The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease
Abstract
OBJECTIVE: To evaluate the utility of MRI hippocampal and entorhinal cortex atrophy in predicting conversion from mild cognitive impairment (MCI) to Alzheimer disease (AD). METHODS: Baseline brain MRI was performed in 139 patients with MCI, broadly defined, and 63 healthy controls followed for an average of 5 years (range 1 to 9 years). RESULTS: Hippocampal and entorhinal cortex volumes were each largest in controls, intermediate in MCI nonconverters, and smallest in MCI converters to AD (37 of 139 patients converted to AD). In separate Cox proportional hazards models, covarying for intracranial volume, smaller hippocampal volume (risk ratio [RR] 3.62, 95% CI 1.93 to 6.80, p < 0.0001), and entorhinal cortex volume (RR 2.43, 95% CI 1.56 to 3.79, p < 0.0001) each predicted time to conversion to AD. Similar results were obtained for hippocampal and entorhinal cortex volume in patients with MCI with Mini-Mental State Examination (MMSE) scores > or = 27 out of 30 (21% converted to AD) and in the subset of patients with amnestic MCI (35% converted to AD). In the total patient sample, when both hippocampal and entorhinal volume were entered into an age-stratified Cox model with sex, MMSE, education, and intracranial volume, smaller hippocampal volume (RR 2.21, 95% CI 1.14 to 4.29, p < 0.02) and entorhinal cortex volume (RR 2.48, 95% CI 1.54 to 3.97, p < 0.0002) predicted time to conversion to AD. Similar results were obtained in a Cox model that also included Selective Reminding Test (SRT) delayed recall and Wechsler Adult Intelligence Scale-Revised (WAIS-R) Digit Symbol as predictors. Based on logistic regression models in the 3-year follow-up sample, for a fixed specificity of 80%, the sensitivities for MCI conversion to AD were as follows: age 43.3%, MMSE 43.3%, age + MMSE 63.7%, age + MMSE + SRT delayed recall + WAIS-R Digit Symbol 80.6% (79.6% correctly classified), hippocampus + entorhinal cortex 66.7%, age + MMSE + hippocampus + entorhinal cortex 76.7% (85% correctly classified), age + MMSE + SRT delayed recall + WAIS-R Digit Symbol + hippocampus + entorhinal cortex 83.3% (86.8% correctly classified). CONCLUSIONS: Smaller hippocampal and entorhinal cortex volumes each contribute to the prediction of conversion to Alzheimer disease. Age and cognitive variables also contribute to prediction, and the added value of hippocampal and entorhinal cortex volumes is small. Nonetheless, combining these MRI volumes with age and cognitive measures leads to high levels of predictive accuracy that may have potential clinical application.
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Cites
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Mild Cognitive Impairment: Clinical Characterization and Outcome
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Profound Loss of Layer II Entorhinal Cortex Neurons Occurs in Very Mild Alzheimer’s Disease
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Self-citationMitchell, A. J., & Shiri‐Feshki, M. (2009) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2008.01326.x · Cited by 1,659
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Comparing predictors of conversion and decline in mild cognitive impairment
Landau, S. M., Harvey, D., Madison, C. M., Reiman, E. M., Foster, N. L., Aisen, P. S., Petersen, R. C., Shaw, L. M., Trojanowski, J. Q., Jack, C. R., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181e8e8b8 · Cited by 616
Metadata
- Authors
- Devanand, D. P., Pradhaban, G., Liu, X., Khandji, A., De Santi, S., Segal, S., Rusinek, H., Pelton, G. H., Honig, L. S., Mayeux, R., Stern, Y., Tabert, M. H., & de Leon, M. J.
- Venue
- Neurology
- Published
- 2007-03-13
- Volume
- 68
- Issue
- 11
- Pages
- 828-836
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- OpenAlex
- W2133703021
- Cited by
- 748
- References
- 10
- FWCI
- 27.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- Devanand, D. P., Pradhaban, G., Liu, X., Khandji, A., De Santi, S., Segal, S., Rusinek, H., Pelton, G. H., Honig, L. S., Mayeux, R., Stern, Y., Tabert, M. H., & de Leon, M. J. (2007). Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease. Neurology. https://doi.org/10.1212/01.wnl.0000256697.20968.d7
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