Validity of screening tools for dementia and mild cognitive impairment among the elderly in primary health care: a systematic review

Razak, Mohamad Aznuddin Abd; Ahmad, NA; Chan, Ying Ying; Kasim, Noraida Mohamad; Yusof, Mohd Apandi; Ghani, M.K.A. Abdul; Omar, Mohd Idris; Aziz, Fakhra; Jamaluddin, Rasidah

doi:10.1016/j.puhe.2019.01.001

Cited by 65 publications

(74 citation statements)

References 49 publications

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“…Many studies have previously examined neurocognitive tests and identified a few top neurocognitive classifiers using non-machine learning methods, but they are not discussed here [9,[11][12][13] (see reviews by Abd Razak In sum, our results are comparable or compared favorably with previous studies although comparisons were not made on the same datasets. Our study is novel in that we employed a nonsupervised learning (as opposed to supervised learning) method, applied to a large and multi-center ADNI dataset with commonly used measures.…”

Section: Risk Score Modelsupporting

confidence: 66%

“…A large array of neurocognitive tests are currently used to detect cognitive impairment and classify amongst normal controls (CN), EMCI, LMCI and AD [7][8]. Many studies have identified a few top classifiers using logistic regression and machine learning methods [9][10][11][12][13][14][15][16][17][18]. Some studies have also used MRI and genetic data in conjunction with neurocognitive measures for classification [19][20].…”

Section: Introductionmentioning

confidence: 99%

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Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Nagaraj

Duong

2020

Preprint

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A large battery of neurocognitive tests are used to detect cognitive impairment and classify early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s Disease (AD) from cognitive normal (CN). The goal of this study was to develop a deep learning algorithm to identify a few top neurocognitive tests that can accurately classify these four groups. We also derived a simplified risk-stratification score model for classification. Over 100 variables that included neuropsychological/neurocognitive tests, demographics, genetic factors, and blood biomarkers were collected from 383 EMCI, 644 LMCI, 394 AD patients, and 516 cognitive normal from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A neural network algorithm was trained on data split 90% for training and 10% testing. We evaluated five different feature selection methods. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis. The five different feature selection methods consistently yielded the top classifiers to be the Clinical Dementia Rating Scale - Sum of Boxes (CDRSB), Delayed total recall (LDELTOTAL), Modified Preclinical Alzheimer Cognitive Composite with Trails test (mPACCtrailsB), the Modified Preclinical Alzheimer Cognitive Composite with Digit test (mPACCdigit), and Mini-Mental State Examination (MMSE). The best classification model yielded an AUC of 0.984, and the simplified risk-stratification score yielded an AUC of 0.962 on the test dataset. The deep-learning algorithm and simplified risk score derived from our deep-learning algorithm accurately classifies EMCI, LMCI, AD and CN patients using a few commonly available neurocognitive tests.

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Section: Risk Score Modelsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Nagaraj

Duong

2020

Preprint

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“…Brief evaluations for MCI are supported overall by studies reporting that cognitive screening tests have valuable sensitivity and specificity (Tsoi et al, 2017 ; Breton et al, 2019 ; Razak et al, 2019 ). However, some previous studies have already shown that dementia cases discovered by screening tests included patients with dementia at intermediate and advanced stages and, more rarely, patients with prodromal dementia (Riley McCarten et al, 2012 ).…”

Section: Why Initial Brief Evaluations Could Be Inadequate To Detect mentioning

confidence: 99%

The Two-Step Strategy Could Be Inadequate and Counteracting to Diagnose Prodromal Dementia or Mild Cognitive Impairment

Abbate

Trimarchi

Inglese

et al. 2020

Front. Aging Neurosci.

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“…Compared with the blank group(Cont), the differential ions in the model group(Mod) showed obvious up-down relationship. To further identify potential metabolic markers of MCI, we combined the matching degree in mass spectrometry and used related mass spectrometry websites such as: Human Metabolome Database [1] ; Lipidomics Gateway [2] ; METLIN Metabolite Database [3] . Differential metabolites were identified by SDBS [4] and related literature reports.…”

Section: Identification Of Differential Metabolitesmentioning

confidence: 99%

“…The specific pathogenesis of MCI is not clear. At present, some hypotheses and studies have put forward [3], including senile plaque caused by β-amyloid (Aβ), nerve fiber tangles caused by hyperphosphorylation of Tau protein, synaptic dysfunction, chronic inflammatory cascade reaction, oxidative stress damage, etc. are the main pathogenesis of MCI.…”

Section: Introductionmentioning

confidence: 99%

Metabolomics Study on Improvement of Mild Cognitive Impairment by SCPE: a UPLC-Q/TOF-MS Mass Spectrometric Study

Hou

Sun

Wei

et al. 2020

Preprint

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Background: Mild Cognitive Impairment (MCI) is a common and easilyneglected neurological disease in clinic. Aging is the main cause of the disease,but its exact pathogenesis is not yet clear. Sagacious Confucius' PillowElixir(SCPE) is a classic medicine for the treatment of amnesia in traditionalChinese medicine.The aim of this study was to prove the stability and reliabilityof SAMP8 mice as MCI model, and to nd potential biomarkers for thetransformation of mild cognitive impairment to dementia, so as to reveal thepathogenesis of mild cognitive impairment and the mechanism of SCPE at thewhole animal level.Methods: Senescence-accelerated P8 (SAMP8) mice were selected as the modelof MCI. Based on UHPLC/Q-TOF-MS platform, the eect of SCPE on MCI micewas evaluated by metabolomics to screen and identify brain tissue biomarkers andpathways of MCI. T test of SPSS 22.0 software and meta-analysis software wereused for univariate and multivariate analysis to screen and identify the biomarkersand pathways of brain tissue with mild cognitive impairment; SPSS 22.0statistical software was used to conduct t-test on the total ionic strengthcorresponding to the dierent metabolites in each group, and to clarify thechange mechanism of biomarkers and pathways in dierent doses of SCPE.Results: From the point of view of brain metabolomics, 84 MCI-relatedbiomarkers and 8 metabolic pathways were identied. It was found that dierentdoses of SCPE could call back 36 same metabolic markers and 3 commonmetabolic pathways.And with the increase of dose, the number of SCPE callbackmetabolic markers gradually increased(LD:40; MD:48; HD:54), and the types ofmain metabolic pathways also changed to a certain extent (the biosynthesis ofvaline, leucine and isoleucine in low dose changed to cysteine and methioninemetabolic pathway in middle and high dose),which may be related to the eectof aminoacyl tRNA.Conclusions: The stability and reliability of MCI model, and the eectiveness ofSCPE in the treatment of MCI were clared. Compared three dierent dosegroups, the high dose SCPE group has more advantages.

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Validity of screening tools for dementia and mild cognitive impairment among the elderly in primary health care: a systematic review

Cited by 65 publications

References 49 publications

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

Risk Score Stratification of Alzheimer’s Disease and Mild Cognitive Impairment using Deep Learning

The Two-Step Strategy Could Be Inadequate and Counteracting to Diagnose Prodromal Dementia or Mild Cognitive Impairment

Metabolomics Study on Improvement of Mild Cognitive Impairment by SCPE: a UPLC-Q/TOF-MS Mass Spectrometric Study

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