Evidence map›Paper›PMID 31078129›Full record

ArticleAging2019

Predicting progression from mild cognitive impairment to Alzheimer's disease on an individual subject basis by applying the CARE index across different independent cohorts.

Jiu Chen, Gang Chen, Hao Shu, Guangyu Chen, B Douglas Ward, Zan Wang, Duan Liu, Piero G Antuono, Shi-Jiang Li, Zhijun Zhang and 1 more

Abstract read
In one paragraph

Article in Aging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jiu ChenDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Gang ChenDepartment of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Hao ShuDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Guangyu ChenDepartment of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
B Douglas WardDepartment of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Zan WangDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Duan LiuDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Piero G AntuonoDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Shi-Jiang LiDepartment of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Zhijun ZhangDepartment of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.
Alzheimer’s Disease Neuroimaging InitiativeA complete listing of ADNI investigators can be found at at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Funding

Alzheimer's Disease Connectome ProjectUF1AG051216 · NIA · MEDICAL COLLEGE OF WISCONSIN · PI BENDLIN, BARBARA BRIGITTA, LI, SHI-JIANG · 2016 to 2019
$5.8M
Imaging Software of Functional Connectivity MRI for Alzheimer's DiseaseR44AG035405 · NIA · BRAINSYMPHONICS, LLC · PI HU, HUI · 2013 to 2015
$1.8M
NIA NIH HHS R44 AG035405NIA NIH HHS UF1 AG051216
6 · The paper itself

Abstract

The purposes of this study are to investigate whether the Characterizing Alzheimer's disease Risk Events (CARE) index can accurately predict progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) on an individual subject basis, and to investigate whether this model can be generalized to an independent cohort. Using an event-based probabilistic model approach to integrate widely available biomarkers from behavioral data and brain structural and functional imaging, we calculated the CARE index. We then applied the CARE index to identify which MCI individuals from the ADNI dataset progressed to AD during a three-year follow-up period. Subsequently, the CARE index was generalized to the prediction of MCI individuals from an independent Nanjing Aging and Dementia Study (NADS) dataset during the same time period. The CARE index achieved high prediction performance with 80.4% accuracy, 75% sensitivity, 82% specificity, and 0.809 area under the receiver operating characteristic (ROC) curve (AUC) on MCI subjects from the ADNI dataset over three years, and a highly validated prediction performance with 87.5% accuracy, 81% sensitivity, 90% specificity, and 0.861 AUC on MCI subjects from the NADS dataset. In conclusion, the CARE index is highly accurate, sufficiently robust, and generalized for predicting which MCI individuals will develop AD over a three-year period. This suggests that the CARE index can be usefully applied to select individuals with MCI for clinical trials and to identify which individuals will convert from MCI to AD for administration of early disease-modifying treatment.

Indexed as

AgedAged, 80 and overAlzheimer DiseaseBrainCognitionCognitive DysfunctionDisease ProgressionFemaleHumansIndividualityMaleMiddle AgedNeuropsychological TestsPrognosisRisk AssessmentSensitivity and SpecificityAlzheimer’s diseaseAlzheimer’s Disease Neuroimaging Initiative (ADNI)Characterizing Alzheimer’s disease Risk Events (CARE) indexmild cognitive impairment

Identifiers

PMID31078129
PMCPMC6520016

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.