Literature DB >> 18328842

A percutaneous coronary intervention-thrombolytic predictive instrument to assist choosing between immediate thrombolytic therapy versus delayed primary percutaneous coronary intervention for acute myocardial infarction.

David M Kent1, Robin Ruthazer, John L Griffith, Joni R Beshansky, Thomas W Concannon, Thomas Aversano, Cindy L Grines, Robert J Zalenski, Harry P Selker.   

Abstract

Based on the thrombolytic predictive instrument (TPI), we sought to create electrocardiographically based, real-time decision support to immediate identification of patients with ST-segment elevation myocardial infarction (STEMI) likely to benefit from primary percutaneous coronary intervention (PCI) compared with thrombolysis. Using data from the Atlantic Cardiovascular Patient Outcomes Research Team (C-PORT) Trial, we tested a mathematical model predicting mortality in patients with STEMI if treated with PCI and if treated with thrombolytic therapy. We adapted the model for incorporation into computerized electrocardiograms as a PCI-TPI. For patients with STEMI in the C-PORT Trial, the model yielded unbiased mortality predictions: for those receiving thrombolysis, it predicted 6.3% mortality and actual mortality was 6.0% (95% confidence interval 3.0 to 10.6); for those receiving PCI, it predicted 4.5% mortality and actual mortality was 3.9% (95% confidence interval 1.4 to 8.2). Excellent discrimination was reflected by its receiver operating characteristic curve area of 0.86. According to the model, and validated by actual trial outcomes, 1/3 of subjects accounted for all the mortality benefit from PCI. In conclusion, for STEMI, the PCI-TPI accurately predicts mortality for treatment with PCI and with thrombolytic therapy. Incorporated into electrocardiogram, it may assist targeting PCI to those who benefit most and identifying patients before hospitalization for whom a receiving hospital should prepare for PCI.

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Year:  2008        PMID: 18328842     DOI: 10.1016/j.amjcard.2007.10.050

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  7 in total

1.  Random treatment assignment using mathematical equipoise for comparative effectiveness trials.

Authors:  Harry P Selker; Robin Ruthazer; Norma Terrin; John L Griffith; Thomas Concannon; David M Kent
Journal:  Clin Transl Sci       Date:  2011-02       Impact factor: 4.689

2.  Comparative effectiveness of ST-segment-elevation myocardial infarction regionalization strategies.

Authors:  Thomas W Concannon; David M Kent; Sharon-Lise Normand; Joseph P Newhouse; John L Griffith; Joshua Cohen; Joni R Beshansky; John B Wong; Thomas Aversano; Harry P Selker
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2010-07-27

3.  Using internally developed risk models to assess heterogeneity in treatment effects in clinical trials.

Authors:  James F Burke; Rodney A Hayward; Jason P Nelson; David M Kent
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2014-01-14

4.  Risk and treatment effect heterogeneity: re-analysis of individual participant data from 32 large clinical trials.

Authors:  David M Kent; Jason Nelson; Issa J Dahabreh; Peter M Rothwell; Douglas G Altman; Rodney A Hayward
Journal:  Int J Epidemiol       Date:  2016-12-01       Impact factor: 7.196

5.  Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal.

Authors:  David M Kent; Peter M Rothwell; John P A Ioannidis; Doug G Altman; Rodney A Hayward
Journal:  Trials       Date:  2010-08-12       Impact factor: 2.279

6.  The use of patient-specific equipoise to support shared decision-making for clinical care and enrollment into clinical trials.

Authors:  Harry P Selker; Denise H Daudelin; Robin Ruthazer; Manlik Kwong; Rebecca C Lorenzana; Daniel J Hannon; John B Wong; David M Kent; Norma Terrin; Alejandro D Moreno-Koehler; Timothy E McAlindon
Journal:  J Clin Transl Sci       Date:  2019-02

7.  Can risk modelling improve treatment decisions in asymptomatic carotid stenosis?

Authors:  James F Burke; Lewis B Morgenstern; Rodney A Hayward
Journal:  BMC Neurol       Date:  2019-11-22       Impact factor: 2.474

  7 in total

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