information was able to provide clinically meaningful risk stratification on patients that were at high risk of dying and may be used to identify patients for prophylactic implantable cardioverter-defibrillator therapy.

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Year:  2007        PMID: 17499864     DOI: 10.1016/j.ijcard.2007.03.122

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  4 in total

1.  The beat goes on--driven by a cardiac calcium clock?

Authors:  Satish R Raj; Björn C Knollmann
Journal:  Heart Rhythm       Date:  2008-03-04       Impact factor: 6.343

Review 2.  Individual participant data meta-analysis of prognostic factor studies: state of the art?

Authors:  Ghada Abo-Zaid; Willi Sauerbrei; Richard D Riley
Journal:  BMC Med Res Methodol       Date:  2012-04-24       Impact factor: 4.615

Review 3.  Developing and validating risk prediction models in an individual participant data meta-analysis.

Authors:  Ikhlaaq Ahmed; Thomas P A Debray; Karel G M Moons; Richard D Riley
Journal:  BMC Med Res Methodol       Date:  2014-01-08       Impact factor: 4.615

4.  Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models.

Authors:  Gaurav Gulati; Jenica Upshaw; Benjamin S Wessler; Riley J Brazil; Jason Nelson; David van Klaveren; Christine M Lundquist; Jinny G Park; Hannah McGinnes; Ewout W Steyerberg; Ben Van Calster; David M Kent
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2022-03-31
  4 in total

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