Neuropsychological tests for the diagnosis of Alzheimer's disease dementia and other dementias: a generic protocol for cross-sectional and delayed-verification studies

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.

Correlation between neurofilament, HMGB1, MMP9, ds DNA blood levels and cognitive impairment in patients with neuropsychiatric systemic lupus erythematosus

Background

Diagnosis of neuropsychiatric systemic lupus erythematosus (NPSLE) is challenging due to nonspecific biomarkers. High serum levels of neurofilament protein light subunit (NFL), high mobility group box 1 (HMGB1), Matrix metalloproteinase-9 (MMP-9) and have been reported in several autoimmune diseases. The aim of this study was to examine whether their plasma levels could serve as a diagnostic or prognostic biomarker for NPSLE.

Methods

There were 90 SLE patients enrolled in this cross-sectional study (87.8% women and 12.2% men with a mean age of 41.67±11.05 years). We assessed the mental status of patients, also we measured the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and Systemic Lupus International Collaborating Clinics/ACR (SLICC/ ACR) Damage Index or SDI scores. Serum levels of NFL, HMGB1, MMP9, and ds-DNA were investigated to find a role in the pathophysiology of NPSLE.

Results

Among the 90 patients with SLE, 63 (70%) met the criteria of NPSLE syndrome. Our results have shown a notable difference concerning SEDIAC-2k score, SDI score, PANS, MoCA, and Beck anxiety depression, between the two groups (p < 0.05). Although serum level of all measured serum biomarkers (NFL, MMP-9, HMGB1, dsDNA) were higher in patients with NPSLE, the difference was not statistically significant. Interestingly, our results showed that the serum level of NFL was correlated with the serum level of HMGB-1 and MMP-9. (r: 0.411, P=0.003).

Conclusion

Serum level of NFL, HMGB-1 and MMP-9 may be used to detect abnormal mental status in patients with SLE.

Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) for the diagnosis of delirium in adults in critical care settings

BACKGROUND

Delirium is an underdiagnosed clinical syndrome typified by an acute alteration of mental state. It is an important problem in critical care and intensive care units (ICU) due to its high prevalence and its association with adverse outcomes. Delirium is a very distressing condition for patients, with a huge impact on their well-being. Diagnosis of delirium in the critical care setting is challenging. This is especially true for patients who are mechanically ventilated and are therefore unable to engage in a verbal interview. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) is a tool specifically designed to assess for delirium in the context of ICU patients, including those on mechanical ventilation. CAM-ICU can be administered by non-specialists to give a dichotomous delirium present/absent result.

OBJECTIVES

To determine the diagnostic accuracy of the CAM-ICU for the diagnosis of delirium in adult patients in critical care units.

SEARCH METHODS

We searched MEDLINE (Ovid SP, 1946 to 8 July 2022), Embase (Ovid SP, 1982 to 8 July 2022), Web of Science Core Collection (ISI Web of Knowledge, 1945 to 8 July 2022), PsycINFO (Ovid SP, 1806 to 8 July 2022), and LILACS (BIREME, 1982 to 8 July 2022). We checked the reference lists of included studies and other resources for additional potentially relevant studies. We also searched the Health Technology Assessment database, the Cochrane Library, Aggressive Research Intelligence Facility database, WHO ICTRP, ClinicalTrials.gov, and websites of scientific associations to access any annual meetings and abstracts of conference proceedings in the field.

SELECTION CRITERIA

We included diagnostic studies enrolling adult ICU patients assessed using the CAM-ICU tool, regardless of language or publication status and reporting sufficient data on delirium diagnosis for the construction of 2 x 2 tables. Eligible studies evaluated the diagnostic performance of the CAM-ICU versus a clinical reference standard based on any iteration of the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria applied by a clinical expert.

DATA COLLECTION AND ANALYSIS

Two review authors independently selected and collated study data. We assessed the methodological quality of studies using the QUADAS-2 tool. We used two univariate fixed-effect or random-effects models to determine summary estimates of sensitivity and specificity. We performed sensitivity analyses that excluded studies considered to be at high risk of bias and high concerns in applicability, due mainly to the target population included (e.g. patients with traumatic brain injury). We also investigated potential sources of heterogeneity, assessing the effect of reference standard diagnosis and proportion of patients ventilated.

MAIN RESULTS

We included 25 studies (2817 participants). The mean age of participants ranged from 48 to 69 years; 15 of the studies included critical care units admitting mixed populations (e.g. medical, trauma, surgery patients). The percentage of patients receiving mechanical ventilation ranged from 11.8% to 100%. The prevalence of delirium in the studies included ranged from 12.5% to 83.9%. Presence of delirium was determined by the application of DSM-IV criteria in 13 out of 25 included studies. We assessed 13 studies as at low risk of bias and low applicability concerns for all QUADAS-2 domains. The most common issue of concern was flow and timing of the tests, followed by patient selection. Overall, we estimated a pooled sensitivity of 0.78 (95% confidence interval (CI) 0.72 to 0.83) and a pooled specificity of 0.95 (95% CI 0.92 to 0.97). Sensitivity analysis restricted to studies at low risk of bias and without any applicability concerns (n = 13 studies) gave similar summary accuracy indices (sensitivity 0.80 (95% CI 0.72 to 0.86), specificity 0.95 (95% CI 0.93 to 0.97)). Subgroup analyses based on diagnostic assessment found summary estimates of sensitivity and specificity for studies using DSM-IV of 0.79 (95% CI 0.72 to 0.85) and 0.94 (95% CI 0.90 to 0.96). For studies that used DSM-5 criteria, summary estimates of sensitivity and specificity were 0.75 (95% CI 0.67 to 0.82) and 0.98 (95% CI 0.95 to 0.99). DSM criteria had no significant effect on sensitivity (P = 0.421), but the specificity for detection of delirium was higher when DSM-5 criteria were used (P = 0.024). The relative specificity comparing DSM-5 versus DSM-IV criteria was 1.05 (95% CI 1.02 to 1.08). Summary estimates of sensitivity and specificity for studies recruiting < 100% of patients with mechanical ventilation were 0.81 (95% CI 0.75 to 0.85) and 0.95 (95% CI 0.91 to 0.98). For studies that exclusively recruited patients with mechanical ventilation, summary estimates of sensitivity and specificity were 0.91 (95% CI 0.76 to 0.97) and 0.98 (95% CI 0.92 to 0.99). Although there was a suggestion of differential performance of CAM-ICU in ventilated patients, the differences were not significant in sensitivity (P = 0.316) or in specificity (P = 0.493).

AUTHORS' CONCLUSIONS

The CAM-ICU tool may have a role in the early identification of delirium, in adult patients hospitalized in intensive care units, including those on mechanical ventilation, when non-specialized, properly trained clinical personnel apply the CAM-ICU. The test is most useful for exclusion of delirium. The test may miss a proportion of patients with incident delirium, therefore in situations where detection of all delirium cases is desirable, it may be best to repeat the test or combine CAM-ICU with another assessment. Future studies should compare different screening tests proposed for bedside assessment of delirium, as this approach will reveal which tool yields superior accuracy. In addition, future studies should consider and report the flow and timing of the tests and clearly report key characteristics related to patient selection. Finally, future research should focus on the impact of CAM-ICU screening on patient outcomes.

MoCA in five Indian languages: A brief screening tool to diagnose dementia and MCI in a linguistically diverse setting

INTRODUCTION AND OBJECTIVES

Early dementia diagnosis in low and middle-income countries (LMIC) is challenging due to limited availability of brief, culturally appropriate, and psychometrically validated tests. Montreal Cognitive Assessment (MoCA) is one of the most widely used cognitive screening tests in primary and secondary care globally. In the current study, we adapted and validated MoCA in five Indian languages (Hindi, Bengali, Telugu, Kannada, and Malayalam) and determined the optimal cut-off points that correspond to screening for clinical diagnosis of dementia and MCI.

METHODS

A systematic process of adaptation and modifications of MoCA was fulfilled. A total of 446 participants: 214 controls, 102 dementia, and 130 MCI were recruited across six centers.

RESULTS

Across five languages, the area under the curve for diagnosis of dementia varied from 0.89 to 0.98 and MCI varied from 0.73 to 0.96. The sensitivity, specificity and optimum cut-off scores were established separately for five Indian languages.

CONCLUSIONS

The Indian adapted MoCA is standardized and validated in five Indian languages for early diagnosis of dementia and MCI in a linguistically and culturally diverse population.

Comparison of the Greek Version of the Quick Mild Cognitive Impairment Screen and Montreal Cognitive Assessment in Older Adults

Objective: Cognitive screening instruments (CSIs) are essential for everyday practice. The Quick Mild Cognitive Impairment (Qmci) screen, a short instrument designed to identify mild cognitive impairment, was recently translated into Greek (Qmci-Gr). The present study compared its diagnostic value against the Montreal Cognitive Assessment (MoCA) screen and examined its optimal cutoffs. Method: We recruited consecutive patients aged ≥55 years that presented with cognitive complaints from two outpatient clinics in Greece. The Qmci-Gr and MoCA were completed by all patients. Furthermore, they were assessed independently with a comprehensive flexible neuropsychological battery to establish a diagnostic classification. Results: In the current study, we assessed a total of 145 patients, with a median age of 70 years; 44 were classified as having Subjective Memory Complaints (SMC) but normal cognition, 32 with MCI and 69 with dementia. The Qmci-Gr had a higher accuracy compared to the MoCA in discriminating MCI from dementia, area under the curve (AUC) of 0.81 versus 0.75, respectively; however, this finding was marginally significant (p = 0.08). Its accuracy was marginally higher for distinguishing SMC from dementia, AUC of 0.94 versus 0.89 (p = 0.03). However, Qmci-Gr presented a lower accuracy than MoCa in differentiating SMC from MCI, AUC of 0.76 versus 0.94 (p = 0.006). Conclusions: The Qmci-Gr has comparable diagnostic accuracy to the MoCA regarding MCI and dementia groups. Further research, with larger and more diverse samples, may be necessary to ensure generalizability.

Diagnostic test accuracy of remote, multidomain cognitive assessment (telephone and video call) for dementia

BACKGROUND

Remote cognitive assessments are increasingly needed to assist in the detection of cognitive disorders, but the diagnostic accuracy of telephone- and video-based cognitive screening remains unclear.

OBJECTIVES

To assess the test accuracy of any multidomain cognitive test delivered remotely for the diagnosis of any form of dementia. To assess for potential differences in cognitive test scoring when using a remote platform, and where a remote screener was compared to the equivalent face-to-face test.

SEARCH METHODS

We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group Specialized Register, CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, LILACS, and ClinicalTrials.gov (www.

CLINICALTRIALS

gov/) databases on 2 June 2021. We performed forward and backward searching of included citations.

SELECTION CRITERIA

We included cross-sectional studies, where a remote, multidomain assessment was administered alongside a clinical diagnosis of dementia or equivalent face-to-face test.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed risk of bias and extracted data; a third review author moderated disagreements. Our primary analysis was the accuracy of remote assessments against a clinical diagnosis of dementia. Where data were available, we reported test accuracy as sensitivity and specificity. We did not perform quantitative meta-analysis as there were too few studies at individual test level. For those studies comparing remote versus in-person use of an equivalent screening test, if data allowed, we described correlations, reliability, differences in scores and the proportion classified as having cognitive impairment for each test.

MAIN RESULTS

The review contains 31 studies (19 differing tests, 3075 participants), of which seven studies (six telephone, one video call, 756 participants) were relevant to our primary objective of describing test accuracy against a clinical diagnosis of dementia. All studies were at unclear or high risk of bias in at least one domain, but were low risk in applicability to the review question. Overall, sensitivity of remote tools varied with values between 26% and 100%, and specificity between 65% and 100%, with no clearly superior test. Across the 24 papers comparing equivalent remote and in-person tests (14 telephone, 10 video call), agreement between tests was good, but rarely perfect (correlation coefficient range: 0.48 to 0.98).

AUTHORS' CONCLUSIONS

Despite the common and increasing use of remote cognitive assessment, supporting evidence on test accuracy is limited. Available data do not allow us to suggest a preferred test. Remote testing is complex, and this is reflected in the heterogeneity seen in tests used, their application, and their analysis. More research is needed to describe accuracy of contemporary approaches to remote cognitive assessment. While data comparing remote and in-person use of a test were reassuring, thresholds and scoring rules derived from in-person testing may not be applicable when the equivalent test is adapted for remote use.

The effect of long-term radiofrequency exposure on cognition in human observational studies: A protocol for a systematic review

BACKGROUND

The long term effects of exposure to radiofrequency (RF) electromagnetic fields (EMF) for frequencies from 100 kHz to 300 GHz on cognitive performance are best assessed using observational studies. In recent years, the use of mobile (cell) phones has been the main source of RF EMF exposure to the brain, although other sources of exposure may be significant. Cognitive function includes various mental and psychological abilities, which can be measured in a range of domains, such as learning, memory, reasoning, problem solving, decision making and attention. Although effects on cognitive function may be most evident later in life, in the experimental setting acute and immediate effects can only be studied. Observational studies are needed when effects are observed after months or years following short or long-term exposure. The importance of the effects of exposure on children has also been recently identified.

OBJECTIVES

To assess the long-term effects of RF EMF local and whole-body exposure compared to no or a lower level of exposure on indicators of cognition, including complex attention, executive function, learning and memory, perceptual motor ability and social cognition, but excluding cognitive effects caused by neurodegenerative diseases or neurodevelopmental disorders, and to assess if there is evidence of a dose response relationship.

STUDY ELIGIBILITY AND CRITERIA

We will include observational studies that have evaluated cognitive effects of RF energy including a comparator group with a different level of exposure. Studies must report at least one validated measure of cognitive function, including global or domain specific measures, or cognitive impairment, with a minimum follow-up of 6 months. Cohort or case-control studies published in the peer review literature in any language are eligible. We will exclude cross-sectional studies and any that only report brain structure or biomarkers.

STUDY APPRAISAL AND SYNTHESIS METHOD

We will conduct searches of PubMed, Embase, PsycINFO and the EMF-Portal. At least two authors will independently screen the titles/abstracts of all records, with any conflicts resolved by a third reviewer. Full-text screening will also be conducted independently by two authors with conflicts resolved by consensus. Data will be extracted from the studies included, such as identifiers and characteristics of the study design, exposure and comparator groups, participants, outcomes assessed and results. Risk of bias will be assessed with the Office of Health Assessment and Translation (OHAT) tool. We will conduct a meta-analysis of similar studies with a random effects model in STATA or similar software, if two or more studies are available for a given exposure-outcome combination. Confidence in the body evidence will be judged using GRADE methods as adapted by OHAT for reviews of environmental exposures.

The patient-caregiver dyad: the impact of cognitive and functional impairment

This study evaluates the cognitive impairment impact on the caregiver's burden and quality of life.Patient-caregiver dyads admitted to dementia Diagnostic-Therapeutic Care Pathway underwent a psychological and neuropsychological assessment. Overall, 30 caregivers (age 58.97 ± 14.68) of patients with dementia and 28 caregivers (age 58.57 ± 12.22) of patients with MCI were recruited. Caregiver's burden is positively correlated to the number (r = .37, p = .003) and severity (r = .37, p = .003) of neuropsychiatric patient's symptoms and with the caregiver's distress (r = .36, p = .004). It is also negatively related to good quality of life perception (r =  - .52, p =  < .0001), to lower cognitive impairment (r =  - .26, p = .05), to higher patient's residual functional abilities in daily living (r =  - .32, p = .010) and to positive perception of the physician's communication (r =  - .28, p = .026). Moreover, the caregiver's burden is significantly predicted by the patient's low level of instrumental activity of daily living (β =  - .74; p = .043) and by the number of neuropsychiatric symptoms (β = .74; p = .029). Thus, this study suggests that the autonomy and neuropsychiatric symptoms may determine the caregiver's burden.

Screening for depression in older adults with cognitive impairment in the homecare setting: a systematic review

OBJECTIVE

Previous systematic reviews have examined depression screening in older adults with cognitive impairment (CI) in outpatient and inpatient clinics, nursing homes, and residential care. Despite an increasing number of older adults with CI receiving care in their homes, less is known about best depression screening practices in homecare. The objective of this review is to identify evidence-based practices for depression screening for individuals with CI receiving homecare by assessing tool performance and establishing the current evidence for screening practices in this setting.

METHODS

This review is registered under PROSPERO (ID: CRD42018110243). A systematic search was conducted using MEDLINE, EMBASE, Health and Psychosocial Abstracts, PsycINFO and CINAHL. The following criteria were used: assessment of depression at home in older adults (>55 years) with CI, where performance outcomes of the depression screening tool were reported.

RESULTS

Of 5,453 studies, only three met eligibility criteria. These studies evaluated the Patient Health Questionnaire (n = 236), the Geriatric Depression Scale (n = 79) and the Mental Health Index (n = 1,444) in older adults at home with and without CI. Psychometric evaluation demonstrated moderate performance in the subsamples of people with CI.

CONCLUSION

At present, there is insufficient evidence to support best practices in screening for depression in people with CI in homecare.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

The optimal management of post-stroke cognitive impairment remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making around prevention, diagnosis, treatment and prognosis. These guidelines were developed according to ESO standard operating procedure and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews and, where possible, meta-analyses of the literature, assessed the quality of the available evidence and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations based on the GRADE approach. There was limited randomised controlled trial evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Interventions to improve lifestyle and treat vascular risk factors may have many health benefits but a beneficial effect on cognition is not proven. We found no evidence around routine cognitive screening following stroke but recognise the importance of targeted cognitive assessment. We described the accuracy of various cognitive screening tests but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognitive syndromes (cognitive impairment, dementia and delirium). The association between post-stroke cognitive impairment and most acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on acute MRI brain may help predict cognitive outcomes. These guidelines have highlighted fundamental areas where robust evidence is lacking. Further, definitive randomised controlled trials are needed, and we suggest priority areas for future research.

Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)

BACKGROUND

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings.

OBJECTIVES

To determine the accuracy of the Mini Mental State Examination for the early detection of dementia in people with mild cognitive impairment SEARCH METHODS: We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data.

SELECTION CRITERIA

We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve.

MAIN RESULTS

In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis.

AUTHORS' CONCLUSIONS

Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

Diagnostic test accuracy of telehealth assessment for dementia and mild cognitive impairment

BACKGROUND

Many millions of people living with dementia around the world are not diagnosed, which has a negative impact both on their access to care and treatment and on rational service planning. Telehealth - the use of information and communication technology (ICT) to provide health services at a distance - may be a way to increase access to specialist assessment for people with suspected dementia, especially those living in remote or rural areas. It has also been much used during the COVID-19 pandemic. It is important to know whether diagnoses made using telehealth assessment are as accurate as those made in conventional, face-to-face clinical settings.

OBJECTIVES

Primary objective: to assess the diagnostic accuracy of telehealth assessment for dementia and mild cognitive impairment. Secondary objectives: to identify the quality and quantity of the relevant research evidence; to identify sources of heterogeneity in the test accuracy data; to identify and synthesise any data on patient or clinician satisfaction, resource use, costs or feasibility of the telehealth assessment models in the included studies.

SEARCH METHODS

We searched multiple databases and clinical trial registers on 4 November 2020 for published and 'grey' literature and registered trials. We applied no search filters and no language restrictions. We screened the retrieved citations in duplicate and assessed in duplicate the full texts of papers considered potentially relevant.

SELECTION CRITERIA

We included in the review cross-sectional studies with 10 or more participants who had been referred to a specialist service for assessment of a suspected cognitive disorder. Within a period of one month or less, each participant had to undergo two clinical assessments designed to diagnose dementia or mild cognitive impairment (MCI): a telehealth assessment (the index test) and a conventional face-to-face assessment (the reference standard). The telehealth assessment could be informed by some data collected face-to-face, e.g. by nurses working in primary care, but all contact between the patient and the specialist clinician responsible for synthesising the information and making the diagnosis had to take place remotely using ICT.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data from included studies. Data extracted covered study design, setting, participants, details of index test and reference standard, and results in the form of numbers of participants given diagnoses of dementia or MCI. Data were also sought on dementia subtype diagnoses and on quantitative measures of patient or clinician satisfaction, resource use, costs and feasibility. We assessed risk of bias and applicability of each included study using QUADAS-2. We entered the results into 2x2 tables in order to calculate the sensitivity and specificity of telehealth assessment for the diagnosis of all-cause dementia, MCI, and any cognitive syndrome (combining dementia and MCI). We presented the results of included studies narratively because there were too few studies to derive summary estimates of sensitivity and specificity.

MAIN RESULTS

Three studies with 136 participants were eligible for inclusion. Two studies (20 and 100 participants) took place in community settings in Australia and one study (16 participants) was conducted in veterans' homes in the USA. Participants were referred from primary care with undiagnosed cognitive symptoms or were identified as being at high risk of having dementia on a screening test in the care homes. Dementia and MCI were target conditions in the larger study; the other studies targeted dementia diagnosis only. Only one small study used a 'pure' telehealth model, i.e. not involving any elements of face-to-face assessment. The studies were generally well-conducted. We considered two studies to be at high risk of incorporation bias because a substantial amount of information collected face-to-face by nurses was used to inform both index test and reference standard assessments. One study was at unclear risk of selection bias. For the diagnosis of all-cause dementia, sensitivity of telehealth assessment ranged from 0.80 to 1.00 and specificity from 0.80 to 1.00. We considered this to be very low-certainty evidence due to imprecision, inconsistency between studies and risk of bias. For the diagnosis of MCI, data were available from only one study (100 participants) giving a sensitivity of 0.71 (95% CI 0.54 to 0.84) and a specificity of 0.73 (95% CI 0.60 to 0.84). We considered this to be low-certainty evidence due to imprecision and risk of bias. For diagnosis of any cognitive syndrome (dementia or MCI), data from the same study gave a sensitivity of 0.97 (95% CI 0.91 to 0.99) and a specificity of 0.22 (95% CI 0.03 to 0.60). The majority of diagnostic disagreements concerned the distinction between MCI and dementia, occurring approximately equally in either direction. There was also a tendency for patients identified as cognitively healthy at face-to-face assessment to be diagnosed with MCI at telehealth assessment (but numbers were small). There were insufficient data to make any assessment of the accuracy of dementia subtype diagnosis. One study provided a small amount of data indicating a good level of clinician and especially patient satisfaction with the telehealth model. There were no data on resource use, costs or feasibility.

AUTHORS' CONCLUSIONS

We found only very few eligible studies with a small number of participants. An important difference between the studies providing data for the analyses was whether the target condition was dementia only (two studies) or dementia and MCI (one study). The data suggest that telehealth assessment may be highly sensitive and specific for the diagnosis of all-cause dementia when assessed against a reference standard of conventional face-to-face assessment, but the estimates are imprecise due to small sample sizes and between-study heterogeneity, and may apply mainly to telehealth models which incorporate a considerable amount of face-to-face contact with healthcare professionals other than the doctor responsible for making the diagnosis. For the diagnosis of MCI by telehealth assessment, best estimates of both sensitivity and specificity were somewhat lower, but were based on a single study. Errors occurred at the cognitively healthy/MCI and the MCI/dementia boundaries. However, there is no evidence that diagnostic disagreements were more frequent than would be expected due to the known variation between clinicians' opinions when assigning a dementia diagnosis.

Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) for the detection of dementia within a secondary care setting

BACKGROUND

The diagnosis of dementia relies on the presence of new-onset cognitive impairment affecting an individual's functioning and activities of daily living. The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) is a questionnaire instrument, completed by a suitable 'informant' who knows the patient well, designed to assess change in functional performance secondary to cognitive change; it is used as a tool for identifying those who may have dementia. In secondary care there are two specific instances where patients may be assessed for the presence of dementia. These are in the general acute hospital setting, where opportunistic screening may be undertaken, or in specialist memory services where individuals have been referred due to perceived cognitive problems. To ensure an instrument is suitable for diagnostic use in these settings, its test accuracy must be established.

OBJECTIVES

To determine the accuracy of the informant-based questionnaire IQCODE for detection of dementia in a secondary care setting.

SEARCH METHODS

We searched the following sources on the 28th of January 2013: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE (Ovid SP), EMBASE (Ovid SP), PsycINFO (Ovid SP), BIOSIS Previews (Thomson Reuters Web of Science), Web of Science Core Collection (includes Conference Proceedings Citation Index) (Thomson Reuters Web of Science), CINAHL (EBSCOhost) and LILACS (BIREME). We also searched sources specific to diagnostic test accuracy: MEDION (Universities of Maastricht and Leuven); DARE (Database of Abstracts of Reviews of Effects - via the Cochrane Library); HTA Database (Health Technology Assessment Database via the Cochrane Library) and ARIF (Birmingham University). We also checked reference lists of relevant studies and reviews, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on IQCODE for dementia diagnosis to try to find additional studies. We developed a sensitive search strategy; search terms were designed to cover key concepts using several different approaches run in parallel and included terms relating to cognitive tests, cognitive screening and dementia. We used standardised database subject headings such as MeSH terms (in MEDLINE) and other standardised headings (controlled vocabulary) in other databases, as appropriate.

SELECTION CRITERIA

We selected those studies performed in secondary-care settings, which included (not necessarily exclusively) IQCODE to assess for the presence of dementia and where dementia diagnosis was confirmed with clinical assessment. For the 'secondary care' setting we included all studies which assessed patients in hospital (e.g. acute unscheduled admissions, referrals to specialist geriatric assessment services etc.) and those referred for specialist 'memory' assessment, typically in psychogeriatric services.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by electronic database searches, and reviewed abstracts of all potentially relevant studies. Two independent assessors checked full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool, and reporting quality using the STARD tool.

MAIN RESULTS

From 72 papers describing IQCODE test accuracy, we included 13 papers, representing data from 2745 individuals (n = 1413 (51%) with dementia). Pooled analysis of all studies using data presented closest to a cut-off of 3.3 indicated that sensitivity was 0.91 (95% CI 0.86 to 0.94); specificity 0.66 (95% CI 0.56 to 0.75); the positive likelihood ratio was 2.7 (95% CI 2.0 to 3.6) and the negative likelihood ratio was 0.14 (95% CI 0.09 to 0.22). There was a statistically significant difference in test accuracy between the general hospital setting and the specialist memory setting (P = 0.019), suggesting that IQCODE performs better in a 'general' setting. We found no significant differences in the test accuracy of the short (16-item) versus the 26-item IQCODE, or in the language of administration. There was significant heterogeneity in the included studies, including a highly varied prevalence of dementia (10.5% to 87.4%). Across the included papers there was substantial potential for bias, particularly around sampling of included participants and selection criteria, which may limit generalisability. There was also evidence of suboptimal reporting, particularly around disease severity and handling indeterminate results, which are important if considering use in clinical practice.

AUTHORS' CONCLUSIONS

The IQCODE can be used to identify older adults in the general hospital setting who are at risk of dementia and require specialist assessment; it is useful specifically for ruling out those without evidence of cognitive decline. The language of administration did not affect test accuracy, which supports the cross-cultural use of the tool. These findings are qualified by the significant heterogeneity, the potential for bias and suboptimal reporting found in the included studies.

Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) for the detection of dementia within a general practice (primary care) setting

BACKGROUND

The IQCODE (Informant Questionnaire for Cognitive Decline in the Elderly) is a commonly used questionnaire based tool that uses collateral information to assess for cognitive decline and dementia. Brief tools that can be used for dementia "screening" or "triage" may have particular utility in primary care / general practice healthcare settings but only if they have suitable test accuracy. A synthesis of the available data regarding IQCODE accuracy in a primary care setting should help inform cognitive assessment strategies for clinical practice; research and policy.

OBJECTIVES

To determine the accuracy of the informant-based questionnaire IQCODE, for detection of dementia in a primary care setting.

SEARCH METHODS

A search was performed in the following sources on the 28th of January 2013: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE (Ovid SP), EMBASE (Ovid SP), PsycINFO (Ovid SP), BIOSIS (Ovid SP), ISI Web of Science and Conference Proceedings (ISI Web of Knowledge), CINHAL (EBSCOhost) and LILACs (BIREME). We also searched sources specific to diagnostic test accuracy: MEDION (Universities of Maastricht and Leuven); DARE (York University); HTA Database (Health Technology Assessments Database via The Cochrane Library) and ARIF (Birmingham University). We developed a sensitive search strategy; search terms were designed to cover key concepts using several different approaches run in parallel and included terms relating to cognitive tests, cognitive screening and dementia. We used standardized database subject headings such as MeSH terms (in MEDLINE) and other standardized headings (controlled vocabulary) in other databases, as appropriate.

SELECTION CRITERIA

We selected those studies performed in primary care settings, which included (not necessarily exclusively) IQCODE to assess for the presence of dementia and where dementia diagnosis was confirmed with clinical assessment. For the "primary care" setting, we included those healthcare settings where unselected patients, present for initial, non-specialist assessment of memory or non-memory related symptoms; often with a view to onward referral for more definitive assessment.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by electronic database searches and abstracts of all potentially relevant studies were reviewed. Full papers were assessed for eligibility and data extracted by two independent assessors. Quality assessment (risk of bias and applicability) was determined using the QUADAS-2 tool. Reporting quality was determined using the STARDdem extension to the STARD tool.

MAIN RESULTS

From 71 papers describing IQCODE test accuracy, we included 1 paper, representing data from 230 individuals (n=16 [7%] with dementia). The paper described those patients consulting a primary care service who self-identified as Japanese-American. Dementia diagnosis was made using Benson & Cummings criteria and the IQCODE was recorded as part of a longer interview with the informant. IQCODE accuracy was assessed at various test thresholds, with a "trade-off" between sensitivity and specificity across these cutpoints. At an IQCODE threshold of 3.2 sensitivity: 100%, specificity: 76%; for IQCODE 3.7 sensitivity: 75%, specificity: 98%. Applying the QUADAS-2 assessments, the study was at high risk of bias in all categories. In particular degree of blinding was unclear and not all participants were included in the final analysis.

AUTHORS' CONCLUSIONS

It is not possible to give definitive guidance on the test accuracy of IQCODE for the diagnosis of dementia in a primary care setting based on the single study identified. We are surprised by the lack of research using the IQCODE in primary care as this is, arguably, the most appropriate setting for targeted case finding of those with undiagnosed dementia in order to maximise opportunities to intervene and provide support for the individual and their carers.

Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) for the early detection of dementia across a variety of healthcare settings

BACKGROUND

The Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) is a structured interview based on informant responses that is used to assess for possible dementia. IQCODE has been used for retrospective or contemporaneous assessment of cognitive decline. There is considerable interest in tests that may identify those at future risk of developing dementia. Assessing a population free of dementia for the prospective development of dementia is an approach often used in studies of dementia biomarkers. In theory, questionnaire-based assessments, such as IQCODE, could be used in a similar way, assessing for dementia that is diagnosed on a later (delayed) assessment.

OBJECTIVES

To determine the accuracy of the informant-based questionnaire IQCODE for the early detection of dementia across a variety of health care settings.

SEARCH METHODS

We searched these sources on 16 January 2016: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE Ovid SP, Embase Ovid SP, PsycINFO Ovid SP, BIOSIS Previews on Thomson Reuters Web of Science, Web of Science Core Collection (includes Conference Proceedings Citation Index) on Thomson Reuters Web of Science, CINAHL EBSCOhost, and LILACS BIREME. We also searched sources specific to diagnostic test accuracy: MEDION (Universities of Maastricht and Leuven); DARE (Database of Abstracts of Reviews of Effects, in the Cochrane Library); HTA Database (Health Technology Assessment Database, in the Cochrane Library), and ARIF (Birmingham University). We checked reference lists of included studies and reviews, used searches of included studies in PubMed to track related articles, and contacted research groups conducting work on IQCODE for dementia diagnosis to try to find additional studies. We developed a sensitive search strategy; search terms were designed to cover key concepts using several different approaches run in parallel, and included terms relating to cognitive tests, cognitive screening, and dementia. We used standardised database subject headings, such as MeSH terms (in MEDLINE) and other standardised headings (controlled vocabulary) in other databases, as appropriate.

SELECTION CRITERIA

We selected studies that included a population free from dementia at baseline, who were assessed with the IQCODE and subsequently assessed for the development of dementia over time. The implication was that at the time of testing, the individual had a cognitive problem sufficient to result in an abnormal IQCODE score (defined by the study authors), but not yet meeting dementia diagnostic criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches, and reviewed abstracts of all potentially relevant studies. Two assessors independently checked the full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool, and reported quality using the STARDdem tool.

MAIN RESULTS

From 85 papers describing IQCODE, we included three papers, representing data from 626 individuals. Of this total, 22% (N = 135/626) were excluded because of prevalent dementia. There was substantial attrition; 47% (N = 295) of the study population received reference standard assessment at first follow-up (three to six months) and 28% (N = 174) received reference standard assessment at final follow-up (one to three years). Prevalence of dementia ranged from 12% to 26% at first follow-up and 16% to 35% at final follow-up. The three studies were considered to be too heterogenous to combine, so we did not perform meta-analyses to describe summary estimates of interest. Included patients were poststroke (two papers) and hip fracture (one paper). The IQCODE was used at three thresholds of positivity (higher than 3.0, higher than 3.12 and higher than 3.3) to predict those at risk of a future diagnosis of dementia. Using a cut-off of 3.0, IQCODE had a sensitivity of 0.75 (95%CI 0.51 to 0.91) and a specificity of 0.46 (95%CI 0.34 to 0.59) at one year following stroke. Using a cut-off of 3.12, the IQCODE had a sensitivity of 0.80 (95%CI 0.44 to 0.97) and specificity of 0.53 (95C%CI 0.41 to 0.65) for the clinical diagnosis of dementia at six months after hip fracture. Using a cut-off of 3.3, the IQCODE had a sensitivity of 0.84 (95%CI 0.68 to 0.94) and a specificity of 0.87 (95%CI 0.76 to 0.94) for the clinical diagnosis of dementia at one year after stroke. In generaI, the IQCODE was sensitive for identification of those who would develop dementia, but lacked specificity. Methods for both excluding prevalent dementia at baseline and assessing for the development of dementia were varied, and had the potential to introduce bias.

AUTHORS' CONCLUSIONS

Included studies were heterogenous, recruited from specialist settings, and had potential biases. The studies identified did not allow us to make specific recommendations on the use of the IQCODE for the future detection of dementia in clinical practice. The included studies highlighted the challenges of delayed verification dementia research, with issues around prevalent dementia assessment, loss to follow-up over time, and test non-completion potentially limiting the studies. Future research should recognise these issues and have explicit protocols for dealing with them.

Mini-Cog for the detection of dementia within a secondary care setting

BACKGROUND

The diagnosis of Alzheimer's disease dementia and other dementias relies on clinical assessment. There is a high prevalence of cognitive disorders, including undiagnosed dementia in secondary care settings. Short cognitive tests can be helpful in identifying those who require further specialist diagnostic assessment; however, there is a lack of consensus around the optimal tools to use in clinical practice. The Mini-Cog is a short cognitive test comprising three-item recall and a clock-drawing test that is used in secondary care settings.

OBJECTIVES

The primary objective was to determine the accuracy of the Mini-Cog for detecting dementia in a secondary care setting. The secondary objectives were to investigate the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity will include the baseline prevalence of dementia in study samples, thresholds used to determine positive test results, the type of dementia (Alzheimer's disease dementia or all causes of dementia), and aspects of study design related to study quality.

SEARCH METHODS

We searched the following sources in September 2012, with an update to 12 March 2019: Cochrane Dementia Group Register of Diagnostic Test Accuracy Studies, MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (Web of Knowledge), Science Citation Index (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We made no exclusions with regard to language of Mini-Cog administration or language of publication, using translation services where necessary.

SELECTION CRITERIA

We included cross-sectional studies and excluded case-control designs, due to the risk of bias. We selected those studies that included the Mini-Cog as an index test to diagnose dementia where dementia diagnosis was confirmed with reference standard clinical assessment using standardised dementia diagnostic criteria. We only included studies in secondary care settings (including inpatient and outpatient hospital participants).

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts generated by the electronic database searches. Two review authors independently checked full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool. We extracted data into two-by-two tables to allow calculation of accuracy metrics for individual studies, reporting the sensitivity, specificity, and 95% confidence intervals of these measures, summarising them graphically using forest plots.

MAIN RESULTS

Three studies with a total of 2560 participants fulfilled the inclusion criteria, set in neuropsychology outpatient referrals, outpatients attending a general medicine clinic, and referrals to a memory clinic. Only n = 1415 (55.3%) of participants were included in the analysis to inform evaluation of Mini-Cog test accuracy, due to the selective use of available data by study authors. There were concerns related to high risk of bias with respect to patient selection, and unclear risk of bias and high concerns related to index test conduct and applicability. In all studies, the Mini-Cog was retrospectively derived from historic data sets. No studies included acute general hospital inpatients. The prevalence of dementia ranged from 32.2% to 87.3%. The sensitivities of the Mini-Cog in the individual studies were reported as 0.67 (95% confidence interval (CI) 0.63 to 0.71), 0.60 (95% CI 0.48 to 0.72), and 0.87 (95% CI 0.83 to 0.90). The specificity of the Mini-Cog for each individual study was 0.87 (95% CI 0.81 to 0.92), 0.65 (95% CI 0.57 to 0.73), and 1.00 (95% CI 0.94 to 1.00). We did not perform meta-analysis due to concerns related to risk of bias and heterogeneity.

AUTHORS' CONCLUSIONS

This review identified only a limited number of diagnostic test accuracy studies using Mini-Cog in secondary care settings. Those identified were at high risk of bias related to patient selection and high concerns related to index test conduct and applicability. The evidence was indirect, as all studies evaluated Mini-Cog differently from the review question, where it was anticipated that studies would conduct Mini-Cog and independently but contemporaneously perform a reference standard assessment to diagnose dementia. The pattern of test accuracy varied across the three studies. Future research should evaluate Mini-Cog as a test in itself, rather than derived from other neuropsychological assessments. There is also a need for evaluation of the feasibility of the Mini-Cog for the detection of dementia to help adequately determine its role in the clinical pathway.

Montreal Cognitive Assessment for the detection of dementia

BACKGROUND

Dementia is a progressive syndrome of global cognitive impairment with significant health and social care costs. Global prevalence is projected to increase, particularly in resource-limited settings. Recent policy changes in Western countries to increase detection mandates a careful examination of the diagnostic accuracy of neuropsychological tests for dementia.

OBJECTIVES

To determine the accuracy of the Montreal Cognitive Assessment (MoCA) for the detection of dementia.

SEARCH METHODS

We searched MEDLINE, EMBASE, BIOSIS Previews, Science Citation Index, PsycINFO and LILACS databases to August 2012. In addition, we searched specialised sources containing diagnostic studies and reviews, including MEDION (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases. We also searched ALOIS (Cochrane Dementia and Cognitive Improvement Group specialized register of diagnostic and intervention studies). We identified further relevant studies from the PubMed 'related articles' feature and by tracking key studies in Science Citation Index and Scopus. We also searched for relevant grey literature from the Web of Science Core Collection, including Science Citation Index and Conference Proceedings Citation Index (Thomson Reuters Web of Science), PhD theses and contacted researchers with potential relevant data.

SELECTION CRITERIA

Cross-sectional designs where all participants were recruited from the same sample were sought; case-control studies were excluded due to high chance of bias. We searched for studies from memory clinics, hospital clinics, primary care and community populations. We excluded studies of early onset dementia, dementia from a secondary cause, or studies where participants were selected on the basis of a specific disease type such as Parkinson's disease or specific settings such as nursing homes.

DATA COLLECTION AND ANALYSIS

We extracted dementia study prevalence and dichotomised test positive/test negative results with thresholds used to diagnose dementia. This allowed calculation of sensitivity and specificity if not already reported in the study. Study authors were contacted where there was insufficient information to complete the 2x2 tables. We performed quality assessment according to the QUADAS-2 criteria. Methodological variation in selected studies precluded quantitative meta-analysis, therefore results from individual studies were presented with a narrative synthesis.

MAIN RESULTS

Seven studies were selected: three in memory clinics, two in hospital clinics, none in primary care and two in population-derived samples. There were 9422 participants in total, but most of studies recruited only small samples, with only one having more than 350 participants. The prevalence of dementia was 22% to 54% in the clinic-based studies, and 5% to 10% in population samples. In the four studies that used the recommended threshold score of 26 or over indicating normal cognition, the MoCA had high sensitivity of 0.94 or more but low specificity of 0.60 or less.

AUTHORS' CONCLUSIONS

The overall quality and quantity of information is insufficient to make recommendations on the clinical utility of MoCA for detecting dementia in different settings. Further studies that do not recruit participants based on diagnoses already present (case-control design) but apply diagnostic tests and reference standards prospectively are required. Methodological clarity could be improved in subsequent DTA studies of MoCA by reporting findings using recommended guidelines (e.g. STARDdem). Thresholds lower than 26 are likely to be more useful for optimal diagnostic accuracy of MoCA in dementia, but this requires confirmation in further studies.

International HIV Dementia Scale for HIV-Associated Neurocognitive Disorders: A Systematic Review and Meta-Analysis

The present study aims to systematically review the evidence on the accuracy of the International HIV Dementia Scale (IHDS) test for diagnosing human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND) and outline the quality and quantity of research evidence available on the accuracy of IHDS in people living with HIV. We conducted a systematic literature review, searching five databases from inception until July 2020. We extracted dichotomized positive and negative test results at various thresholds and calculated the sensitivity and specificity of IHDS. Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Fifteen cross-sectional studies, published between 2011 and 2018, met the inclusion criteria for meta-analysis. Overall, 3760 patients were included, but most studies recruited small samples. We assessed most studies as being applicable to the review question, though we had concerns about the selection of participants in three studies. The accuracy of IHDS was investigated at thirteen cut-off points (scores 6-12). The threshold of 10 is the most useful for optimal HAND screening (including asymptomatic neurocognitive disorder, symptomatic HAND, and HIV-associated dementia) with fair diagnostic accuracy.

Informant-based screening tools for diagnosis of dementia, an overview of systematic reviews of test accuracy studies protocol

BACKGROUND

Robust diagnosis of dementia requires an understanding of the accuracy of the available diagnostic tests. Informant questionnaires are frequently used to assess for dementia in clinical practice. Recent systematic reviews have sought to establish the diagnostic test accuracy of various dementia informant screening tools. However, most reviews to date have focused on a single diagnostic tool and this does not address which tool is 'best'. A key aim of the overview of systematic reviews is to present a disparate evidence base in a single, easy to access platform.

METHODS

We will conduct an overview of systematic reviews in which we 'review the systematic reviews' of diagnostic test accuracy studies evaluating informant questionnaires for dementia. As an overview of systematic reviews of test accuracy is a relatively novel approach, we will use this review to explore methods for visual representation of complex data, for highlighting evidence gaps and for indirect comparative analyses. We will create a list of informant tools by consulting with dementia experts. We will search 6 databases (EMBASE (OVID); Health and Psychosocial Instruments (OVID); Medline (OVID); CINAHL (EBSCO); PSYCHinfo (EBSCO) and the PROSPERO registry of review protocols) to identify systematic reviews that describe the diagnostic test accuracy of informant questionnaires for dementia. We will assess review quality using the AMSTAR-2 (Assessment of Multiple Systematic Reviews) and assess reporting quality using PRISMA-DTA (Preferred Reporting Items for Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies) checklists. We will collate the identified reviews to create an 'evidence map' that highlights where evidence does and does not exist in relation to informant questionnaires. We will pool sensitivity and specificity data via meta-analysis to generate a diagnostic test accuracy summary statistic for each informant questionnaire. If data allow, we will perform a statistical comparison of the diagnostic test accuracy of each informant questionnaire using a network approach.

DISCUSSION

Our overview of systematic reviews will provide a concise summary of the diagnostic test accuracy of informant tools and highlight areas where evidence is currently lacking in this regard. It will also apply network meta-analysis techniques to a new area.

Validation of the Russian Version of the MoCA Test as a Cognitive Screening Instrument in Cognitively Asymptomatic Older Individuals and Those With Mild Cognitive Impairment

Background: Cognitive impairment is a common condition in older people, and age-related cognitive symptoms may progress to Mild Cognitive Impairment and Dementia. Physical exercise and cognitive training may be useful in maintaining cognitive function, and those developing impaired cognitive function should be advised to plan for the future. The MoCA test is a useful cognitive screening instrument, but the Russian version of this test has not yet been validated. The aim of the present study was to validate the Russian version of the MoCA test.

Methods: The study population included 160 residents of Israel aged 65 years and older with Russian as their mother tongue, 80 of whom were cognitively asymptomatic (AC) and 80 with a clinical diagnosis of MCI. All participants underwent cognitive screening using the Russian version of the MoCA test (MoCA-Ru) as well as evaluation by means of a validated computerized cognitive assessment battery (Neurotrax).

Results: The mean age of the study population was 78 ± 6.6 years and 123 (76.9%) were women. The MoCA-Ru score was higher in the AC group than in those with MCI (24.3 ± 3.74 vs. 20.2 ± 3.07, P < 0.0001). At a cutoff value of ≥25, sensitivity was 0.99 and specificity 0.54, with area under the curve (AUC) of 0.81.

Conclusions: We found the Russian language version of the MoCA test to be a useful cognitive screening instrument for older people with mild cognitive impairment.

Mini-Addenbrooke's Cognitive Examination (MACE): a Useful Cognitive Screening Instrument in Older People?

Background

The Mini-Addenbrooke’s Cognitive Examination (MACE) is a recently described brief cognitive screening instrument.

Objective

To examine the test accuracy of MACE for the identification of dementia and mild cognitive impairment (MCI) in a cohort of older patients assessed in a neurology-led dedicated cognitive disorders clinic.

Methods

Cross-sectional assessment of consecutive patients with MACE was performed independent of the reference standard diagnosis based on clinical interview of patient and, where possible, informant and structural brain imaging, and applying standard clinical diagnostic criteria for dementia and MCI. Various test accuracy metrics were examined at two MACE cut-offs ( ≤ 25/30 and ≤ 21/30), comparing the whole patient cohort with those aged ≥ 65 or ≥ 75 years, hence at different disease prevalences.

Results

Dependent upon the chosen cut-off, MACE was either very sensitive or very specific for the identification of any cognitive impairment in the older patient cohorts with increased disease prevalence. However, at both cut-offs the positive predictive values and post-test odds increased in the older patient cohorts. At the more sensitive cut-off, improvements in some new unitary test metrics were also seen.

Conclusion

MACE is a valid instrument for identification of cognitive impairment in older people. Test accuracy metrics may differ with disease prevalence.

Diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) for cognitive screening in old age psychiatry: Determining cutoff scores in clinical practice. Avoiding spectrum bias caused by healthy controls

OBJECTIVE/METHODS

The Montreal Cognitive Assessment (MoCA) is an increasingly used screening tool for cognitive impairment. While it has been validated in multiple settings and languages, most studies have used a biased case-control design including healthy controls as comparisons not representing a clinical setting. The purpose of the present cross-sectional study is to test the criterion validity of the MoCA for mild cognitive impairment (MCI) and mild dementia (MD) in an old age psychiatry cohort (n = 710). The reference standard consists of a multidisciplinary, consensus-based diagnosis in accordance with international criteria. As a secondary outcome, the use of healthy community older adults as additional comparisons allowed us to underscore the effects of case-control spectrum-bias.

RESULTS

The criterion validity of the MoCA for cognitive impairment (MCI + MD) in a case-control design, using healthy controls, was satisfactory (area under the curve [AUC] 0.93; specificity of 73% less than 26), but declined in the cross-sectional design using referred but not cognitive impaired as comparisons (AUC 0.77; specificity of 37% less than 26). In an old age psychiatry setting, the MoCA is valuable for confirming normal cognition (greater than or equal to 26, 95% sensitivity), excluding MD (greater than or equal to 21; negative predictive value [NPV] 98%) and excluding MCI (greater than or equal to 26;NPV 94%); but not for diagnosing MD (less than 21; positive predictive value [PPV] 31%) or MCI (less than 26; PPV 33%).

CONCLUSIONS

This study shows that validating the MoCA using healthy controls overestimates specificity. Taking clinical and demographic characteristics into account, the MoCA is a suitable screening tool-in an old age psychiatry setting-for distinguishing between those in need of further diagnostic investigations and those who are not but not for diagnosing cognitive impairment.

Long-term effects of alcohol consumption on cognitive function: a systematic review and dose-response analysis of evidence published between 2007 and 2018

BACKGROUND

Understanding the long-term health effects of low to moderate alcohol consumption is important for establishing thresholds for minimising the lifetime risk of harm. Recent research has elucidated the dose-response relationship between alcohol and cardiovascular outcomes, showing an increased risk of harm at levels of intake previously thought to be protective. The primary objective of this review was to examine (1) whether there is a dose-response relationship between levels of alcohol consumption and long-term cognitive effects, and (2) what the effects are of different levels of consumption.

METHODS

The review was conducted according to a pre-specified protocol. Eligible studies were those published 2007 onwards that compared cognitive function among people with different levels of alcohol consumption (measured ≥ 6 months prior to first follow-up of cognition). Major cognitive impairment was excluded. Searches were limited to MEDLINE, Embase and PsycINFO (January 2007 to April 2018). Screening, data extraction, and risk of bias assessment (ROBINS-I) were piloted by three authors, then completed by a single author and checked by a second. Analyses were undertaken to identify and characterise dose-response relationships between levels of alcohol consumption and cognition. Certainty of evidence was assessed using GRADE.

RESULTS

We included 27 cohort studies (from 4786 citations). Eighteen studies examined the effects of alcohol consumption at different levels (risk of bias 16 serious, 2 critical). Ten studies provided data for dose-response analysis. The pooled dose-response relationship showed a maximum standardised mean difference (SMD) indicating slightly better cognition among women with moderate alcohol consumption compared to current non-drinkers (SMD 0.18, 95%CI 0.02 to 0.34, at 14.4 grams/day; 5 studies, very low certainty evidence), and a trivial difference for men (SMD 0.05, 95% CI 0.00 to 0.10, at 19.4 grams/day; 6 studies, very low certainty evidence).

CONCLUSIONS

Major limitations in the design and reporting of included studies made it impossible to discern if the effects of 'lower' levels of alcohol intake are due to bias. Further review of the evidence is unlikely to resolve this issue without meta-analysis of individual patient data from cohort studies that address biases in the selection of participants and classification of alcohol consumption.

Applying Kraemer's Q (Positive Sign Rate): Some Implications for Diagnostic Test Accuracy Study Results

Background/Aims

Sensitivity and specificity (Sens, Spec) are not invariant properties of diagnostic and screening tests, but vary in different patient samples. Kraemer [Evaluating medical tests. Objective and quantitative guidelines. 1992] used the level of test, Q, also known as "positive sign rate" (sum of true and false positives divided by sample size), to calculate quality sensitivity and specificity (QSN, QSP). These scaled indices may be more comparable across different patient samples, but have been little studied hitherto.

Methods

The dataset of a pragmatic test accuracy study of the Mini-Addenbrooke's Cognitive Examination (MACE) was re-interrogated to calculate values of QSN and QSP and other paired and unitary test outcome measures based on them, and comparison was made with outcomes previously calculated by standard methods.

Results

QSN and QSP values in this cohort (n = 755; overall prevalence of dementia and mild cognitive impairment [MCI] 0.15 and 0.29, respectively) were inferior to Sens and Spec, as were all other outcome measures for MACE for the diagnosis of both dementia and MCI. QSN was relatively preserved, indicating the sensitivity of MACE.

Conclusion

Indices of test outcome scaled according to Kraemer's Q, the positive sign rate, are less impressive than outcomes calculated by standard methods. These discrepancies may have implications for test evaluation.

Free-Cog: Pragmatic Test Accuracy Study and Comparison with Mini-Addenbrooke's Cognitive Examination

BACKGROUND/AIMS

Canonical definitions of the dementia construct encompass deficits in both cognition and function, but most screening instruments for possible dementia address only cognitive abilities. Free-Cog is a recently described brief screening instrument for dementia designed to address not only cognitive but also functional abilities.

METHODS

A pragmatic test accuracy study of Free-Cog was undertaken in consecutive patients seen over 1 year in a secondary care setting. The performance of Free-Cog for diagnosis of dementia and mild cognitive impairment (MCI) was compared to that of Mini-Addenbrooke's Cognitive Examination (MACE).

RESULTS

In a cohort of 141 patients (prevalence of dementia and MCI 11 and 32%, respectively) both Free-Cog and MACE were quick and easy to use and acceptable to patients. Both tests had high sensitivity (1.00) and large effect sizes (Cohen's d) for diagnosis of dementia, but Free-Cog was more specific. For diagnosis of MCI, Free-Cog lacked sensitivity (0.58) but was specific (0.81), whereas MACE was sensitive (0.91) but not specific (0.35). Weighted comparison suggested equivalence for dementia diagnosis but a net benefit for MACE regarding MCI diagnosis.

CONCLUSION

Free-Cog is an acceptable and accurate test for dementia screening in a dedicated cognitive disorders clinic, but it appears less sensitive than MACE for the identification of MCI.

Addenbrooke's Cognitive Examination III (ACE-III) and mini-ACE for the detection of dementia and mild cognitive impairment

BACKGROUND

The number of new cases of dementia is projected to rise significantly over the next decade. Thus, there is a pressing need for accurate tools to detect cognitive impairment in routine clinical practice. The Addenbrooke's Cognitive Examination III (ACE-III), and the mini-ACE are brief, bedside cognitive screens that have previously reported good sensitivity and specificity. The quality and quantity of this evidence has not, however, been robustly investigated.

OBJECTIVES

To assess the diagnostic test accuracy of the ACE-III and mini-ACE for the detection of dementia, dementia sub-types, and mild cognitive impairment (MCI) at published thresholds in primary, secondary, and community care settings in patients presenting with, or at high risk of, cognitive decline.

SEARCH METHODS

We performed the search for this review on 13 February 2019. We searched MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science Core Collection (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We applied no language or date restrictions to the electronic searches; and to maximise sensitivity we did not use methodological filters. The search yielded 5655 records, of which 2937 remained after we removed duplicates. We identified a further four articles through PubMed 'related articles'. We found no additional records through reference list citation searching, or grey literature.

SELECTION CRITERIA

Cross-sectional studies investigating the accuracy of the ACE-III or mini-ACE in patients presenting with, or at high risk of, cognitive decline were suitable for inclusion. We excluded case-control, delayed verification and longitudinal studies, and studies which investigated a secondary cause of dementia. We did not restrict studies by language; and we included those with pre-specified thresholds (88 and 82 for the ACE-III, and 21 or 25 for the mini-ACE).

DATA COLLECTION AND ANALYSIS

We extracted information on study and participant characteristics and used information on dementia and MCI prevalence, sensitivity, specificity, and sample size to generate 2×2 tables in Review Manager 5. We assessed methodological quality of included studies using the QUADAS-2 tool; and we assessed the quality of study reporting with the STARDdem tool. Due to significant heterogeneity in the included studies and an insufficient number of studies, we did not perform meta-analyses.

MAIN RESULTS

This review identified seven studies (1711 participants in total) of cross-sectional design, four examining the accuracy of the ACE-III, and three of the mini-ACE. Overall, the majority of studies were at low or unclear risk of bias and applicability on quality assessment. Studies were at high risk of bias for the index test (n = 4) and reference standard (n = 2). Study reporting was variable across the included studies. No studies investigated dementia sub-types. The ACE-III had variable sensitivity across thresholds and patient populations (range for dementia at 82 and 88: 82% to 97%, n = 2; range for MCI at 88: 75% to 77%, n = 2), but with more variability in specificity (range for dementia: 4% to 77%, n = 2; range for MCI: 89% to 92%, n = 2). Similarly, sensitivity of the mini-ACE was variable (range for dementia at 21 and 25: 70% to 99%, n = 3; range for MCI at 21 and 25: 64% to 95%, n = 3) but with more variability specificity (range for dementia: 32% to 100%, n = 3; range for MCI: 46% to 79%, n = 3). We identified no studies in primary care populations: four studies were conducted in outpatient clinics, one study in an in-patient setting, and in two studies the settings were unclear.

AUTHORS' CONCLUSIONS

There is insufficient information in terms of both quality and quantity to recommend the use of either the ACE-III or mini-ACE for the screening of dementia or MCI in patients presenting with, or at high risk of, cognitive decline. No studies were conducted in a primary care setting so the accuracy of the ACE-III and mini-ACE in this setting are not known. Lower thresholds (82 for the ACE-III, and 21 for the mini-ACE) provide better specificity with acceptable sensitivity and may provide better clinical utility. The ACE-III and mini-ACE should only be used to support the diagnosis as an adjunct to a full clinical assessment. Further research is needed to determine the utility of the ACE-III and mini-ACE for the detection of dementia, dementia sub-types, and MCI. Specifically, the optimal thresholds for detection need to be determined in a variety of settings (primary care, secondary care (inpatient and outpatient), and community services), prevalences, and languages.

Detecting delirium in patients with acute stroke: a systematic review of test accuracy

BACKGROUND

Patients with acute stroke are particularly vulnerable to delirium episodes. Although delirium detection is important, no evidence-based recommendations have been established to date on how these patients should be routinely screened for delirium or which tool should be used for this purpose in this population. Therefore, the aim of this study was to identify delirium screening tools for patients with acute stroke and to summarise their accuracy.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search of Medline, CINAHL and Scopus databases was performed to include: (a) diagnostic test accuracy studies; (b) evaluating tools detecting delirium among patients with acute stroke; (c) written in English; (d) published up to September 2018. The included studies were assessed in their quality by using the Quality Assessment of Diagnostic Accuracy Studies-2.

RESULTS

A total of four studies have been performed to date in the field with a variable quality for the methodology used and documentation of the accuracy of mainly two tools, as (1) the 4-Assessment Test for delirium (4AT), reporting a range of sensitivity from 90.2 to 100% and a specificity from 64.5 to 86%; and (2) the Confusion Assessment Method-Intensive Care Unit (CAM-ICU) showing a sensitivity of 76% (95% Confidence of Interval [CI] 55-91) and a specificity of 98% (95%CI 93-100). Other tools have been studied as: The Abbreviated Mental Test-10, the Abbreviated Mental Test short form, the Clock Drawing Test, the Cognitive Examination derived from the National Institutes of Health Stroke Scale and the Glasgow Coma Scale. Moreover, the use of a single question-namely, 'Does this patient have cognitive issues?' as answered by the multidisciplinary team-has been subjected to a validation process.

CONCLUSIONS

To date a few primary studies have been published to test the accuracy of tools in their ability to detect post-stroke delirium; among those available, the 4AT and the CAM-ICU tools have been mostly studied. Research has just started to add evidence to the challenge of detecting and usefully assessing newly-acquired delirium among stroke patients: therefore, more studies are needed to improve the knowledge and allow a robust selection of the most useful tool to use in this population.

Mini-Cog for the diagnosis of Alzheimer's disease dementia and other dementias within a secondary care setting

BACKGROUND

The diagnosis of Alzheimer's disease dementia and other dementias relies on clinical assessment. There is a high prevalence of cognitive disorders, including undiagnosed dementia in secondary care settings. Short cognitive tests can be helpful in identifying those who require further specialist diagnostic assessment; however, there is a lack of consensus around the optimal tools to use in clinical practice. The Mini-Cog is a short cognitive test comprising three-item recall and a clock-drawing test that is used in secondary care settings.

OBJECTIVES

The primary objective was to determine the diagnostic accuracy of the Mini-Cog for detecting Alzheimer's disease dementia and other dementias in a secondary care setting. The secondary objectives were to investigate the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity will include the baseline prevalence of dementia in study samples, thresholds used to determine positive test results, the type of dementia (Alzheimer's disease dementia or all causes of dementia), and aspects of study design related to study quality.

SEARCH METHODS

We searched the following sources in September 2012, with an update to 12 March 2019: Cochrane Dementia Group Register of Diagnostic Test Accuracy Studies, MEDLINE (OvidSP), Embase (OvidSP), BIOSIS Previews (Web of Knowledge), Science Citation Index (ISI Web of Knowledge), PsycINFO (OvidSP), and LILACS (BIREME). We made no exclusions with regard to language of Mini-Cog administration or language of publication, using translation services where necessary.

SELECTION CRITERIA

We included cross-sectional studies and excluded case-control designs, due to the risk of bias. We selected those studies that included the Mini-Cog as an index test to diagnose dementia where dementia diagnosis was confirmed with reference standard clinical assessment using standardised dementia diagnostic criteria. We only included studies in secondary care settings (including inpatient and outpatient hospital participants).

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts generated by the electronic database searches. Two review authors independently checked full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool. We extracted data into two-by-two tables to allow calculation of accuracy metrics for individual studies, reporting the sensitivity, specificity, and 95% confidence intervals of these measures, summarising them graphically using forest plots.

MAIN RESULTS

Three studies with a total of 2560 participants fulfilled the inclusion criteria, set in neuropsychology outpatient referrals, outpatients attending a general medicine clinic, and referrals to a memory clinic. Only n = 1415 (55.3%) of participants were included in the analysis to inform evaluation of Mini-Cog test accuracy, due to the selective use of available data by study authors. There were concerns related to high risk of bias with respect to patient selection, and unclear risk of bias and high concerns related to index test conduct and applicability. In all studies, the Mini-Cog was retrospectively derived from historic data sets. No studies included acute general hospital inpatients. The prevalence of dementia ranged from 32.2% to 87.3%. The sensitivities of the Mini-Cog in the individual studies were reported as 0.67 (95% confidence interval (CI) 0.63 to 0.71), 0.60 (95% CI 0.48 to 0.72), and 0.87 (95% CI 0.83 to 0.90). The specificity of the Mini-Cog for each individual study was 0.87 (95% CI 0.81 to 0.92), 0.65 (95% CI 0.57 to 0.73), and 1.00 (95% CI 0.94 to 1.00). We did not perform meta-analysis due to concerns related to risk of bias and heterogeneity.

AUTHORS' CONCLUSIONS

This review identified only a limited number of diagnostic test accuracy studies using Mini-Cog in secondary care settings. Those identified were at high risk of bias related to patient selection and high concerns related to index test conduct and applicability. The evidence was indirect, as all studies evaluated Mini-Cog differently from the review question, where it was anticipated that studies would conduct Mini-Cog and independently but contemporaneously perform a reference standard assessment to diagnose dementia. The pattern of test accuracy varied across the three studies. Future research should evaluate Mini-Cog as a test in itself, rather than derived from other neuropsychological assessments. There is also a need for evaluation of the feasibility of the Mini-Cog for the diagnosis of dementia to help adequately determine its role in the clinical pathway.

Montreal Cognitive Assessment (MoCA) for HIV-Associated Neurocognitive Disorders

This study aims to systematically review the evidence on the accuracy of the Montreal Cognitive Assessment (MoCA) test for diagnosing HIV-associated neurocognitive disorders (HAND) and to outline the quality and quantity of research evidence available about the accuracy of MoCA in populations infected with HIV. We conducted a systematic literature review, searching five databases from inception until January 2019. We extracted dichotomized positive and negative test results at various thresholds and calculated the sensitivity and specificity of MoCA. Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Eight cross-sectional studies met the inclusion criteria for meta-analysis. Overall, 1014 patients were included but most studies recruited small samples. Recruitment period ranged from 2009 to 2015. We assessed most studies as being applicable to the review question though we had concerns about the selection of participants in three studies. The accuracy of MoCA for diagnosing HAND was reported at six cut-off points (scores 22-27). The MoCA test provides information about general cognitive functioning disturbances that contribute to a diagnosis of HAND. A lower threshold than the original cut-off of 26 is probably more useful for optimal screening of HAND, as it lowers false positive rates and improves diagnostic accuracy. Nonetheless, the choice of cut-off always comes with a sensitivity-specificity trade-off, the preferred cut point depending on whether sensitivity or specificity is more valuable in a given context.

Validity of screening instruments for the detection of dementia and mild cognitive impairment in hospital inpatients: A systematic review of diagnostic accuracy studies

INTRODUCTION

As the population ages, Alzheimer's disease and other subtypes of dementia are becoming increasingly prevalent. However, in recent years, diagnosis has often been delayed or not made at all. Thus, improving the rate of diagnosis has become an integral part of national dementia strategies. Although screening for dementia remains controversial, the case is strong for screening for dementia and other forms of cognitive impairment in hospital inpatients. For this reason, the objective of this systematic review was to provide clinicians, who wish to implement screening, an up-to-date choice of cognitive tests with the most extensive evidence base for the use in elective hospital inpatients.

METHODS

For this systematic review, PubMed, PsycINFO and Cochrane Library were searched by using a multi-concept search strategy. The databases were accessed on April 10, 2019. All cross-sectional studies that utilized brief, multi-domain cognitive tests as index test and a reference standard diagnosis of dementia or mild cognitive impairment as comparator were included. Only studies conducted in the hospital setting, sampling from unselected, elective inpatients older than 64 were considered.

RESULTS

Six studies met the inclusion criteria, with a total of 2112 participants. Diagnostic accuracy data for the Six-Item Cognitive Impairment Test, Cognitive Performance Scale, Clock-Drawing Test, Mini-Mental Status Examination, and Time & Change test were extracted and descriptively analyzed. Clinical and methodological heterogeneity between the studies precluded performing a meta-analysis.

DISCUSSION

This review found only a small number of instruments and was not able to recommend a single best instrument for use in a hospital setting. Although it was not possible to estimate the pooled operating characteristics, the included description of instrument characteristics, the descriptive analysis of performance measures, and the critical evaluation of the reporting studies may contribute to clinician's choice of the screening instrument that fits best their purpose.

MACE for Diagnosis of Dementia and MCI: Examining Cut-Offs and Predictive Values

The definition of test cut-offs is a critical determinant of many paired and unitary measures of diagnostic or screening test accuracy, such as sensitivity and specificity, positive and negative predictive values, and correct classification accuracy. Revision of test cut-offs from those defined in index studies is frowned upon as a potential source of bias, seemingly accepting any biases present in the index study, for example related to sample bias. Data from a large pragmatic test accuracy study examining the Mini-Addenbrooke's Cognitive Examination (MACE) were interrogated to determine optimal test cut-offs for the diagnosis of dementia and mild cognitive impairment (MCI) using either the maximal Youden index or the maximal correct classification accuracy. Receiver operating characteristic (ROC) and precision recall (PR) curves for dementia and MCI were also plotted, and MACE predictive values across a range of disease prevalences were calculated. Optimal cut-offs were found to be a point lower than those defined in the index study. MACE had good metrics for the area under the ROC curve and for the effect size (Cohen's d) for both dementia and MCI diagnosis, but PR curves suggested the superiority for MCI diagnosis. MACE had high negative predictive value at all prevalences, suggesting that a MACE test score above either cut-off excludes dementia and MCI in any setting.

AD-8 for detection of dementia across a variety of healthcare settings

BACKGROUND

Dementia assessment often involves initial screening, using a brief tool, followed by more detailed assessment where required. The AD-8 is a short questionnaire, completed by a suitable 'informant' who knows the person well. AD-8 is designed to assess change in functional performance secondary to cognitive change.

OBJECTIVES

To determine the diagnostic accuracy of the informant-based AD-8 questionnaire, in detection of all-cause (undifferentiated) dementia in adults. Where data were available, we described the following: the diagnostic accuracy of the AD-8 at various predefined threshold scores; the diagnostic accuracy of the AD-8 for each healthcare setting and the effects of heterogeneity on the reported diagnostic accuracy of the AD-8.

SEARCH METHODS

We searched the following sources on 27 May 2014, with an update to 7 June 2018: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE (Ovid SP), Embase (Ovid SP), PsycINFO (Ovid SP), BIOSIS Previews (Thomson Reuters Web of Science), Web of Science Core Collection (includes Conference Proceedings Citation Index) (Thomson Reuters Web of Science), CINAHL (EBSCOhost) and LILACS (BIREME). We checked reference lists of relevant studies and reviews, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on the AD-8 to try to find additional studies. We developed a sensitive search strategy and used standardised database subject headings as appropriate. Foreign language publications were translated.

SELECTION CRITERIA

We selected those studies which included the AD-8 to assess for the presence of dementia and where dementia diagnosis was confirmed with clinical assessment. We only included those studies where the AD-8 was used as an informant assessment. We made no exclusions in relation to healthcare setting, language of AD-8 or the AD-8 score used to define a 'test positive' case.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by electronic database searches, and reviewed abstracts of potentially relevant studies. Two independent assessors checked full papers for eligibility and extracted data. We extracted data into two-by-two tables to allow calculation of accuracy metrics for individual studies. We then created summary estimates of sensitivity, specificity and likelihood ratios using the bivariate approach and plotting results in receiver operating characteristic (ROC) space. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool.

MAIN RESULTS

From 36 papers describing AD-8 test accuracy, we included 10 papers. We utilised data from nine papers with 4045 individuals, 1107 of whom (27%) had a clinical diagnosis of dementia. Pooled analysis of seven studies, using an AD-8 informant cut-off score of two, indicated that sensitivity was 0.92 (95% confidence interval (CI) 0.86 to 0.96); specificity was 0.64 (95% CI 0.39 to 0.82); the positive likelihood ratio was 2.53 (95% CI 1.38 to 4.64); and the negative likelihood ratio was 0.12 (95% CI 0.07 to 0.21). Pooled analysis of five studies, using an AD-8 informant cut-off score of three, indicated that sensitivity was 0.91 (95% CI 0.80 to 0.96); specificity was 0.76 (95% CI 0.57 to 0.89); the positive likelihood ratio was 3.86 (95% CI 2.03 to 7.34); and the negative likelihood ratio was 0.12 (95% CI 0.06 to 0.24).Four studies were conducted in community settings; four were in secondary care (one in the acute hospital); and one study was in primary care. The AD-8 has a higher relative sensitivity (1.11, 95% CI 1.02 to 1.21), but lower relative specificity (0.51, 95% CI 0.23 to 1.09) in secondary care compared to community care settings.There was heterogeneity across the included studies. Dementia prevalence rate varied from 12% to 90% of included participants. The tool was also used in various different languages. Among all the included studies there was evidence of risk of bias. Issues included the selection of participants, conduct of index test, and flow of assessment procedures.

AUTHORS' CONCLUSIONS

The high sensitivity of the AD-8 suggests it can be used to identify adults who may benefit from further specialist assessment and diagnosis, but is not a diagnostic test in itself. This pattern of high sensitivity and lower specificity is often suited to a screening test. Test accuracy varies by setting, however data in primary care and acute hospital settings are limited. This review identified significant heterogeneity and risk of bias, which may affect the validity of its summary findings.

Validity of the QUADAS-2 in Assessing Risk of Bias in Alzheimer's Disease Diagnostic Accuracy Studies

Accurate detection of Alzheimer's disease (AD) is of considerable clinical importance. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) is the current research standard for evaluating the quality of studies that validate diagnostic tests; however, its own construct validity has not yet been evaluated empirically. Our aim was to evaluate how well the proposed QUADAS-2 items and its domains converge to indicate the study quality criteria. This study applies confirmatory factor analysis to determine whether a measurement model would be consistent with meta-analytic data. Cochrane meta-analyses assessing the accuracy of AD diagnostic tests were identified. The seven ordinal QUADAS-2 items, intended to inform study quality based on risk of bias and applicability concerns, were extracted for each of the included studies. The QUADAS-2 pre-specified factor structure (i.e., four domains assessed in terms of risk of bias and applicability concerns) was not testable. An alternative model based on two correlated factors (i.e., risk of bias and applicability concerns) returned a poor fit model. Poor factor loadings were obtained, indicating that we cannot provide evidence that the indicators convergent validity markers in the context of AD diagnostic accuracy metanalyses, where normally the sample size is low (around 60 primary included studies). A Monte Carlo simulation suggested that such a model would require at least 90 primary studies to estimate these parameters with 80% power. The reliability of the QUADAS-2 items to inform a measurement model for study quality remains unconfirmed. Considerations for conceptualizing such a tool are discussed.

Accuracy and Feasibility of an Android-Based Digital Assessment Tool for Post Stroke Visual Disorders-The StrokeVision App

Background

Visual impairment affects up to 70% of stroke survivors. We designed an app (StrokeVision) to facilitate screening for common post stroke visual issues (acuity, visual fields, and visual inattention). We sought to describe the test time, feasibility, acceptability, and accuracy of our app-based digital visual assessments against (a) current methods used for bedside screening and (b) gold standard measures.

Methods

Patients were prospectively recruited from acute stroke settings. Index tests were app-based assessments of fields and inattention performed by a trained researcher. We compared against usual clinical screening practice of visual fields to confrontation, including inattention assessment (simultaneous stimuli). We also compared app to gold standard assessments of formal kinetic perimetry (Goldman or Octopus Visual Field Assessment); and pencil and paper-based tests of inattention (Albert's, Star Cancelation, and Line Bisection). Results of inattention and field tests were adjudicated by a specialist Neuro-ophthalmologist. All assessors were masked to each other's results. Participants and assessors graded acceptability using a bespoke scale that ranged from 0 (completely unacceptable) to 10 (perfect acceptability).

Results

Of 48 stroke survivors recruited, the complete battery of index and reference tests for fields was successfully completed in 45. Similar acceptability scores were observed for app-based [assessor median score 10 (IQR: 9-10); patient 9 (IQR: 8-10)] and traditional bedside testing [assessor 10 (IQR: 9-10); patient 10 (IQR: 9-10)]. Median test time was longer for app-based testing [combined time to completion of all digital tests 420 s (IQR: 390-588)] when compared with conventional bedside testing [70 s, (IQR: 40-70)], but shorter than gold standard testing [1,260 s, (IQR: 1005-1,620)]. Compared with gold standard assessments, usual screening practice demonstrated 79% sensitivity and 82% specificity for detection of a stroke-related field defect. This compares with 79% sensitivity and 88% specificity for StrokeVision digital assessment.

Conclusion

StrokeVision shows promise as a screening tool for visual complications in the acute phase of stroke. The app is at least as good as usual screening and offers other functionality that may make it attractive for use in acute stroke.

Clinical Trial Registration

https://ClinicalTrials.gov/ct2/show/NCT02539381.

18F PET with florbetapir for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

¹⁸F-florbetapir uptake by brain tissue measured by positron emission tomography (PET) is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using amyloid biomarkers tests like ¹⁸F-florbetapir. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of ¹⁸F-florbetapir to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated.

OBJECTIVES

To determine the DTA of the ¹⁸F-florbetapir PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD), or any form of dementia at follow-up.

SEARCH METHODS

This review is current to May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to the electronic searches.

SELECTION CRITERIA

We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of ¹⁸F-florbetapir scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies.

MAIN RESULTS

We included three studies, two of which evaluated the progression from MCI to ADD, and one evaluated the progression from MCI to any form of dementia.Progression from MCI to ADD was evaluated in 448 participants. The studies reported data on 401 participants with 1.6 years of follow-up and in 47 participants with three years of follow-up. Sixty-one (15.2%) participants converted at 1.6 years follow-up; nine (19.1%) participants converted at three years of follow-up.Progression from MCI to any form of dementia was evaluated in five participants with 1.5 years of follow-up, with three (60%) participants converting to any form of dementia.There were concerns regarding applicability in the reference standard in all three studies. Regarding the domain of flow and timing, two studies were considered at high risk of bias. MCI to ADD;Progression from MCI to ADD in those with a follow-up between two to less than four years had a sensitivity of 67% (95% CI 30 to 93) and a specificity of 71% (95% CI 54 to 85) by visual assessment (n = 47, 1 study).Progression from MCI to ADD in those with a follow-up between one to less than two years had a sensitivity of 89% (95% CI 78 to 95) and a specificity of 58% (95% CI 53 to 64) by visual assessment, and a sensitivity of 87% (95% CI 76 to 94) and a specificity of 51% (95% CI 45 to 56) by quantitative assessment by the standardised uptake value ratio (SUVR)(n = 401, 1 study). MCI to any form of dementia;Progression from MCI to any form of dementia in those with a follow-up between one to less than two years had a sensitivity of 67% (95% CI 9 to 99) and a specificity of 50% (95% CI 1 to 99) by visual assessment (n = 5, 1 study). MCI to any other forms of dementia (non-ADD);There was no information regarding the progression from MCI to any other form of dementia (non-ADD).

AUTHORS' CONCLUSIONS

Although sensitivity was good in one included study, considering the poor specificity and the limited data available in the literature, we cannot recommend routine use of ¹⁸F-florbetapir PET in clinical practice to predict the progression from MCI to ADD.Because of the poor sensitivity and specificity, limited number of included participants, and the limited data available in the literature, we cannot recommend its routine use in clinical practice to predict the progression from MCI to any form of dementia.Because of the high financial costs of ¹⁸F-florbetapir, clearly demonstrating the DTA and standardising the process of this modality are important prior to its wider use.

18F PET with flutemetamol for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

¹⁸F-flutemetamol uptake by brain tissue, measured by positron emission tomography (PET), is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and the confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using some amyloid biomarkers tests like ¹⁸F-flutemetamol. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of ¹⁸F-flutemetamol to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated.

OBJECTIVES

To determine the DTA of the ¹⁸F-flutemetamol PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD) or any form of dementia at follow-up.

SEARCH METHODS

The most recent search for this review was performed in May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to the electronic searches.

SELECTION CRITERIA

We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of ¹⁸F-flutemetamol scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies.

MAIN RESULTS

Progression from MCI to ADD was evaluated in 243 participants from two studies. The studies reported data on 19 participants with two years of follow-up and on 224 participants with three years of follow-up. Nine (47.4%) participants converted at two years follow-up and 81 (36.2%) converted at three years of follow-up.There were concerns about participant selection and sampling in both studies. The index test domain in one study was considered unclear and in the second study it was considered at low risk of bias. For the reference standard domain, one study was considered at low risk and the second study was considered to have an unclear risk of bias. Regarding the domains of flow and timing, both studies were considered at high risk of bias. MCI to ADD;Progression from MCI to ADD at two years of follow-up had a sensitivity of 89% (95% CI 52 to 100) and a specificity of 80% (95% CI 44 to 97) by quantitative assessment by SUVR (n = 19, 1 study).Progression from MCI to ADD at three years of follow-up had a sensitivity of 64% (95% CI 53 to 75) and a specificity of 69% (95% CI 60 to 76) by visual assessment (n = 224, 1 study).There was no information regarding the other two objectives in this systematic review (SR): progression from MCI to other forms of dementia and progression to any form of dementia at follow-up.

AUTHORS' CONCLUSIONS

Due to the varying sensitivity and specificity for predicting the progression from MCI to ADD and the limited data available, we cannot recommend routine use of ¹⁸F-flutemetamol in clinical practice. ¹⁸F-flutemetamol has high financial costs; therefore, clearly demonstrating its DTA and standardising the process of the ¹⁸F-flutemetamol modality is important prior to its wider use.

18F PET with florbetaben for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

18F-florbetaben uptake by brain tissue, measured by positron emission tomography (PET), is accepted by regulatory agencies like the Food and Drug Administration (FDA) and the European Medicine Agencies (EMA) for assessing amyloid load in people with dementia. Its added value is mainly demonstrated by excluding Alzheimer's pathology in an established dementia diagnosis. However, the National Institute on Aging and Alzheimer's Association (NIA-AA) revised the diagnostic criteria for Alzheimer's disease and confidence in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer's disease may be increased when using some amyloid biomarkers tests like 18F-florbetaben. These tests, added to the MCI core clinical criteria, might increase the diagnostic test accuracy (DTA) of a testing strategy. However, the DTA of 18F-florbetaben to predict the progression from MCI to Alzheimer's disease dementia (ADD) or other dementias has not yet been systematically evaluated.

OBJECTIVES

To determine the DTA of the 18F-florbetaben PET scan for detecting people with MCI at time of performing the test who will clinically progress to ADD, other forms of dementia (non-ADD), or any form of dementia at follow-up.

SEARCH METHODS

The most recent search for this review was performed in May 2017. We searched MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), BIOSIS Citation Index (Thomson Reuters Web of Science), Web of Science Core Collection, including the Science Citation Index (Thomson Reuters Web of Science) and the Conference Proceedings Citation Index (Thomson Reuters Web of Science), LILACS (BIREME), CINAHL (EBSCOhost), ClinicalTrials.gov (https://clinicaltrials.gov), and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (http://www.who.int/ictrp/search/en/). We also searched ALOIS, the Cochrane Dementia & Cognitive Improvement Group's specialised register of dementia studies (http://www.medicine.ox.ac.uk/alois/). We checked the reference lists of any relevant studies and systematic reviews, and performed citation tracking using the Science Citation Index to identify any additional relevant studies. No language or date restrictions were applied to electronic searches.

SELECTION CRITERIA

We included studies that had prospectively defined cohorts with any accepted definition of MCI at time of performing the test and the use of 18F-florbetaben scan to evaluate the DTA of the progression from MCI to ADD or other forms of dementia. In addition, we only selected studies that applied a reference standard for Alzheimer's dementia diagnosis, for example, the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) or Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles and abstracts identified in electronic-database searches. Two review authors independently selected studies for inclusion and extracted data to create two-by-two tables, showing the binary test results cross-classified with the binary reference standard. We used these data to calculate sensitivities, specificities, and their 95% confidence intervals. Two independent assessors performed quality assessment using the QUADAS-2 tool plus some additional items to assess the methodological quality of the included studies.

MAIN RESULTS

Progression from MCI to ADD, any other form of dementia, and any form of dementia was evaluated in one study (Ong 2015). It reported data on 45 participants at four years of follow-up; 21 participants met NINCDS-ADRDA criteria for Alzheimer's disease dementia at four years of follow-up, the proportion converting to ADD was 47% of the 45 participants, and 11% of the 45 participants met criteria for other types of dementias (three cases of FrontoTemporal Dementia (FTD), one of Dementia with Lewy body (DLB), and one of Progressive Supranuclear Palsy (PSP)). We considered the study to be at high risk of bias in the domains of the reference standard, flow, and timing (QUADAS-2). MCI to ADD; 18F-florbetaben PET scan analysed visually: the sensitivity was 100% (95% confidence interval (CI) 84% to 100%) and the specificity was 83% (95% CI 63% to 98%) (n = 45, 1 study). Analysed quantitatively: the sensitivity was 100% (95% CI 84% to 100%) and the specificity was 88% (95% CI 68% to 97%) for the diagnosis of ADD at follow-up (n = 45, 1 study). MCI to any other form of dementia (non-ADD); 18F-florbetaben PET scan analysed visually: the sensitivity was 0% (95% CI 0% to 52%) and the specificity was 38% (95% CI 23% to 54%) (n = 45, 1 study). Analysed quantitatively: the sensitivity was 0% (95% CI 0% to 52%) and the specificity was 40% (95% CI 25% to 57%) for the diagnosis of any other form of dementia at follow-up (n = 45, 1 study). MCI to any form of dementia;18F-florbetaben PET scan analysed visually: the sensitivity was 81% (95% CI 61% to 93%) and the specificity was 79% (95% CI 54% to 94%) (n = 45, 1 study). Analysed quantitatively: the sensitivity was 81% (95% CI 61% to 93%) and the specificity was 84% (95% CI 60% to 97%) for the diagnosis of any form of dementia at follow-up (n = 45, 1 study).

AUTHORS' CONCLUSIONS

Although we were able to calculate one estimation of DTA in, especially, the prediction of progression from MCI to ADD at four years follow-up, the small number of participants implies imprecision of sensitivity and specificity estimates. We cannot make any recommendation regarding the routine use of 18F-florbetaben in clinical practice based on one single study with 45 participants. 18F-florbetaben has high financial costs, therefore, clearly demonstrating its DTA and standardising the process of the 18F-florbetaben modality are important prior to its wider use.

Using quality assessment tools to critically appraise ageing research: a guide for clinicians

Evidence based medicine tells us that we should not accept published research at face value. Even research from established teams published in the highest impact journals can have methodological flaws, biases and limited generalisability. The critical appraisal of research studies can seem daunting, but tools are available to make the process easier for the non-specialist. Understanding the language and process of quality assessment is essential when considering or conducting research, and is also valuable for all clinicians who use published research to inform their clinical practice.We present a review written specifically for the practising geriatrician. This considers how quality is defined in relation to the methodological conduct and reporting of research. Having established why quality assessment is important, we present and critique tools which are available to standardise quality assessment. We consider five study designs: RCTs, non-randomised studies, observational studies, systematic reviews and diagnostic test accuracy studies. Quality assessment for each of these study designs is illustrated with an example of published cognitive research. The practical applications of the tools are highlighted, with guidance on their strengths and limitations. We signpost educational resources and offer specific advice for use of these tools.We hope that all geriatricians become comfortable with critical appraisal of published research and that use of the tools described in this review - along with awareness of their strengths and limitations - become a part of teaching, journal clubs and practice.

Big data and data repurposing - using existing data to answer new questions in vascular dementia research

INTRODUCTION

Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD.

METHODS

We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group's experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015).

RESULTS

We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach.

CONCLUSIONS

There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use.

Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) for the early diagnosis of dementia across a variety of healthcare settings

BACKGROUND

The Informant Questionnaire for Cognitive Decline in the Elderly (IQCODE) is a structured interview based on informant responses that is used to assess for possible dementia. IQCODE has been used for retrospective or contemporaneous assessment of cognitive decline. There is considerable interest in tests that may identify those at future risk of developing dementia. Assessing a population free of dementia for the prospective development of dementia is an approach often used in studies of dementia biomarkers. In theory, questionnaire-based assessments, such as IQCODE, could be used in a similar way, assessing for dementia that is diagnosed on a later (delayed) assessment.

OBJECTIVES

To determine the diagnostic accuracy of IQCODE in a population free from dementia for the delayed diagnosis of dementia (test accuracy with delayed verification study design).

SEARCH METHODS

We searched these sources on 16 January 2016: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE Ovid SP, Embase Ovid SP, PsycINFO Ovid SP, BIOSIS Previews on Thomson Reuters Web of Science, Web of Science Core Collection (includes Conference Proceedings Citation Index) on Thomson Reuters Web of Science, CINAHL EBSCOhost, and LILACS BIREME. We also searched sources specific to diagnostic test accuracy: MEDION (Universities of Maastricht and Leuven); DARE (Database of Abstracts of Reviews of Effects, in the Cochrane Library); HTA Database (Health Technology Assessment Database, in the Cochrane Library), and ARIF (Birmingham University). We checked reference lists of included studies and reviews, used searches of included studies in PubMed to track related articles, and contacted research groups conducting work on IQCODE for dementia diagnosis to try to find additional studies. We developed a sensitive search strategy; search terms were designed to cover key concepts using several different approaches run in parallel, and included terms relating to cognitive tests, cognitive screening, and dementia. We used standardised database subject headings, such as MeSH terms (in MEDLINE) and other standardised headings (controlled vocabulary) in other databases, as appropriate.

SELECTION CRITERIA

We selected studies that included a population free from dementia at baseline, who were assessed with the IQCODE and subsequently assessed for the development of dementia over time. The implication was that at the time of testing, the individual had a cognitive problem sufficient to result in an abnormal IQCODE score (defined by the study authors), but not yet meeting dementia diagnostic criteria.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches, and reviewed abstracts of all potentially relevant studies. Two assessors independently checked the full papers for eligibility and extracted data. We determined quality assessment (risk of bias and applicability) using the QUADAS-2 tool, and reported quality using the STARDdem tool.

MAIN RESULTS

From 85 papers describing IQCODE, we included three papers, representing data from 626 individuals. Of this total, 22% (N = 135/626) were excluded because of prevalent dementia. There was substantial attrition; 47% (N = 295) of the study population received reference standard assessment at first follow-up (three to six months) and 28% (N = 174) received reference standard assessment at final follow-up (one to three years). Prevalence of dementia ranged from 12% to 26% at first follow-up and 16% to 35% at final follow-up.The three studies were considered to be too heterogenous to combine, so we did not perform meta-analyses to describe summary estimates of interest. Included patients were poststroke (two papers) and hip fracture (one paper). The IQCODE was used at three thresholds of positivity (higher than 3.0, higher than 3.12 and higher than 3.3) to predict those at risk of a future diagnosis of dementia. Using a cut-off of 3.0, IQCODE had a sensitivity of 0.75 (95%CI 0.51 to 0.91) and a specificity of 0.46 (95%CI 0.34 to 0.59) at one year following stroke. Using a cut-off of 3.12, the IQCODE had a sensitivity of 0.80 (95%CI 0.44 to 0.97) and specificity of 0.53 (95C%CI 0.41 to 0.65) for the clinical diagnosis of dementia at six months after hip fracture. Using a cut-off of 3.3, the IQCODE had a sensitivity of 0.84 (95%CI 0.68 to 0.94) and a specificity of 0.87 (95%CI 0.76 to 0.94) for the clinical diagnosis of dementia at one year after stroke.In generaI, the IQCODE was sensitive for identification of those who would develop dementia, but lacked specificity. Methods for both excluding prevalent dementia at baseline and assessing for the development of dementia were varied, and had the potential to introduce bias.

AUTHORS' CONCLUSIONS

Included studies were heterogenous, recruited from specialist settings, and had potential biases. The studies identified did not allow us to make specific recommendations on the use of the IQCODE for the future diagnosis of dementia in clinical practice. The included studies highlighted the challenges of delayed verification dementia research, with issues around prevalent dementia assessment, loss to follow-up over time, and test non-completion potentially limiting the studies. Future research should recognise these issues and have explicit protocols for dealing with them.

An Investigation of the Utility of the Addenbrooke's Cognitive Examination III in the Early Detection of Dementia in Memory Clinic Patients Aged over 75 Years

BACKGROUND/AIMS

To examine the validity of Addenbrooke's Cognitive Examination III (ACE-III) in detecting early dementia in UK memory clinic patients aged 75-85 years.

METHODS

The ACE-III was administered to 59 patients prior to diagnosis. The extent to which scores predicted the membership of the dementia or no-dementia group was explored using receiver operating characteristic curve analysis and other parameters of diagnostic performance. Thirty-three participants (55.9%) were diagnosed with dementia (Alzheimer's disease = 56.3%, Alzheimer's disease with cerebrovascular disease = 31.3%, and vascular dementia = 12.5%).

RESULTS

The optimal cut-off for detecting dementia was 81/100 (scores <81 indicating dementia with a sensitivity of 0.79, a specificity of 0.96, and a positive predictive value of 0.96), with superiority over published cut-offs (88/100 and 82/100) at medium and lower prevalence rates. The number of years of full-time education had a significant positive relationship to total ACE-III scores (r = 0.697, p < 0.001) for the no-dementia group. Exploratory analysis indicated that optimal cut-offs were different for higher versus lower education groups.

CONCLUSIONS

The ACE-III has excellent accuracy for the detection of dementia in day-to-day clinical practice. Lower cut-offs than those specified in the index paper, and the consideration of the patients' years of full-time education may be necessary for optimal diagnostic performance.

Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations

BACKGROUND

The Mini Mental State Examination (MMSE) is a cognitive test that is commonly used as part of the evaluation for possible dementia.

OBJECTIVES

To determine the diagnostic accuracy of the Mini-Mental State Examination (MMSE) at various cut points for dementia in people aged 65 years and over in community and primary care settings who had not undergone prior testing for dementia.

SEARCH METHODS

We searched the specialised register of the Cochrane Dementia and Cognitive Improvement Group, MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), LILACS (BIREME), ALOIS, BIOSIS previews (Thomson Reuters Web of Science), and Web of Science Core Collection, including the Science Citation Index and the Conference Proceedings Citation Index (Thomson Reuters Web of Science). We also searched specialised sources of diagnostic test accuracy studies and reviews: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). We attempted to locate possibly relevant but unpublished data by contacting researchers in this field. We first performed the searches in November 2012 and then fully updated them in May 2014. We did not apply any language or date restrictions to the electronic searches, and we did not use any methodological filters as a method to restrict the search overall.

SELECTION CRITERIA

We included studies that compared the 11-item (maximum score 30) MMSE test (at any cut point) in people who had not undergone prior testing versus a commonly accepted clinical reference standard for all-cause dementia and subtypes (Alzheimer disease dementia, Lewy body dementia, vascular dementia, frontotemporal dementia). Clinical diagnosis included all-cause (unspecified) dementia, as defined by any version of the Diagnostic and Statistical Manual of Mental Disorders (DSM); International Classification of Diseases (ICD) and the Clinical Dementia Rating.

DATA COLLECTION AND ANALYSIS

At least three authors screened all citations.Two authors handled data extraction and quality assessment. We performed meta-analysis using the hierarchical summary receiver-operator curves (HSROC) method and the bivariate method.

MAIN RESULTS

We retrieved 24,310 citations after removal of duplicates. We reviewed the full text of 317 full-text articles and finally included 70 records, referring to 48 studies, in our synthesis. We were able to perform meta-analysis on 28 studies in the community setting (44 articles) and on 6 studies in primary care (8 articles), but we could not extract usable 2 x 2 data for the remaining 14 community studies, which we did not include in the meta-analysis. All of the studies in the community were in asymptomatic people, whereas two of the six studies in primary care were conducted in people who had symptoms of possible dementia. We judged two studies to be at high risk of bias in the patient selection domain, three studies to be at high risk of bias in the index test domain and nine studies to be at high risk of bias regarding flow and timing. We assessed most studies as being applicable to the review question though we had concerns about selection of participants in six studies and target condition in one study.The accuracy of the MMSE for diagnosing dementia was reported at 18 cut points in the community (MMSE score 10, 14-30 inclusive) and 10 cut points in primary care (MMSE score 17-26 inclusive). The total number of participants in studies included in the meta-analyses ranged from 37 to 2727, median 314 (interquartile range (IQR) 160 to 647). In the community, the pooled accuracy at a cut point of 24 (15 studies) was sensitivity 0.85 (95% confidence interval (CI) 0.74 to 0.92), specificity 0.90 (95% CI 0.82 to 0.95); at a cut point of 25 (10 studies), sensitivity 0.87 (95% CI 0.78 to 0.93), specificity 0.82 (95% CI 0.65 to 0.92); and in seven studies that adjusted accuracy estimates for level of education, sensitivity 0.97 (95% CI 0.83 to 1.00), specificity 0.70 (95% CI 0.50 to 0.85). There was insufficient data to evaluate the accuracy of the MMSE for diagnosing dementia subtypes.We could not estimate summary diagnostic accuracy in primary care due to insufficient data.

AUTHORS' CONCLUSIONS

The MMSE contributes to a diagnosis of dementia in low prevalence settings, but should not be used in isolation to confirm or exclude disease. We recommend that future work evaluates the diagnostic accuracy of tests in the context of the diagnostic pathway experienced by the patient and that investigators report how undergoing the MMSE changes patient-relevant outcomes.

Associations with publication and assessing publication bias in dementia diagnostic test accuracy studies

OBJECTIVE

Biomarkers are of increasing interest in dementia research. Studies describing favourable accuracy of various dementia tests have influenced research, guidelines and diagnostic criteria. Publication bias is known to compromise reports on efficacy of therapeutic interventions. Traditional methods of quantifying publication bias are not suited to reviews of diagnostic tests. We aimed to describe rates and predictors of publication of dementia test accuracy studies presented at scientific meetings.

METHODS

We chose three exemplar scientific meetings from 2009. Two independent researchers assessed conference proceedings and selected all abstracts relating to dementia diagnostics. We recorded basic descriptors and dichotomised results as 'positive' or 'neutral'. We assessed publication status using electronic literature databases and contacting lead authors. We described univariate and multivariate predictors of publication status using logistic regression modelling.

RESULTS

From n = 2257 abstracts, we identified n = 250 (11%) abstracts relating to dementia diagnostics. The majority n = 209 (84%) reported positive results. Only 97 (39%) of these studies are published. Univariate predictors of publication status included positive result (p = 0.042), North American or European authors (p = 0.047), higher number of participants (p = 0.008) and use of a 'biomarker' test (p = 0.035). On multivariate analysis, only increasing number of participants was independently associated with publication (p = 0.034).

CONCLUSIONS

Our strategy did not prove or disprove a publication bias effect in dementia test accuracy studies. The substantial proportion of 'positive' studies may point to a downstream 'submission bias' effect on decision to submit data to meetings. Modest rate of publication of dementia test accuracy studies is concerning, and publication bias remains possible.

Montreal Cognitive Assessment for the diagnosis of Alzheimer's disease and other dementias

BACKGROUND

Dementia is a progressive syndrome of global cognitive impairment with significant health and social care costs. Global prevalence is projected to increase, particularly in resource-limited settings. Recent policy changes in Western countries to increase detection mandates a careful examination of the diagnostic accuracy of neuropsychological tests for dementia.

OBJECTIVES

To determine the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) at various thresholds for dementia and its subtypes.

SEARCH METHODS

We searched MEDLINE, EMBASE, BIOSIS Previews, Science Citation Index, PsycINFO and LILACS databases to August 2012. In addition, we searched specialised sources containing diagnostic studies and reviews, including MEDION (Meta-analyses van Diagnostisch Onderzoek), DARE (Database of Abstracts of Reviews of Effects), HTA (Health Technology Assessment Database), ARIF (Aggressive Research Intelligence Facility) and C-EBLM (International Federation of Clinical Chemistry and Laboratory Medicine Committee for Evidence-based Laboratory Medicine) databases. We also searched ALOIS (Cochrane Dementia and Cognitive Improvement Group specialized register of diagnostic and intervention studies). We identified further relevant studies from the PubMed 'related articles' feature and by tracking key studies in Science Citation Index and Scopus. We also searched for relevant grey literature from the Web of Science Core Collection, including Science Citation Index and Conference Proceedings Citation Index (Thomson Reuters Web of Science), PhD theses and contacted researchers with potential relevant data.

SELECTION CRITERIA

Cross-sectional designs where all participants were recruited from the same sample were sought; case-control studies were excluded due to high chance of bias. We searched for studies from memory clinics, hospital clinics, primary care and community populations. We excluded studies of early onset dementia, dementia from a secondary cause, or studies where participants were selected on the basis of a specific disease type such as Parkinson's disease or specific settings such as nursing homes.

DATA COLLECTION AND ANALYSIS

We extracted dementia study prevalence and dichotomised test positive/test negative results with thresholds used to diagnose dementia. This allowed calculation of sensitivity and specificity if not already reported in the study. Study authors were contacted where there was insufficient information to complete the 2x2 tables. We performed quality assessment according to the QUADAS-2 criteria.Methodological variation in selected studies precluded quantitative meta-analysis, therefore results from individual studies were presented with a narrative synthesis.

MAIN RESULTS

Seven studies were selected: three in memory clinics, two in hospital clinics, none in primary care and two in population-derived samples. There were 9422 participants in total, but most of studies recruited only small samples, with only one having more than 350 participants. The prevalence of dementia was 22% to 54% in the clinic-based studies, and 5% to 10% in population samples. In the four studies that used the recommended threshold score of 26 or over indicating normal cognition, the MoCA had high sensitivity of 0.94 or more but low specificity of 0.60 or less.

AUTHORS' CONCLUSIONS

The overall quality and quantity of information is insufficient to make recommendations on the clinical utility of MoCA for detecting dementia in different settings. Further studies that do not recruit participants based on diagnoses already present (case-control design) but apply diagnostic tests and reference standards prospectively are required. Methodological clarity could be improved in subsequent DTA studies of MoCA by reporting findings using recommended guidelines (e.g. STARDdem). Thresholds lower than 26 are likely to be more useful for optimal diagnostic accuracy of MoCA in dementia, but this requires confirmation in further studies.