The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predictors of Hospital Mortality in the Global Registry of Acute Coronary Events
Abstract
BACKGROUND: Management of acute coronary syndromes (ACS) should be guided by an estimate of patient risk. OBJECTIVE: To develop a simple model to assess the risk for in-hospital mortality for the entire spectrum of ACS treated in general clinical practice. METHODS: A multivariable logistic regression model was developed using 11 389 patients (including 509 in-hospital deaths) with ACS with and without ST-segment elevation enrolled in the Global Registry of Acute Coronary Events (GRACE) from April 1, 1999, through March 31, 2001. Validation data sets included a subsequent cohort of 3972 patients enrolled in GRACE and 12 142 in the Global Use of Strategies to Open Occluded Coronary Arteries IIb (GUSTO-IIb) trial. RESULTS: The following 8 independent risk factors accounted for 89.9% of the prognostic information: age (odds ratio [OR], 1.7 per 10 years), Killip class (OR, 2.0 per class), systolic blood pressure (OR, 1.4 per 20-mm Hg decrease), ST-segment deviation (OR, 2.4), cardiac arrest during presentation (OR, 4.3), serum creatinine level (OR, 1.2 per 1-mg/dL [88.4- micro mol/L] increase), positive initial cardiac enzyme findings (OR, 1.6), and heart rate (OR, 1.3 per 30-beat/min increase). The discrimination ability of the simplified model was excellent with c statistics of 0.83 in the derived database, 0.84 in the confirmation GRACE data set, and 0.79 in the GUSTO-IIb database. CONCLUSIONS: Across the entire spectrum of ACS and in general clinical practice, this model provides excellent ability to assess the risk for death and can be used as a simple nomogram to estimate risk in individual patients.
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Citations
Cites
Mortality by cause for eight regions of the world: Global Burden of Disease Study
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“A CUTE CORONARYsyndromes (ACS) represent the most common cause of death in the western world.(1,2) Considerablevariabilityexists in the risk for adverse events across the spectrum of ACS.”
Background
Cited by
Roffi, M., Patrono, C., Collet, J.-P., Mueller, C., Valgimigli, M., Andreotti, F., Bax, J. J., Borger, M. A., Brotons, C., Chew, D. P., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv320 · Cited by 8,261
2023 ESC Guidelines for the management of acute coronary syndromes
Byrne, R. A., Rossello, X., Coughlan, J. J., Barbato, E., Berry, C., Chieffo, A., Claeys, M. J., Dan, G.-A., Dweck, M. R., Galbraith, M., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad191 · Cited by 5,333
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2014 AHA/ACC Guideline for the Management of Patients With Non–ST-Elevation Acute Coronary Syndromes
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JCS 2018 Guideline on Diagnosis and Treatment of Acute Coronary Syndrome
Kimura, K., Kimura, T., Ishihara, M., Nakagawa, Y., Nakao, K., Miyauchi, K., Sakamoto, T., Tsujita, K., Hagiwara, N., Miyazaki, S., et al. (2019) · Circulation Journal · doi:10.1253/circj.cj-19-0133 · Cited by 565
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Atherothrombotic risk stratification and ezetimibe for secondary prevention
Bohula, E. A., Morrow, D. A., Giugliano, R. P., Blazing, M. A., He, P., Park, J.-G., Murphy, S. A., White, J. A., Kesaniemi, Y. A., Pedersen, T. R., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.11.070 · Cited by 204
Bohula, E. A., Bonaca, M. P., Braunwald, E., Aylward, P. E., Corbalan, R., De Ferrari, G. M., He, P., Lewis, B. S., Merlini, P. A., Murphy, S. A., et al. (2016) · Circulation · doi:10.1161/circulationaha.115.019861 · Cited by 168
Zhou, M., Liu, J., Hao, Y., Liu, J., Huo, Y., Smith, S. C., Ge, J., Ma, C., Han, Y., Fonarow, G. C., et al. (2018) · Cardiovascular Diabetology · doi:10.1186/s12933-018-0793-x · Cited by 103
Machine learning improves risk stratification after acute coronary syndrome
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“clinical variables to estimate the patient risk 7,9 .”
Wessler, B. S., Nelson, J., Park, J. G., McGinnes, H., Gulati, G., Brazil, R., Van Calster, B., van Klaveren, D., Venema, E., Steyerberg, E., et al. (2021) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.007858 · Cited by 83
Multiple risk factors predict recurrence of major depressive disorder in women
van Loo, H. M., Aggen, S. H., Gardner, C. O., & Kendler, K. S. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.03.045 · Cited by 45
Albuminuria as a predictor of cardiovascular outcomes in patients with acute myocardial infarction
Mok, Y., Ballew, S. H., Sang, Y., Grams, M. E., Coresh, J., Evans, M., Barany, P., Ärnlöv, J., Carrero, J., & Matsushita, K. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.118.010546 · Cited by 41
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Metadata
- Authors
- Granger, C. B.
- Venue
- Archives of Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2003-10-27
- Volume
- 163
- Issue
- 19
- Pages
- 2345
- ISSN
- 0003-9926 (ISSN-L)
- OpenAlex
- W2114198397
- Cited by
- 2,356
- References
- 1
- FWCI
- 20.32
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
