ObjectiveThe 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.MethodsSix 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.ResultsSixteen 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.ConclusionSome 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.

The National Institute for Health Research Health Technology Assessment programme.

BackgroundCognitive impairment is a growing public health concern, and is one of the most distinctive characteristics of all dementias. The timely recognition of dementia syndromes can be beneficial, as some causes of dementia are treatable and are fully or partially reversible. Several automated cognitive assessment tools for assessing mild cognitive impairment (MCI) and early dementia are now available. Proponents of these tests cite as benefits the tests’ repeatability and robustness and the saving of clinicians’ time. However, the use of these tools to diagnose and/or monitor progressive cognitive impairment or response to treatment has not yet been evaluated.ObjectivesThe aim of this review was to determine whether or not automated computerised tests could accurately identify patients with progressive cognitive impairment in MCI and dementia and, if so, to investigate their role in monitoring disease progression and/or response to treatment.Data sourcesFive electronic databases (MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science and PsycINFO), plus ProQuest, were searched from 2005 to August 2015. The bibliographies of retrieved citations were also examined. Trial and research registers were searched for ongoing studies and reviews. A second search was run to identify individual test costs and acquisition costs for the various tools identified in the review.Review methodsTwo reviewers independently screened all titles and abstracts to identify potentially relevant studies for inclusion in the review. Full-text copies were assessed independently by two reviewers. Data were extracted and assessed for risk of bias by one reviewer and independently checked for accuracy by a second. The results of the data extraction and quality assessment for each study are presented in structured tables and as a narrative summary.ResultsThe electronic searching of databases, including ProQuest, resulted in 13,542 unique citations. The titles and abstracts of these were screened and 399 articles were shortlisted for full-text assessment. Sixteen studies were included in the diagnostic accuracy review. No studies were eligible for inclusion in the review of tools for monitoring progressive disease. Eleven automated computerised tests were assessed in the 16 included studies. 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 meaningful synthesis or statistical analysis was not possible.LimitationsThe main limitation of this review is the substantial heterogeneity of the tests assessed in the included studies. As a result, no meta-analyses could be undertaken.ConclusionThe quantity of information available is insufficient to be able to make recommendations on the clinical use of the computerised tests for diagnosing and monitoring MCI and early dementia progression. The value of these tests also depends on the costs of acquisition, training, administration and scoring.Future workResearch is required to establish stable cut-off points for automated computerised tests that are used to diagnose patients with MCI or early dementia. Additionally, the costs associated with acquiring and using these tests in clinical practice should be estimated.Study registrationThe study is registered as PROSPERO CRD42015025410.FundingThe National Institute for Health Research Health Technology Assessment programme.