Dataset Information

A systematic review of the diagnostic accuracy of automated tests for cognitive impairment.

ABSTRACT: The aim of this review is to determine whether automated computerised tests accurately identify patients with progressive cognitive impairment and, if so, to investigate their role in monitoring disease progression and/or response to treatment.Six electronic databases (Medline, Embase, Cochrane, Institute for Scientific Information, PsycINFO, and ProQuest) were searched from January 2005 to August 2015 to identify papers for inclusion. Studies assessing the diagnostic accuracy of automated computerised tests for mild cognitive impairment (MCI) and early dementia against a reference standard were included. Where possible, sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios were calculated. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess risk of bias.Sixteen studies assessing 11 diagnostic tools for MCI and early dementia were included. No studies were eligible for inclusion in the review of tools for monitoring progressive disease and response to treatment. The overall quality of the studies was good. However, the wide range of tests assessed and the non-standardised reporting of diagnostic accuracy outcomes meant that statistical analysis was not possible.Some tests have shown promising results for identifying MCI and early dementia. However, concerns over small sample sizes, lack of replicability of studies, and lack of evidence available make it difficult to make recommendations on the clinical use of the computerised tests for diagnosing, monitoring progression, and treatment response for MCI and early dementia. Research is required to establish stable cut-off points for automated computerised tests used to diagnose patients with MCI or early dementia.

SUBMITTER: Aslam RW

PROVIDER: S-EPMC5887872 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A systematic review of the diagnostic accuracy of automated tests for cognitive impairment.

Aslam Rabeea'h W RW Bates Vickie V Dundar Yenal Y Hounsome Juliet J Richardson Marty M Krishan Ashma A Dickson Rumona R Boland Angela A Fisher Joanne J Robinson Louise L Sikdar Sudip S

International journal of geriatric psychiatry 20180122 4

<h4>Objective</h4>The aim of this review is to determine whether automated computerised tests accurately identify patients with progressive cognitive impairment and, if so, to investigate their role in monitoring disease progression and/or response to treatment.<h4>Methods</h4>Six electronic databases (Medline, Embase, Cochrane, Institute for Scientific Information, PsycINFO, and ProQuest) were searched from January 2005 to August 2015 to identify papers for inclusion. Studies assessing the diag ...[more]

PMID: 29356098

Similar Datasets

Project description:BackgroundA range of molecular amplification techniques have been developed for the diagnosis of Human African Trypanosomiasis (HAT); however, careful evaluation of these tests must precede implementation to ensure their high clinical accuracy. Here, we investigated the diagnostic accuracy of molecular amplification tests for HAT, the quality of articles and reasons for variation in accuracy.MethodologyData from studies assessing diagnostic molecular amplification tests were extracted and pooled to calculate accuracy. Articles were included if they reported sensitivity and specificity or data whereby values could be calculated. Study quality was assessed using QUADAS and selected studies were analysed using the bivariate random effects model.Results16 articles evaluating molecular amplification tests fulfilled the inclusion criteria: PCR (n?=?12), NASBA (n?=?2), LAMP (n?=?1) and a study comparing PCR and NASBA (n?=?1). Fourteen articles, including 19 different studies were included in the meta-analysis. Summary sensitivity for PCR on blood was 99.0% (95% CI 92.8 to 99.9) and the specificity was 97.7% (95% CI 93.0 to 99.3). Differences in study design and readout method did not significantly change estimates although use of satellite DNA as a target significantly lowers specificity. Sensitivity and specificity of PCR on CSF for staging varied from 87.6% to 100%, and 55.6% to 82.9% respectively.ConclusionHere, PCR seems to have sufficient accuracy to replace microscopy where facilities allow, although this conclusion is based on multiple reference standards and a patient population that was not always representative. Future studies should, therefore, include patients for which PCR may become the test of choice and consider well designed diagnostic accuracy studies to provide extra evidence on the value of PCR in practice. Another use of PCR for control of disease could be to screen samples collected from rural areas and test in reference laboratories, to spot epidemics quickly and direct resources appropriately.

Project description:ObjectiveTo determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (covid-19).DesignSystematic review and meta-analysis.Data sourcesMedline, bioRxiv, and medRxiv from 1 January to 30 April 2020, using subject headings or subheadings combined with text words for the concepts of covid-19 and serological tests for covid-19.Eligibility criteria and data analysisEligible studies measured sensitivity or specificity, or both of a covid-19 serological test compared with a reference standard of viral culture or reverse transcriptase polymerase chain reaction. Studies were excluded with fewer than five participants or samples. Risk of bias was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using random effects bivariate meta-analyses.Main outcome measuresThe primary outcome was overall sensitivity and specificity, stratified by method of serological testing (enzyme linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), or chemiluminescent immunoassays (CLIAs)) and immunoglobulin class (IgG, IgM, or both). Secondary outcomes were stratum specific sensitivity and specificity within subgroups defined by study or participant characteristics, including time since symptom onset.Results5016 references were identified and 40 studies included. 49 risk of bias assessments were carried out (one for each population and method evaluated). High risk of patient selection bias was found in 98% (48/49) of assessments and high or unclear risk of bias from performance or interpretation of the serological test in 73% (36/49). Only 10% (4/40) of studies included outpatients. Only two studies evaluated tests at the point of care. For each method of testing, pooled sensitivity and specificity were not associated with the immunoglobulin class measured. The pooled sensitivity of ELISAs measuring IgG or IgM was 84.3% (95% confidence interval 75.6% to 90.9%), of LFIAs was 66.0% (49.3% to 79.3%), and of CLIAs was 97.8% (46.2% to 100%). In all analyses, pooled sensitivity was lower for LFIAs, the potential point-of-care method. Pooled specificities ranged from 96.6% to 99.7%. Of the samples used for estimating specificity, 83% (10 465/12 547) were from populations tested before the epidemic or not suspected of having covid-19. Among LFIAs, pooled sensitivity of commercial kits (65.0%, 49.0% to 78.2%) was lower than that of non-commercial tests (88.2%, 83.6% to 91.3%). Heterogeneity was seen in all analyses. Sensitivity was higher at least three weeks after symptom onset (ranging from 69.9% to 98.9%) compared with within the first week (from 13.4% to 50.3%).ConclusionHigher quality clinical studies assessing the diagnostic accuracy of serological tests for covid-19 are urgently needed. Currently, available evidence does not support the continued use of existing point-of-care serological tests.Study registrationPROSPERO CRD42020179452.

Project description:BackgroundAlzheimer's disease (AD) is a widespread neurodegenerative disease that mostly affects the elderly population. Given its prevalence, a precise and efficient stratification system based on AD symptomology that uses functional magnetic resonance imaging (MRI) has great potential in the clinical diagnosis and prognosis estimation of AD patients. It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability.MethodsWe proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI, and NCs) process using MRI. And the accuracy, recall, specificity, area under the curve of receiver operating characteristic curve (AUC), F1 and Matthew's correlation coefficient (MCC) scores to assess accuracy of auxiliary clinical diagnoses.ResultsCompared to other combinations of algorithms, our model obtained remarkable overall stratification accuracies in all different classification sets. In terms of AD vs. MCI, the mean training AUC of the 3 runs were 85.1% in 95% confidence intervals (CIs). In terms of AD vs. NC, the mean training AUC of the 3 runs was 90.6% in 95% CIs. In terms of the 3 stratifications of AD, MCI, and NC, relative precision, recall, and specificity for each category in the training test (TS) were all near 89%, while the F1 and MCC scores of both sets were 59.9% and 59.5%, respectively.ConclusionsUsing a deep CNN and iterated RF architecture, we showed that brain image stratification is a promising means for evaluating AD, and examining the underlying etiology of the disease, by applying computer and medical images to achieve the early auxiliary diagnosis of AD. However, we still have a long way to go from the discovery of image markers to clinical application.

Dataset Information

A systematic review of the diagnostic accuracy of automated tests for cognitive impairment.

Publications

A systematic review of the diagnostic accuracy of automated tests for cognitive impairment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets