Prediction of Long-Term (5 Years) Conversion to Dementia Using Neuropsychological Tests in a Memory Clinic Setting

Silva, Dina; Guerreiro, Manuela; Santana, Isabel; Rodrigues, Ana Carolina; Cardoso, Sandra; Marôco, João

doi:10.3233/jad-122098

Cited by 24 publications

(20 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We are able to predict conversion to dementia within a long-time span (5 years). Indeed, using neuropsychological data to detect cognitive decline in initial phases of AD has faced significant limitations, due to the short follow-up periods which characterize most cohort studies of conversion to dementia [24, 28, 31, 46]. Our work supports the view that longer follow-ups might be an asset in the study of conversion to dementia, as the best results were achieved with the longest windows used.…”

Section: Discussionsupporting

confidence: 59%

“…NPTs are relatively inexpensive and non-invasive, can be readily obtained in most clinical settings [23, 24], are required for diagnosis purpose and have proven their value in tracking the cognitive decline in dementia [8]. Still, their predictive power has not been fully exploit, as it has been addressed mostly by classical statistical methods.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Pereira

Lemos

Cardoso

et al. 2017

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

BackgroundPredicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion.MethodsIn the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question “Will a MCI patient convert to dementia somewhere in the future” to the question “Will a MCI patient convert to dementia in a specific time window”.ResultsThe proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set.ConclusionsPrognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0497-2) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Pereira

Lemos

Cardoso

et al. 2017

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

show abstract

“…Consistent with the observation that neuropathological changes start in brain circuits subserving memory formation and consolidation, many studies identified memory deficits as an early predictor of conversion to AD dementia (Albert et al 2001;Tabert et al 2006;Silva et al 2013). Further, studies also reported subtle deficits in non-memory domains in individuals with prodromal AD, such as insufficiencies in executive functioning (Albert et al 2001;Rapp & Reischies, 2005), verbal fluency (Fabrigoule et al 1998;Laukka et al 2004), attention (Linn et al 1995;Nielsen et al 1999), and global cognitive functioning (Backman & Small, 1998;Fabrigoule et al 1998).…”

Section: Introductionmentioning

confidence: 77%

The trajectory of cognitive decline in the pre-dementia phase in memory clinic visitors: findings from the 4C-MCI study

Hamel

Köhler

Sistermans³

et al. 2014

Psychol. Med.

View full text Add to dashboard Cite

Differences in cognitive performance between converters to AD dementia and subjects remaining non-demented could be established 7 years prior to diagnosis for episodic memory, with verbal fluency and executive functioning following several years later. Therefore, in addition to early episodic memory decline, decline in executive functions may also flag incident AD dementia. By contrast, change in information processing speed/attention seems less informative.

show abstract

“…One of the hallmarks of amnestic MCI and AD is significant verbal memory impairment . There are several neuropsychological tests for memory and learning assessment, such as the logical memory (LM) test, the verbal paired‐associate learning (VPAL) test, and the California verbal learning test (CVLT) . Impairment in LM test has been shown to be associated to higher risk of conversion to AD, although impairment in CVLT revealed better overall accuracy in distinguishing MCI from normal aging .…”

Section: Single‐source Data Analysismentioning

confidence: 99%

Integrative biomarker discovery in neurodegenerative diseases

Carreiro

Mendonça

Carvalho

et al. 2015

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

Data mining has been widely applied in biomarker discovery resulting in significant findings of different clinical and biological biomarkers. With developments in technology, from genomics to proteomics analysis, a deluge of data has become available, as well as standardized data repositories. Nonetheless, researchers are still facing important challenges in analyzing the data, especially when considering the complexity of pathways involved in biological processes and diseases. Data from single sources appear unable to explain complex processes, such as those involved in brain-related disorders, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis, thus raising the need for a more comprehensive perspective. A possible solution relies on data and model integration, where several data types are combined to provide complementary views. This in turn can result in the discovery of previously unknown biomarkers by unraveling otherwise hidden relationships between data from different sources, and/or validate such composite biomarkers in more powerful predictive models.

show abstract

Prediction of Long-Term (5 Years) Conversion to Dementia Using Neuropsychological Tests in a Memory Clinic Setting

Cited by 24 publications

References 55 publications

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

The trajectory of cognitive decline in the pre-dementia phase in memory clinic visitors: findings from the 4C-MCI study

Integrative biomarker discovery in neurodegenerative diseases

Contact Info

Product

Resources

About