The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Prevalence and Prognosis of Unrecognized Myocardial Infarction Determined by Cardiac Magnetic Resonance in Older Adults
Open accessAbstract
CONTEXT: Unrecognized myocardial infarction (MI) is prognostically important. Electrocardiography (ECG) has limited sensitivity for detecting unrecognized MI (UMI). OBJECTIVE: Determine prevalence and mortality risk for UMI detected by cardiac magnetic resonance (CMR) imaging or ECG among older individuals. DESIGN, SETTING, AND PARTICIPANTS: ICELAND MI is a cohort substudy of the Age, Gene/Environment Susceptibility-Reykjavik Study (enrollment January 2004-January 2007) using ECG or CMR to detect UMI. From a community-dwelling cohort of older individuals in Iceland, data for 936 participants aged 67 to 93 years were analyzed, including 670 who were randomly selected and 266 with diabetes. MAIN OUTCOME MEASURES: Prevalence and mortality of MI through September 1, 2011. Results reported with 95% confidence limits and net reclassification improvement (NRI). RESULTS: Of 936 participants, 91 had recognized MI (RMI) (9.7%; 95% CI, 8% to 12%), and 157 had UMI detected by CMR (17%; 95% CI, 14% to 19%), which was more prevalent than the 46 UMI detected by ECG (5%; 95% CI, 4% to 6%; P < .001). Participants with diabetes (n = 337) had more UMI detected by CMR than by ECG (n = 72; 21%; 95% CI, 17% to 26%, vs n = 15; 4%; 95% CI, 2% to 7%; P < .001). Unrecognized MI by CMR was associated with atherosclerosis risk factors, coronary calcium, coronary revascularization, and peripheral vascular disease. Over a median of 6.4 years, 30 of 91 participants (33%; 95% CI, 23% to 43%) with RMI died, and 44 of 157 participants (28%; 95% CI, 21% to 35%) with UMI died, both higher rates than the 119 of 688 participants (17%; 95% CI, 15% to 20%) with no MI who died. Unrecognized MI by CMR improved risk stratification for mortality over RMI (NRI, 0.34; 95% CI, 0.16 to 0.53). Adjusting for age, sex, diabetes, and RMI, UMI by CMR remained associated with mortality (hazard ratio [HR], 1.45; 95% CI, 1.02 to 2.06, absolute risk increase [ARI], 8%) and significantly improved risk stratification for mortality (NRI, 0.16; 95% CI, 0.01 to 0.31), but UMI by ECG did not (HR, 0.88; 95% CI, 0.45 to 1.73; ARI, -2%; NRI, -0.05; 95% CI, -0.17 to 0.05). Compared with those with RMI, participants with UMI by CMR used cardiac medications such as statins less often (36%; 95% CI, 28% to 43%, or 56/157, vs 73%; 95% CI, 63% to 82%, or 66/91; P < .001). CONCLUSIONS: In a community-based cohort of older individuals, the prevalence of UMI by CMR was higher than the prevalence of RMI and was associated with increased mortality risk. In contrast, UMI by ECG prevalence was lower than that of RMI and was not associated with increased mortality risk. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01322568.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,947
Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)
Grundy, S. M., Becker, D. M., Clark, L. T., Cooper, R., Denke, M. A., Howard, J. L., Hunninghake, D. B., Illingworth, D. R., Luepker, R. V., McBride, P. E., et al. (2002) · Civil War Book Review · Cited by 12,198
Follow-up report on the diagnosis of diabetes mellitus
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus* (2003) · Diabetes Care · doi:10.2337/diacare.26.11.3160 · Cited by 3,784
Kim, R. J., Wu, E., Rafael, A., Chen, E.-L., Parker, M. A., Simonetti, O., Klocke, F. J., Bonow, R. O., & Judd, R. M. (2000) · New England Journal of Medicine · doi:10.1056/nejm200011163432003 · Cited by 3,306
Kim, R. J., Fieno, D. S., Parrish, T. B., Harris, K., Chen, E.-L., Simonetti, O., Bundy, J., Finn, J. P., Klocke, F. J., & Judd, R. M. (1999) · Circulation · doi:10.1161/01.cir.100.19.1992 · Cited by 2,581
Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers
Pencina, M. J., D'Agostino, R. B., & Steyerberg, E. W. (2011) · Statistics in Medicine · doi:10.1002/sim.4085 · Cited by 2,453
Wagner, A., Mahrholdt, H., Holly, T. A., Elliott, M. D., Regenfus, M., Parker, M., Klocke, F. J., Bonow, R. O., Kim, R. J., & Judd, R. M. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12389-6 · Cited by 1,307
Incidence and Prognosis of Unrecognized Myocardial Infarction: An Update on the Framingham Study
Kannel, W. B., & Abbott, R. D. (1984) · New England Journal of Medicine · doi:10.1056/nejm198411013111802 · Cited by 1,018
Burke, A. P., Kolodgie, F. D., Farb, A., Weber, D. K., Malcom, G. T., Smialek, J., & Virmani, R. (2001) · Circulation · doi:10.1161/01.cir.103.7.934 · Cited by 919
Cited by
Joseph, J. J., Deedwania, P., Acharya, T., Aguilar, D., Bhatt, D. L., Chyun, D. A., Di Palo, K. E., Golden, S. H., Sperling, L. S., & on behalf of the American Heart Association Diabetes Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Arteriosclerosis, T. A. V. B. C. O. C. C. A. C. O. H. (2022) · Circulation · doi:10.1161/cir.0000000000001040 · Cited by 660
Leening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J., & Steyerberg, E. W. (2014) · Annals of Internal Medicine · doi:10.7326/m13-1522 · Cited by 594
Myocardial fibrosis in athletes.
van de Schoor, F. R., Aengevaeren, V. L., Hopman, M. T. E., Oxborough, D. L., George, K. P., Thompson, P. D., & Eijsvogels, T. M. H. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.07.012 · Cited by 168
Sabayan, B., van Buchem, M. A., Sigurdsson, S., Zhang, Q., Harris, T. B., Gudnason, V., Arai, A. E., & Launer, L. J. (2015) · Journal of the American Heart Association · doi:10.1161/jaha.114.001294 · Cited by 68
Self-citationBig Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging
Suinesiaputra, A., Medrano-Gracia, P., Cowan, B. R., & Young, A. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2370952 · Cited by 45
Metadata
- Authors
- Schelbert, E. B., Cao, J. J., Sigurdsson, S., Aspelund, T., Kellman, P., Aletras, A. H., Dyke, C. K., Thorgeirsson, G., Eiriksdottir, G., Launer, L. J., Gudnason, V., Harris, T. B., & Arai, A. E.
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2012-09-05
- Volume
- 308
- Issue
- 9
- Pages
- 890
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2159683035
- Cited by
- 284
- References
- 28
- FWCI
- 17.80
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Prevalence and Prognosis of Unrecognized Myocardial Infarction Determined by Cardiac Magnetic Resonance in Older Adults. https://app.cochat.ai/papers/10.1001/2012.jama.11089
- The paper
- Schelbert, E. B., Cao, J. J., Sigurdsson, S., Aspelund, T., Kellman, P., Aletras, A. H., Dyke, C. K., Thorgeirsson, G., Eiriksdottir, G., Launer, L. J., Gudnason, V., Harris, T. B., & Arai, A. E. (2012). Prevalence and Prognosis of Unrecognized Myocardial Infarction Determined by Cardiac Magnetic Resonance in Older Adults. JAMA. https://doi.org/10.1001/2012.jama.11089
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.