TIMI Risk Score for ST-Elevation Myocardial Infarction: A Convenient, Bedside, Clinical Score for Risk Assessment at Presentation: An Intravenous nPA for Treatment of Infarcting Myocardium Early II…

By D. A. Morrow, E. Antman, A. Charlesworth, Richard Cairns, S. Murphy, J. D. de Lemos, R. Giugliano, C. Mccabe, E. Braunwald. 2000

1,610 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Epidemiology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Cynthia Rudin

In Cardiology and Cardiovascular Medicine

1. Jeroen J. Bax
2. Gerasimos Filippatos
3. Peter C. Austin
4. Stefano Tracà
5. Berk Ustun
6. Cynthia Rudin
7. Gerhard Hindricks
8. Roger Mundry

In Epidemiology

1. Jeroen J. Bax
2. Peter C. Austin
3. Stefano Tracà
4. Berk Ustun
5. Cynthia Rudin
6. Roger Mundry

Citations in the corpus

• Methods and Models for Interpretable Linear Classification (2014, background): “1 Building Scoring Systems Scoring systems are practical and interpretable prediction models that are primarily used to assess the risk of medical outcomes in hospitals [3, 70, 31], but have also been used to predict the incidence of violent crimes [2, 86], gauge marine safety for military vessels [”
• Management of acute myocardial infarction in patients presenting with persistent ST-segment elevation: the Task Force on the Management of ST-Segment Elevation Acute Myocardial Infarction of the European Society of Cardiology. (2008, background): “Older age, higher Killip class, elevated heart rate, lower systolic blood pressure, and anterior location of the infarct have been identified as the most important independent predictors of early mortality in clinical trials(20) and registries.(17,21) These characteristics contain most of the progno”
• A Bayesian Approach to Learning Scoring Systems (2015, neutral): “…stroke in patients with atrial fibrillation; 1 TIMI, which models mortality and ischemic events in patients with certain types of heart problems; 7,21 SIRS, which diagnoses systemic inflammatory response syndrome; 22 Wells’ criteria for pulmonary embolisms 4 and deep vein thrombosis; 3 Ranson…”
• Bootstrap Methods for Developing Predictive Models (2004, background): “Predictive models described in the cardiovascular literature have c-indices ranging from 0.779 to 0.836 (Morrow et al. 2001; Boersma et al. 2000; Jacobs et al. 1999; Morrow et al. 2000; Krumholz et al. 1999; Lee et al. 1995).”
• A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. (2004, background): “Length of hospital stay, median (IQR), d Patients who died 8 (5-12) 6 (3-10) NA Patients discharged alive 6 (3-10) 6 (3-9) NA”
• Supersparse Linear Integer Models for Interpretable Classification (2013, methodology): “These systems are widely used in our society: to assess the risk of medical outcomes in hospitals [3, 53, 25]; to predict the incidence of violent crimes [2, 64]; to gauge marine safety for military vessels [16]; and to rate business schools [21].”
• Predictors of hospital mortality in the global registry of acute coronary events. (2003, result): “This study, using data from a large multinational registry of ACS patients, confirms the prognostic importance of several baseline characteristics reported from previous models developed from other databases.(8,9) The most important 8 factors—Killip class, age, blood pressure, resuscitated cardiac a”