4.6 Review

Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis

期刊

FRONTIERS IN AGING NEUROSCIENCE
卷 14, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2022.840386

关键词

mild cognitive impairment; Alzheimers disease; dementia; prediction models; systematic review

资金

  1. National Nature Science Foundation of China [71704071]
  2. Natural Science Foundation of Gansu Province [20JR10RA603]
  3. Fundamental Research Funds for the Central Universities [lzujbky-2020-10, lzujbky-2021-33]
  4. National Research Training Program of Gansu Provincial Hospital [19SYPYA-4]
  5. Research Funds for the School of Nursing of Lanzhou University [LZUSON202002]
  6. Gansu Province Health and Health Industry Scientific Research Program Project [GSWSHL2021-016]
  7. Second Hospital of Lanzhou University Cuiying Science and Technology Innovation Clinical Nursing Research Project [CY2021-HL-05]

向作者/读者索取更多资源

This study conducted a systematic review and meta-analysis to summarize the risk prediction models for conversion from MCI to AD. The findings revealed that MRI, APOE4 genotype, older age, MMSE score, and ADAS-cog score were the most common and strongest predictors included in the models. However, the lack of external validation limited the extensive application of the models in the general population.
Background and Purpose: Alzheimer's disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD. Methods: We systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment. Results: In total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67-0.98, and the validation models were 0.62-0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer's Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models. Conclusion: In this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.

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