Evidence map›Paper›PMID 28275699›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2017

A comparison of theoretical and statistically derived indices for predicting cognitive decline.

Holly Wilhalme, Naira Goukasian, Fransia De Leon, Angie He, Kristy S Hwang, Ellen Woo, David Elashoff, Yan Zhou, John M Ringman, Liana G Apostolova

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 26% of its field
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

8 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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

10 authors at 4 institutions in 1 country.

Holly WilhalmeDepartment of Medicine Statistics Core, University of California, Los Angeles, Los Angeles, CA, USA.
Naira GoukasianDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA, USA.
Fransia De LeonDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA, USA.
Angie HeDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA, USA.
Kristy S HwangOakland University, William Beaumont School of Medicine, Rochester, MI, USA.
Ellen WooDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA, USA.
David ElashoffDepartment of Medicine Statistics Core, University of California, Los Angeles, Los Angeles, CA, USA.
Yan ZhouDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA, USA.
John M RingmanDepartment of Neurology, University of Southern California, Los Angeles, CA, USA.
Liana G ApostolovaDepartment of Neurology, University of California, Los Angeles, Los Angeles, CA, USA; Department of Neurology, Indiana University, Indianapolis, IN, USA; Department of Radiology, Indiana University, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA.
University of California, Los Angeles · USIndiana University IndianapolisOakland University · USUniversity of Southern California · US

Funding

UCLA ALZHEIMERS DISEASE RESEARCH CENTERP50AG016570 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI APOSTOLOVA, LIANA G · 1999 to 2014
$25.9M
Imaging and genetic biomarkers for Alzheimer's diseaseR01AG040770 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI APOSTOLOVA, LIANA G · 2012 to 2016
$1.4M
Imaging epigenetics of Alzheimer's DiseaseK02AG048240 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI APOSTOLOVA, LIANA G · 2014 to 2016
$434k
NIA NIH HHS K02 AG048240NIA NIH HHS P50 AG016570NIA NIH HHS R01 AG040770
6 · The paper itself

Abstract

introductionBoth theoretical and statistically derived approaches have been used in research settings for predicting cognitive decline.

methodsFifty-eight cognitively normal (NC) and 71 mild cognitive impairment (MCI) subjects completed a comprehensive cognitive battery for up to 5 years of follow-up. Composite indices of cognitive function were derived using a classic theoretical approach and exploratory factor analysis (EFA). Cognitive variables comprising each factor were averaged to form the EFA composite indices. Logistic regression was used to investigate whether these cognitive composites can reliably predict cognitive outcomes.

resultsNeither method predicted decline in NC. The theoretical memory, executive, attention, and language composites and the EFA-derived "attention/executive" and "verbal memory" composites were significant predictors of decline in MCI. The best models achieved an area under the curve of 0.94 in MCI.

conclusionsThe theoretical and the statistically derived cognitive composite approaches are useful in predicting decline in MCI but not in NC.

Indexed as

ADAlzheimer's diseaseCognitive declineConversionExecutive declineFactor analysisMCIMemory declineMild cognitive impairmentNeuropsychological testPrognosisProgression

Identifiers

PMID28275699
PMCPMC5328960
OpenAlexW2547434860

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.