Evidence map›Paper›PMID 32568644›Full record

ArticleStroke2020

Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts.

Eugene Y H Tang, Christopher I Price, Louise Robinson, Catherine Exley, David W Desmond, Sebastian Köhler, Julie Staals, Bonnie Yin Ka Lam, Adrian Wong, Vincent Mok and 7 more

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

Article in Stroke, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
1.2field-weighted citation impact, top 22% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.

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  9. Machine learning-based predictive model for post-stroke dementia.BMC medical informatics and decision making · 2024
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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

17 authors at 6 institutions in 5 countries.

Eugene Y H TangPopulation Health Sciences Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, United Kingdom (E.Y.H.T., C.I.P., L.R., C.E.).
Christopher I PricePopulation Health Sciences Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, United Kingdom (E.Y.H.T., C.I.P., L.R., C.E.).
Louise RobinsonPopulation Health Sciences Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, United Kingdom (E.Y.H.T., C.I.P., L.R., C.E.).
Catherine ExleyPopulation Health Sciences Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, United Kingdom (E.Y.H.T., C.I.P., L.R., C.E.).
David W Desmond
Sebastian KöhlerSchool for Mental Health and Neuroscience, Maastricht University, the Netherlands (S.K.).
Julie StaalsDepartment of Neurology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, the Netherlands (J.S.).
Bonnie Yin Ka LamDivision of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong SAR (B.Y.K.L., A.W., V.M.).
Adrian WongDivision of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong SAR (B.Y.K.L., A.W., V.M.).
Vincent MokDivision of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong SAR (B.Y.K.L., A.W., V.M.).
Regis BordetUniversity Lille, Inserm, CHU Lille, U1171-Degenerative and Vascular Cognitive Disorders, France (R.B., A.-M.B., T.D.).
Anne-Marie BordetUniversity Lille, Inserm, CHU Lille, U1171-Degenerative and Vascular Cognitive Disorders, France (R.B., A.-M.B., T.D.).
Thibaut DondaineUniversity Lille, Inserm, CHU Lille, U1171-Degenerative and Vascular Cognitive Disorders, France (R.B., A.-M.B., T.D.).
Jessica W LoCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia (J.W.L., P.S.S.).
Perminder S SachdevCentre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia (J.W.L., P.S.S.).
Blossom C M StephanInstitute of Mental Health, Division of Psychiatry and Applied Psychology, School of Medicine, Nottingham University, UK (B.C.M.S.).
STROKOG Collaboration
Newcastle Hospitals - Campus for Ageing and Vitality · GBChinese University of Hong Kong · HKInserm · FRMaastricht University · NLUNSW Sydney · AUInstitute of Mental Health · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeStroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity.

methodsData were harmonized from 4 stroke studies (follow-up range, ≈12-18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests.

resultsThe predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53).

conclusionsDementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

Indexed as

Neuropsychological TestsAgedCohort StudiesDatasets as TopicDementiaFemaleHumansIncidenceMaleMiddle AgedPredictive Value of TestsRisk FactorsStrokeagingdementiafollow-up studiesrisk factorsrisk prediction

Identifiers

PMID32568644
PMCPMC7306263
OpenAlexW3016896281

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.