Anticholinergic deprescribing interventions for reducing risk of cognitive decline or dementia in older adults with and without prior cognitive impairment.

BACKGROUND: Anticholinergics are medications that block the action of acetylcholine in the central or peripheral nervous system. Medications with anticholinergic properties are commonly prescribed to older adults. The cumulative anticholinergic effect of all the medications a person takes is referred to as the anticholinergic burden. A high anticholinergic burden may cause cognitive impairment in people who are otherwise cognitively healthy, or cause further cognitive decline in people with pre-existing cognitive problems. Reducing anticholinergic burden through deprescribing interventions may help to prevent onset of cognitive impairment or slow the rate of cognitive decline. OBJECTIVES: Primary objective • To assess the efficacy and safety of anticholinergic medication reduction interventions for improving cognitive outcomes in cognitively healthy older adults and older adults with pre-existing cognitive issues. Secondary Objectives • To compare the effectiveness of different types of reduction interventions (e.g. pharmacist-led versus general practitioner-led, educational versus audit and feedback) for reducing overall anticholinergic burden. • To establish optimal duration of anticholinergic reduction interventions, sustainability, and lessons learnt for upscaling • To compare results according to differing anticholinergic scales used in medication reduction intervention trials • To assess the efficacy of anticholinergic medication reduction interventions for improving other clinical outcomes, including mortality, quality of life, clinical global impression, physical function, institutionalisation, falls, cardiovascular diseases, and neurobehavioral outcomes. SEARCH METHODS: We searched CENTRAL on 22 December 2022, and we searched MEDLINE, Embase, and three other databases from inception to 1 November 2022. SELECTION CRITERIA: We included randomised controlled trials (RCTs) of interventions that aimed to reduce anticholinergic burden in older people and that investigated cognitive outcomes. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed studies for inclusion, extracted data, and assessed the risk of bias of included studies. The data were not suitable for meta-analysis, so we summarised them narratively. We used GRADE methods to rate our confidence in the review results. MAIN RESULTS: We included three trials with a total of 299 participants. All three trials were conducted in a cognitively mixed population (some cognitively healthy participants, some participants with dementia). Outcomes were assessed after one to three months. One trial reported significantly improved performance on the Digit Symbol Substitution Test (DSST) in the intervention group (treatment difference 0.70, 95% confidence interval (CI) 0.11 to 1.30), although there was no difference between the groups in the proportion of participants with reduced anticholinergic burden. Two trials successfully reduced anticholinergic burden in the intervention group. Of these, one reported no significant difference between the intervention versus control in terms of their effect on cognitive performance measured by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) immediate recall (mean between-group difference 0.54, 95% CI -0.91 to 2.05), CERAD delayed recall (mean between-group difference -0.23, 95% CI-0.85 to 0.38), CERAD recognition (mean between-group difference 0.77, 95% CI -0.39 to 1.94), and Mini-Mental State Examination (mean between-group difference 0.39, 95% CI -0.96 to 1.75). The other trial reported a significant correlation between anticholinergic burden and a test of working memory after the intervention (which suggested reducing the burden improved performance), but reported no effect on multiple other cognitive measures. In GRADE terms, the results were of very low certainty. There were no reported between-group differences for any other clinical outcome we investigated. It was not possible to investigate differences according to type of reduction intervention or type of anticholinergic scale, to measure the sustainability of interventions, or to establish lessons learnt for upscaling. No trials investigated safety outcomes. AUTHORS' CONCLUSIONS: There is insufficient evidence to reach any conclusions on the effects of anticholinergic burden reduction interventions on cognitive outcomes in older adults with or without prior cognitive impairment. The evidence from RCTs was of very low certainty so cannot support or refute the hypothesis that actively reducing or stopping prescription of medications with anticholinergic properties can improve cognitive outcomes in older people. There is no evidence from RCTs that anticholinergic burden reduction interventions improve other clinical outcomes such as mortality, quality of life, clinical global impression, physical function, institutionalisation, falls, cardiovascular diseases, or neurobehavioral outcomes. Larger RCTs investigating long-term outcomes are needed. Future RCTs should also investigate potential benefits of anticholinergic reduction interventions in cognitively healthy populations and cognitively impaired populations separately.

Rapid reviews methods series: Guidance on team considerations, study selection, data extraction and risk of bias assessment.

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group (RRMG). Rapid reviews (RRs) use modified systematic review (SR) methods to accelerate the review process while maintaining systematic, transparent and reproducible methods to ensure integrity. This paper addresses considerations around the acceleration of study selection, data extraction and risk of bias (RoB) assessment in RRs. If a RR is being undertaken, review teams should consider using one or more of the following methodological shortcuts: screen a proportion (eg, 20%) of records dually at the title/abstract level until sufficient reviewer agreement is achieved, then proceed with single-reviewer screening; use the same approach for full-text screening; conduct single-data extraction only on the most relevant data points and conduct single-RoB assessment on the most important outcomes, with a second person verifying the data extraction and RoB assessment for completeness and correctness. Where available, extract data and RoB assessments from an existing SR that meets the eligibility criteria.

Anticholinergic burden for prediction of cognitive decline or neuropsychiatric symptoms in older adults with mild cognitive impairment or dementia.

BACKGROUND: Medications with anticholinergic properties are commonly prescribed to older adults with a pre-existing diagnosis of dementia or cognitive impairment. The cumulative anticholinergic effect of all the medications a person takes is referred to as the anticholinergic burden because of its potential to cause adverse effects. It is possible that a high anticholinergic burden may be a risk factor for further cognitive decline or neuropsychiatric disturbances in people with dementia. Neuropsychiatric disturbances are the most frequent complication of dementia that require hospitalisation, accounting for almost half of admissions; hence, identification of modifiable prognostic factors for these outcomes is crucial. There are various scales available to measure anticholinergic burden but agreement between them is often poor. OBJECTIVES: Our primary objective was to assess whether anticholinergic burden, as defined at the level of each individual scale, was a prognostic factor for further cognitive decline or neuropsychiatric disturbances in older adults with pre-existing diagnoses of dementia or cognitive impairment. Our secondary objective was to investigate whether anticholinergic burden was a prognostic factor for other adverse clinical outcomes, including mortality, impaired physical function, and institutionalisation. SEARCH METHODS: We searched these databases from inception to 29 November 2021: MEDLINE OvidSP, Embase OvidSP, PsycINFO OvidSP, CINAHL EBSCOhost, and ISI Web of Science Core Collection on ISI Web of Science. SELECTION CRITERIA: We included prospective and retrospective longitudinal cohort and case-control observational studies, with a minimum of one-month follow-up, which examined the association between an anticholinergic burden measurement scale and the above stated adverse clinical outcomes, in older adults with pre-existing diagnoses of dementia or cognitive impairment.   DATA COLLECTION AND ANALYSIS: Two review authors independently assessed studies for inclusion, and undertook data extraction, risk of bias assessment, and GRADE assessment. We summarised risk associations between anticholinergic burden and all clinical outcomes in a narrative fashion. We also evaluated the risk association between anticholinergic burden and mortality using a random-effects meta-analysis.  We established adjusted pooled rates for the anticholinergic cognitive burden (ACB) scale; then, as an exploratory analysis, established pooled rates on the prespecified association across scales.  MAIN RESULTS: We identified 18 studies that met our inclusion criteria (102,684 older adults). Anticholinergic burden was measured using five distinct measurement scales: 12 studies used the ACB scale; 3 studies used the Anticholinergic Risk Scale (ARS); 1 study used the Anticholinergic Drug Scale (ADS); 1 study used the Anticholinergic Effect on Cognition (AEC) Scale; and 2 studies used a list developed by Tune and Egeli.  Risk associations between anticholinergic burden and adverse clinical outcomes were highly heterogenous. Four out of 10 (40%) studies reported a significantly increased risk of greater long-term cognitive decline for participants with an anticholinergic burden compared to participants with no or minimal anticholinergic burden. No studies investigated neuropsychiatric disturbance outcomes. One out of four studies (25%) reported a significant association with reduced physical function for participants with an anticholinergic burden versus participants with no or minimal anticholinergic burden. No study (out of one investigating study) reported a significant association between anticholinergic burden and risk of institutionalisation. Six out of 10 studies (60%) found a significantly increased risk of mortality for those with an anticholinergic burden compared to those with no or minimal anticholinergic burden. Pooled analysis of adjusted mortality hazard ratios (HR) measured anticholinergic burden with the ACB scale, and suggested a significantly increased risk of death for those with a high ACB score relative to those with no or minimal ACB scores (HR 1.153, 95% confidence interval (CI) 1.030 to 1.292; 4 studies, 48,663 participants). An exploratory pooled analysis of adjusted mortality HRs across anticholinergic burden scales also suggested a significantly increased risk of death for those with a high anticholinergic burden (HR 1.102, 95% CI 1.044 to 1.163; 6 studies, 68,381 participants).   Overall GRADE evaluation of results found low- or very low-certainty evidence for all outcomes.  AUTHORS' CONCLUSIONS: There is low-certainty evidence that older adults with dementia or cognitive impairment who have a significant anticholinergic burden may be at increased risk of death. No firm conclusions can be drawn for risk of accelerated cognitive decline, neuropsychiatric disturbances, decline in physical function, or institutionalisation.

Clinical judgement by primary care physicians for the diagnosis of all-cause dementia or cognitive impairment in symptomatic people.

BACKGROUND: In primary care, general practitioners (GPs) unavoidably reach a clinical judgement about a patient as part of their encounter with patients, and so clinical judgement can be an important part of the diagnostic evaluation. Typically clinical decision making about what to do next for a patient incorporates clinical judgement about the diagnosis with severity of symptoms and patient factors, such as their ideas and expectations for treatment. When evaluating patients for dementia, many GPs report using their own judgement to evaluate cognition, using information that is immediately available at the point of care, to decide whether someone has or does not have dementia, rather than more formal tests. OBJECTIVES: To determine the diagnostic accuracy of GPs' clinical judgement for diagnosing cognitive impairment and dementia in symptomatic people presenting to primary care. To investigate the heterogeneity of test accuracy in the included studies. SEARCH METHODS: We searched MEDLINE (Ovid SP), Embase (Ovid SP), PsycINFO (Ovid SP), Web of Science Core Collection (ISI Web of Science), and LILACs (BIREME) on 16 September 2021. SELECTION CRITERIA: We selected cross-sectional and cohort studies from primary care where clinical judgement was determined by a GP either prospectively (after consulting with a patient who has presented to a specific encounter with the doctor) or retrospectively (based on knowledge of the patient and review of the medical notes, but not relating to a specific encounter with the patient). The target conditions were dementia and cognitive impairment (mild cognitive impairment and dementia) and we included studies with any appropriate reference standard such as the Diagnostic and Statistical Manual of Mental Disorders (DSM), International Classification of Diseases (ICD), aetiological definitions, or expert clinical diagnosis. DATA COLLECTION AND ANALYSIS: Two review authors screened titles and abstracts for relevant articles and extracted data separately with differences resolved by consensus discussion. We used QUADAS-2 to evaluate the risk of bias and concerns about applicability in each study using anchoring statements. We performed meta-analysis using the bivariate method. MAIN RESULTS: We identified 18,202 potentially relevant articles, of which 12,427 remained after de-duplication. We assessed 57 full-text articles and extracted data on 11 studies (17 papers), of which 10 studies had quantitative data. We included eight studies in the meta-analysis for the target condition dementia and four studies for the target condition cognitive impairment. Most studies were at low risk of bias as assessed with the QUADAS-2 tool, except for the flow and timing domain where four studies were at high risk of bias, and the reference standard domain where two studies were at high risk of bias. Most studies had low concern about applicability to the review question in all QUADAS-2 domains. Average age ranged from 73 years to 83 years (weighted average 77 years). The percentage of female participants in studies ranged from 47% to 100%. The percentage of people with a final diagnosis of dementia was between 2% and 56% across studies (a weighted average of 21%). For the target condition dementia, in individual studies sensitivity ranged from 34% to 91% and specificity ranged from 58% to 99%. In the meta-analysis for dementia as the target condition, in eight studies in which a total of 826 of 2790 participants had dementia, the summary diagnostic accuracy of clinical judgement of general practitioners was sensitivity 58% (95% confidence interval (CI) 43% to 72%), specificity 89% (95% CI 79% to 95%), positive likelihood ratio 5.3 (95% CI 2.4 to 8.2), and negative likelihood ratio 0.47 (95% CI 0.33 to 0.61). For the target condition cognitive impairment, in individual studies sensitivity ranged from 58% to 97% and specificity ranged from 40% to 88%. The summary diagnostic accuracy of clinical judgement of general practitioners in four studies in which a total of 594 of 1497 participants had cognitive impairment was sensitivity 84% (95% CI 60% to 95%), specificity 73% (95% CI 50% to 88%), positive likelihood ratio 3.1 (95% CI 1.4 to 4.7), and negative likelihood ratio 0.23 (95% CI 0.06 to 0.40). It was impossible to draw firm conclusions in the analysis of heterogeneity because there were small numbers of studies. For specificity we found the data were compatible with studies that used ICD-10, or applied retrospective judgement, had higher reported specificity compared to studies with DSM definitions or using prospective judgement. In contrast for sensitivity, we found studies that used a prospective index test may have had higher sensitivity than studies that used a retrospective index test. AUTHORS' CONCLUSIONS: Clinical judgement of GPs is more specific than sensitive for the diagnosis of dementia. It would be necessary to use additional tests to confirm the diagnosis for either target condition, or to confirm the absence of the target conditions, but clinical judgement may inform the choice of further testing. Many people who a GP judges as having dementia will have the condition. People with false negative diagnoses are likely to have less severe disease and some could be identified by using more formal testing in people who GPs judge as not having dementia. Some false positives may require similar practical support to those with dementia, but some - such as some people with depression - may suffer delayed intervention for an alternative treatable pathology.

Crowdsourcing the identification of studies for COVID-19-related Cochrane Rapid Reviews.

BACKGROUND: Utilisation of crowdsourcing within evidence synthesis has increased over the last decade. Crowdsourcing platform Cochrane Crowd has engaged a global community of 22,000 people from 170 countries. The COVID-19 pandemic presented an opportunity to engage the community and keep up with the exponential output of COVID-19 research. AIMS: To test whether a crowd could accurately assess study eligibility for reviews under time constraints. OUTCOME MEASURES: time taken to complete each task, time to produce required training modules, crowd sensitivity, specificity and crowd consensus. METHODS: We created four crowd tasks, corresponding to four Cochrane COVID-19 Rapid Reviews. The search results of each were uploaded and an interactive training module was developed for each task. Contributors who had participated in another COVID-19 task were invited to participate. Each task was live for 48-h. The final inclusion and exclusion decisions made by the core author team were used as the reference standard. RESULTS: Across all four reviews 14,299 records were screened by 101 crowd contributors. The crowd completed each screening task within 48-h for three reviews and in 52 h for one. Sensitivity ranged from 94% to 100%. Four studies, out of a total of 109, were incorrectly rejected by the crowd. However, their absence ultimately would not have altered the conclusions of the reviews. Crowd consensus ranged from 71% to 92% across the four reviews. CONCLUSION: Crowdsourcing can play a valuable role in study identification and offers willing contributors the opportunity to help identify COVID-19 research for rapid evidence syntheses.

The People's Trial: supporting the public's understanding of randomised trials.

BACKGROUND: Randomised trials are considered the gold standard in providing robust evidence on the effectiveness of interventions. However, there are relatively few initiatives to help increase public understanding of what randomised trials are and why they are important. This limits the overall acceptance of and public participation in clinical trials. The People's Trial aims to help the public learn about randomised trials, to understand why they matter, and to be better equipped to think critically about health claims by actively involving them in all aspects of trial design. This was done by involving the public in the design, conduct, and dissemination of a randomised trial. METHODS: Using a reflexive approach, we describe the processes of development, conduct, and dissemination of The People's Trial. RESULTS: Over 3000 members of the public, from 72 countries, participated in The People's Trial. Through a series of online surveys, the public designed a trial called The Reading Trial. They chose the question the trial would try to answer and decided the components of the trial question. In December 2019, 991 participants were recruited to a trial to answer the question identified and prioritised by the public, i.e. 'Does reading a book in bed make a difference to sleep in comparison with not reading a book in bed?' We report the processes of The People's Trial in seven phases, paralleling the steps of a randomised trial, i.e. question identification and prioritisation, recruitment, randomisation, trial conduct, data analysis, and sharing of findings. We describe the decisions we made, the processes we used, the challenges we encountered, and the lessons we learned. CONCLUSION: The People's Trial involved the public successfully in the design, conduct, and dissemination of a randomised trial demonstrating the potential for such initiatives to help the public learn about randomised trials, to understand why they matter, and to be better equipped to think critically about health claims. TRIAL REGISTRATION: ClinicalTrials.gov NCT04185818 . Registered on 4 December 2019.

Machine learning reduced workload for the Cochrane COVID-19 Study Register: development and evaluation of the Cochrane COVID-19 Study Classifier.

BACKGROUND: This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies. METHODS: A ML classifier for retrieving COVID-19 research studies (the 'Cochrane COVID-19 Study Classifier') was developed using a data set of title-abstract records 'included' in, or 'excluded' from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records 'included' in, or 'excluded' from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records 'included' in, or 'excluded' from, the CCSR between the 4th and 19th of January 2021. RESULTS: The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were 'included' in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were 'included' in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded). CONCLUSIONS: The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register.

Mini-Cog for the detection of dementia within a community setting.

BACKGROUND: Alzheimer's disease and related forms of dementia are becoming increasingly prevalent with the aging of many populations. The diagnosis of Alzheimer's disease relies on tests to evaluate cognition and discriminate between individuals with dementia and those without dementia. The Mini-Cog is a brief, cognitive screening test that is frequently used to evaluate cognition in older adults in various settings. OBJECTIVES: The primary objective of this review was to determine the accuracy of the Mini-Cog for detecting dementia in a community setting. Secondary objectives included investigations of the heterogeneity of test accuracy in the included studies and potential sources of heterogeneity. These potential sources of heterogeneity included 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. Overall, the goals of this review were to determine if the Mini-Cog is a cognitive screening test that could be recommended to screen for cognitive impairment in community settings. SEARCH METHODS: We searched MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (Ovid SP), Science Citation Index (Web of Science), BIOSIS previews (Web of Science), LILACS (BIREME), and the Cochrane Dementia Group's developing register of diagnostic test accuracy studies to March 2013. We used citation tracking (using the database's 'related articles' feature, where available) as an additional search method and contacted authors of eligible studies for unpublished data. SELECTION CRITERIA: We included all cross-sectional studies that utilized the Mini-Cog as an index test for the diagnosis of dementia when compared to a reference standard diagnosis of dementia using standardized dementia diagnostic criteria. For the current review we only included studies that were conducted on samples from community settings, and excluded studies that were conducted in primary care or secondary care settings. We considered studies to be conducted in a community setting where participants were sampled from the general population. DATA COLLECTION AND ANALYSIS: Information from studies meeting the inclusion criteria were extracted including information on the characteristics of participants in the studies. The quality of the studies was assessed using the QUADAS-2 criteria and summarized using risk of bias applicability and summary graphs. We extracted information on the diagnostic test accuracy of studies including the sensitivity, specificity, and 95% confidence intervals of these measures and summarized the findings using forest plots. Study specific sensitivities and specificities were also plotted in receiver operating curve space. MAIN RESULTS: Three studies met the inclusion criteria, with a total of 1620 participants. The sensitivities of the Mini-Cog in the individual studies were reported as 0.99, 0.76 and 0.99. The specificity of the Mini-Cog varied in the individual studies and was 0.93, 0.89 and 0.83. There was clinical and methodological heterogeneity between the studies which precluded a pooled meta-analysis of the results. Methodological limitations were present in all the studies introducing potential sources of bias, specifically with respect to the methods for participant selection. AUTHORS' CONCLUSIONS: There are currently few studies assessing the diagnostic test accuracy of the Mini-Cog in community settings. The limited number of studies and the methodological limitations that are present in the current studies make it difficult to provide recommendations for or against the use of the Mini-Cog as a cognitive screening test in community settings. Additional well-designed studies comparing the Mini-Cog to other brief cognitive screening tests are required in order to determine the accuracy and utility of the Mini-Cog in community based settings.

Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) for the detection of dementia within community dwelling populations.

BACKGROUND: Various tools exist for initial assessment of possible dementia with no consensus on the optimal assessment method. Instruments that use collateral sources to assess change in cognitive function over time may have particular utility. The most commonly used informant dementia assessment is the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). A synthesis of the available data regarding IQCODE accuracy will help inform cognitive assessment strategies for clinical practice, research and policy. OBJECTIVES: Our primary obective was to determine the accuracy of the informant-based questionnaire IQCODE for detection of dementia within community dwelling populations. Our secondary objective was to describe the effect of heterogeneity on the summary estimates. We were particularly interested in the traditional 26-item scale versus the 16-item short form; and language of administration. We explored the effect of varying the threshold IQCODE score used to define 'test positivity'. SEARCH METHODS: We searched the following sources on 28 January 2013: ALOIS (Cochrane Dementia and Cognitive Improvement Group), MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), BIOSIS Previews (ISI Web of Knowledge), Web of Science with Conference Proceedings (ISI Web of Knowledge), LILACS (BIREME). We also searched sources relevant or specific to diagnostic test accuracy: MEDION (Universities of Maastrict and Leuven); DARE (York University); ARIF (Birmingham University). We used sensitive search terms based on MeSH terms and other controlled vocabulary. SELECTION CRITERIA: We selected those studies performed in community settings that used (not necessarily exclusively) the IQCODE to assess for presence of dementia and, where dementia diagnosis was confirmed with clinical assessment. Our intention with limiting the search to a 'community' setting was to include those studies closest to population level assessment. Within our predefined community inclusion criteria, there were relevant papers that fulfilled our definition of community dwelling but represented a selected population, for example stroke survivors. We included these studies but performed sensitivity analyses to assess the effects of these less representative populations on the summary results. DATA COLLECTION AND ANALYSIS: We screened all titles generated by the 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. For quality assessment (risk of bias and applicability) we used the QUADAS 2 tool. We included test accuracy data on the IQCODE used at predefined diagnostic thresholds. Where data allowed, we performed meta-analyses to calculate summary values of sensitivity and specificity with corresponding 95% confidence intervals (CIs). We pre-specified analyses to describe the effect of IQCODE format (traditional or short form) and language of administration for the IQCODE. MAIN RESULTS: From 16,144 citations, 71 papers described IQCODE test accuracy. We included 10 papers (11 independent datasets) representing data from 2644 individuals (n = 379 (14%) with dementia). Using IQCODE cut-offs commonly employed in clinical practice (3.3, 3.4, 3.5, 3.6) the sensitivity and specificity of IQCODE for diagnosis of dementia across the studies were generally above 75%. Taking an IQCODE threshold of 3.3 (or closest available) the sensitivity was 0.80 (95% CI 0.75 to 0.85); specificity was 0.84 (95% CI 0.78 to 0.90); positive likelihood ratio was 5.2 (95% CI 3.7 to 7.5) and the negative likelihood ratio was 0.23 (95% CI 0.19 to 0.29). Comparative analysis suggested no significant difference in the test accuracy of the 16 and 26-item IQCODE tests and no significant difference in test accuracy by language of administration. There was little difference in sensitivity across our predefined diagnostic cut-points. There was substantial heterogeneity in the included studies. Sensitivity analyses removing potentially unrepresentative populations in these studies made little difference to the pooled data estimates. The majority of included papers had potential for bias, particularly around participant selection and sampling. The quality of reporting was suboptimal particularly regarding timing of assessments and descriptors of reproducibility and inter-observer variability. AUTHORS' CONCLUSIONS: Published data suggest that if using the IQCODE for community dwelling older adults, the 16 item IQCODE may be preferable to the traditional scale due to lesser test burden and no obvious difference in accuracy. Although IQCODE test accuracy is in a range that many would consider 'reasonable', in the context of community or population settings the use of the IQCODE alone would result in substantial misdiagnosis and false reassurance. Across the included studies there were issues with heterogeneity, several potential biases and suboptimal reporting quality.

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.

Anticholinergic burden (prognostic factor) for prediction of dementia or cognitive decline in older adults with no known cognitive syndrome.

BACKGROUND: Medications with anticholinergic properties are commonly prescribed to older adults. The cumulative anticholinergic effect of all the medications a person takes is referred to as the 'anticholinergic burden' because of its potential to cause adverse effects. It is possible that high anticholinergic burden may be a risk factor for development of cognitive decline or dementia. There are various scales available to measure anticholinergic burden but agreement between them is often poor. OBJECTIVES: To assess whether anticholinergic burden, as defined at the level of each individual scale, is a prognostic factor for future cognitive decline or dementia in cognitively unimpaired older adults. SEARCH METHODS: We searched the following databases from inception to 24 March 2021: MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), CINAHL (EBSCOhost), and ISI Web of Science Core Collection (ISI Web of Science). SELECTION CRITERIA: We included prospective and retrospective longitudinal cohort and case-control observational studies with a minimum of one year' follow-up that examined the association between an anticholinergic burden measurement scale and future cognitive decline or dementia in cognitively unimpaired older adults. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed studies for inclusion, and undertook data extraction, assessment of risk of bias, and GRADE assessment. We extracted odds ratios (OR) and hazard ratios, with 95% confidence intervals (CI), and linear data on the association between anticholinergic burden and cognitive decline or dementia. We intended to pool each metric separately; however, only OR-based data were suitable for pooling via a random-effects meta-analysis. We initially established adjusted and unadjusted pooled rates for each available anticholinergic scale; then, as an exploratory analysis, established pooled rates on the prespecified association across scales. We examined variability based on severity of anticholinergic burden. MAIN RESULTS: We identified 25 studies that met our inclusion criteria (968,428 older adults). Twenty studies were conducted in the community care setting, two in primary care clinics, and three in secondary care settings. Eight studies (320,906 participants) provided suitable data for meta-analysis. The Anticholinergic Cognitive Burden scale (ACB scale) was the only scale with sufficient data for 'scale-based' meta-analysis. Unadjusted ORs suggested an increased risk for cognitive decline or dementia in older adults with an anticholinergic burden (OR 1.47, 95% CI 1.09 to 1.96) and adjusted ORs similarly suggested an increased risk for anticholinergic burden, defined according to the ACB scale (OR 2.63, 95% CI 1.09 to 6.29). Exploratory analysis combining adjusted ORs across available scales supported these results (OR 2.16, 95% CI 1.38 to 3.38), and there was evidence of variability in risk based on severity of anticholinergic burden (ACB scale 1: OR 2.18, 95% CI 1.11 to 4.29; ACB scale 2: OR 2.71, 95% CI 2.01 to 3.56; ACB scale 3: OR 3.27, 95% CI 1.41 to 7.61); however, overall GRADE evaluation of certainty of the evidence was low. AUTHORS' CONCLUSIONS: There is low-certainty evidence that older adults without cognitive impairment who take medications with anticholinergic effects may be at increased risk of cognitive decline or dementia.

ANTECEDENTES: A los adultos mayores se les prescriben con frecuencia fármacos con propiedades anticolinérgicas. El efecto anticolinérgico acumulado de todos los fármacos que toma una persona se denomina "carga anticolinérgica" por su potencial para causar efectos adversos. Es posible que una alta carga anticolinérgica sea un factor de riesgo para la aparición de un deterioro cognitivo o la demencia. Existen varias escalas para medir la carga anticolinérgica, pero la concordancia entre ellas suele ser mala. OBJETIVOS: Evaluar si la carga anticolinérgica, definida a nivel de cada escala individual, es un factor pronóstico de un futuro deterioro cognitivo o demencia en adultos mayores sin deterioro cognitivo. MÉTODOS DE BÚSQUEDA: Se realizaron búsquedas en las siguientes bases de datos desde su creación hasta el 24 de marzo de 2021: MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), CINAHL (EBSCOhost) e ISI Web of Science Core Collection (ISI Web of Science). CRITERIOS DE SELECCIÓN: Se incluyeron los estudios observacionales de cohortes y de casos y controles longitudinales prospectivos y retrospectivos con un seguimiento mínimo de un año, que examinaron la asociación entre una escala de medición de la carga anticolinérgica y el futuro deterioro cognitivo o demencia en adultos mayores sin deterioro cognitivo. OBTENCIÓN Y ANÁLISIS DE LOS DATOS: Dos autores de la revisión, de forma independiente, evaluaron los estudios para su inclusión y realizaron la extracción de los datos, la evaluación del riesgo de sesgo y la evaluación GRADE. Se extrajeron los odds ratios (OR) y los cociente de riesgos instantáneos, con intervalos de confianza (IC) del 95%, y los datos lineales sobre la asociación entre la carga anticolinérgica y el deterioro cognitivo o la demencia. La intención fue agrupar cada métrica por separado; sin embargo, sólo los datos basados en el OR fueron aptos para agruparlos mediante un metanálisis de efectos aleatorios. Inicialmente se establecieron las tasas agrupadas ajustadas y no ajustadas para cada escala anticolinérgica disponible; luego, como un análisis exploratorio, se establecieron las tasas agrupadas sobre la asociación predeterminada entre las escalas. Se examinó la variabilidad según la intensidad de la carga anticolinérgica. RESULTADOS PRINCIPALES: Se identificaron 25 estudios que cumplían los criterios de inclusión (968 428 adultos mayores). Veinte estudios se realizaron en ámbitos de atención comunitaria, dos en centros de atención primaria y tres en ámbitos de atención secundaria. Ocho estudios (320 906 participantes) proporcionaron datos adecuados para el metanálisis. La escala Anticholinergic Cognitive Burden (escala ACB) fue la única escala con datos suficientes para un metanálisis "basado en la escala". Los OR no ajustados indicaron un aumento en el riesgo de deterioro cognitivo o demencia en los adultos mayores con sobrecarga anticolinérgica (OR 1,47; IC del 95%: 1,09 a 1,96) y los OR ajustados indicaron igualmente un aumento en el riesgo de sobrecarga anticolinérgica, definida según la escala ACB (OR 2,63; IC del 95%: 1,09 a 6,29). El análisis exploratorio que combina los OR ajustados entre las escalas disponibles apoyó estos resultados (OR 2,16; IC del 95%: 1,38 a 3,38) y hubo evidencia de variabilidad en el riesgo según la intensidad de la carga anticolinérgica (1 en escala ACB): OR 2,18; IC del 95%: 1,11 a 4,29; 2 en escala ACB: OR 2,71; IC del 95%: 2,01 a 3,56; 3 en escala ACB: OR 3,27; IC del 95%: 1,41 a 7,61); sin embargo, la evaluación global de la certeza de la evidencia con el método GRADE fue baja. CONCLUSIONES DE LOS AUTORES: Existe evidencia de certeza baja de que los adultos mayores sin deterioro cognitivo que toman fármacos con efectos anticolinérgicos podrían tener un mayor riesgo de deterioro cognitivo o demencia.

Crowdsourcing citation-screening in a mixed-studies systematic review: a feasibility study.

BACKGROUND: Crowdsourcing engages the help of large numbers of people in tasks, activities or projects, usually via the internet. One application of crowdsourcing is the screening of citations for inclusion in a systematic review. There is evidence that a 'Crowd' of non-specialists can reliably identify quantitative studies, such as randomized controlled trials, through the assessment of study titles and abstracts. In this feasibility study, we investigated crowd performance of an online, topic-based citation-screening task, assessing titles and abstracts for inclusion in a single mixed-studies systematic review. METHODS: This study was embedded within a mixed studies systematic review of maternity care, exploring the effects of training healthcare professionals in intrapartum cardiotocography. Citation-screening was undertaken via Cochrane Crowd, an online citizen science platform enabling volunteers to contribute to a range of tasks identifying evidence in health and healthcare. Contributors were recruited from users registered with Cochrane Crowd. Following completion of task-specific online training, the crowd and the review team independently screened 9546 titles and abstracts. The screening task was subsequently repeated with a new crowd following minor changes to the crowd agreement algorithm based on findings from the first screening task. We assessed the crowd decisions against the review team categorizations (the 'gold standard'), measuring sensitivity, specificity, time and task engagement. RESULTS: Seventy-eight crowd contributors completed the first screening task. Sensitivity (the crowd's ability to correctly identify studies included within the review) was 84% (N = 42/50), and specificity (the crowd's ability to correctly identify excluded studies) was 99% (N = 9373/9493). Task completion was 33 h for the crowd and 410 h for the review team; mean time to classify each record was 6.06 s for each crowd participant and 3.96 s for review team members. Replicating this task with 85 new contributors and an altered agreement algorithm found 94% sensitivity (N = 48/50) and 98% specificity (N = 9348/9493). Contributors reported positive experiences of the task. CONCLUSION: It might be feasible to recruit and train a crowd to accurately perform topic-based citation-screening for mixed studies systematic reviews, though resource expended on the necessary customised training required should be factored in. In the face of long review production times, crowd screening may enable a more time-efficient conduct of reviews, with minimal reduction of citation-screening accuracy, but further research is needed.

Plasma and cerebrospinal fluid ABeta42 for the differential diagnosis of Alzheimer's disease dementia in participants diagnosed with any dementia subtype in a specialist care setting.

BACKGROUND: Dementia is a syndrome that comprises many differing pathologies, including Alzheimer's disease dementia (ADD), vascular dementia (VaD) and frontotemporal dementia (FTD). People may benefit from knowing the type of dementia they live with, as this could inform prognosis and may allow for tailored treatment. Beta-amyloid (1-42) (ABeta42) is a protein which decreases in both the plasma and cerebrospinal fluid (CSF) of people living with ADD, when compared to people with no dementia. However, it is not clear if changes in ABeta42 are specific to ADD or if they are also seen in other types of dementia. It is possible that ABeta42 could help differentiate ADD from other dementia subtypes. OBJECTIVES: To determine the accuracy of plasma and CSF ABeta42 for distinguishing ADD from other dementia subtypes in people who meet the criteria for a dementia syndrome. SEARCH METHODS: We searched MEDLINE, and nine other databases up to 18 February 2020. We checked reference lists of any relevant systematic reviews to identify additional studies. SELECTION CRITERIA: We considered cross-sectional studies that differentiated people with ADD from other dementia subtypes. Eligible studies required measurement of participant plasma or CSF ABeta42 levels and clinical assessment for dementia subtype. DATA COLLECTION AND ANALYSIS: Seven review authors working independently screened the titles and abstracts generated by the searches. We collected data on study characteristics and test accuracy. We used the second version of the 'Quality Assessment of Diagnostic Accuracy Studies' (QUADAS-2) tool to assess internal and external validity of results. We extracted data into 2 x 2 tables, cross-tabulating index test results (ABeta42) with the reference standard (diagnostic criteria for each dementia subtype). We performed meta-analyses using bivariate, random-effects models. We calculated pooled estimates of sensitivity, specificity, positive predictive values, positive and negative likelihood ratios, and corresponding 95% confidence intervals (CIs). In the primary analysis, we assessed accuracy of plasma or CSF ABeta42 for distinguishing ADD from other mixed dementia types (non-ADD). We then assessed accuracy of ABeta42 for differentiating ADD from specific dementia types: VaD, FTD, dementia with Lewy bodies (DLB), alcohol-related cognitive disorder (ARCD), Creutzfeldt-Jakob disease (CJD) and normal pressure hydrocephalus (NPH). To determine test-positive cases, we used the ABeta42 thresholds employed in the respective primary studies. We then performed sensitivity analyses restricted to those studies that used common thresholds for ABeta42. MAIN RESULTS: We identified 39 studies (5000 participants) that used CSF ABeta42 levels to differentiate ADD from other subtypes of dementia. No studies of plasma ABeta42 met the inclusion criteria. No studies were rated as low risk of bias across all QUADAS-2 domains. High risk of bias was found predominantly in the domains of patient selection (28 studies) and index test (25 studies). The pooled estimates for differentiating ADD from other dementia subtypes were as follows: ADD from non-ADD: sensitivity 79% (95% CI 0.73 to 0.85), specificity 60% (95% CI 0.52 to 0.67), 13 studies, 1704 participants, 880 participants with ADD; ADD from VaD: sensitivity 79% (95% CI 0.75 to 0.83), specificity 69% (95% CI 0.55 to 0.81), 11 studies, 1151 participants, 941 participants with ADD; ADD from FTD: sensitivity 85% (95% CI 0.79 to 0.89), specificity 72% (95% CI 0.55 to 0.84), 17 studies, 1948 participants, 1371 participants with ADD; ADD from DLB: sensitivity 76% (95% CI 0.69 to 0.82), specificity 67% (95% CI 0.52 to 0.79), nine studies, 1929 participants, 1521 participants with ADD. Across all dementia subtypes, sensitivity was greater than specificity, and the balance of sensitivity and specificity was dependent on the threshold used to define test positivity. AUTHORS' CONCLUSIONS: Our review indicates that measuring ABeta42 levels in CSF may help differentiate ADD from other dementia subtypes, but the test is imperfect and tends to misdiagnose those with non-ADD as having ADD. We would caution against the use of CSF ABeta42 alone for dementia classification. However, ABeta42 may have value as an adjunct to a full clinical assessment, to aid dementia diagnosis.

An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials.

BACKGROUND AND OBJECTIVES: Filtering the deluge of new research to facilitate evidence synthesis has proven to be unmanageable using current paradigms of search and retrieval. Crowdsourcing, a way of harnessing the collective effort of a "crowd" of people, has the potential to support evidence synthesis by addressing this information overload created by the exponential growth in primary research outputs. Cochrane Crowd, Cochrane's citizen science platform, offers a range of tasks aimed at identifying studies related to health care. Accompanying each task are brief, interactive training modules, and agreement algorithms that help ensure accurate collective decision-making.The aims of the study were to evaluate the performance of Cochrane Crowd in terms of its accuracy, capacity, and autonomy and to examine contributor engagement across three tasks aimed at identifying randomized trials. STUDY DESIGN AND SETTING: Crowd accuracy was evaluated by measuring the sensitivity and specificity of crowd screening decisions on a sample of titles and abstracts, compared with "quasi gold-standard" decisions about the same records using the conventional methods of dual screening. Crowd capacity, in the form of output volume, was evaluated by measuring the number of records processed by the crowd, compared with baseline. Crowd autonomy, the capability of the crowd to produce accurate collectively derived decisions without the need for expert resolution, was measured by the proportion of records that needed resolving by an expert. RESULTS: The Cochrane Crowd community currently has 18,897 contributors from 163 countries. Collectively, the Crowd has processed 1,021,227 records, helping to identify 178,437 reports of randomized controlled trials (RCTs) for Cochrane's Central Register of Controlled Trials. The sensitivity for each task was 99.1% for the RCT identification task (RCT ID), 99.7% for the RCT identification task of trials from ClinicalTrials.gov (CT ID), and 97.7% for the identification of RCTs from the International Clinical Trials Registry Platform (ICTRP ID). The specificity for each task was 99% for RCT ID, 98.6% for CT ID, and 99.1% for CT ICTRP ID. The capacity of the combined Crowd and machine learning workflow has increased fivefold in 6 years, compared with baseline. The proportion of records requiring expert resolution across the tasks ranged from 16.6% to 19.7%. CONCLUSION: Cochrane Crowd is sufficiently accurate and scalable to keep pace with the current rate of publication (and registration) of new primary studies. It has also proved to be a popular, efficient, and accurate way for a large number of people to play an important voluntary role in health evidence production. Cochrane Crowd is now an established part of Cochrane's effort to manage the deluge of primary research being produced.

State of the evidence: a survey of global disparities in clinical trials.

INTRODUCTION: Ideally, health conditions causing the greatest global disease burden should attract increased research attention. We conducted a comprehensive global study investigating the number of randomised controlled trials (RCTs) published on different health conditions, and how this compares with the global disease burden that they impose. METHODS: We use machine learning to monitor PubMed daily, and find and analyse RCT reports. We assessed RCTs investigating the leading causes of morbidity and mortality from the Global Burden of Disease study. Using regression models, we compared numbers of actual RCTs in different health conditions to numbers predicted from their global disease burden (disability-adjusted life years (DALYs)). We investigated whether RCT numbers differed for conditions disproportionately affecting countries with lower socioeconomic development. RESULTS: We estimate 463 000 articles describing RCTs (95% prediction interval 439 000 to 485 000) were published from 1990 to July 2020. RCTs recruited a median of 72 participants (IQR 32-195). 82% of RCTs were conducted by researchers in the top fifth of countries by socio-economic development. As DALYs increased for a particular health condition by 10%, the number of RCTs in the same year increased by 5% (3.2%-6.9%), but the association was weak (adjusted R2=0.13). Conditions disproportionately affecting countries with lower socioeconomic development, including respiratory infections and tuberculosis (7000 RCTs below predicted) and enteric infections (9700 RCTs below predicted), appear relatively under-researched for their disease burden. Each 10% shift in DALYs towards countries with low and middle socioeconomic development was associated with a 4% reduction in RCTs (3.7%-4.9%). These disparities have not changed substantially over time. CONCLUSION: Research priorities are not well optimised to reduce the global burden of disease. Most RCTs are produced by highly developed countries, and the health needs of these countries have been, on average, favoured.

Does reading a book in bed make a difference to sleep in comparison to not reading a book in bed? The People's Trial-an online, pragmatic, randomised trial.

BACKGROUND: The best way of comparing healthcare treatments is through a randomised trial. In a randomised trial, we compare something (a treatment or intervention) to something else, often another treatment. Who gets what is decided at random, meaning everyone has an equal chance of getting any of the treatments. This means any differences found can be put down to the treatment received rather than other things, such as where people live, or health conditions they might have. The People's Trial aimed to help the public better understand randomised trials by inviting them to design and carry out a trial. The question chosen by the public for The People's Trial was: 'Does reading a book in bed make a difference to sleep, in comparison to not reading a book in bed?' This paper describes that trial, called 'The Reading Trial'. METHODS: The Reading Trial was an online, randomised trial. Members of the public were invited to take part through social media campaigns. People were asked to either read a book in bed before going to sleep (intervention group) or not read a book in bed before going to sleep (control group). We asked everyone to do this for 7 days, after which they measured their sleep quality. RESULTS: During December 2019, a total of 991 people took part in The Reading Trial, half (496 (50%)) in the intervention group and half (495 (50%)) in the control group. Not everyone finished the trial: 127 (25.6%) people in the intervention group and 90 (18.18%) people in the control group. Of those providing data, 156/369 (42%) people in the intervention group felt their sleep improved, compared to 112/405 (28%) of those in the control group, a difference of 14%. When we consider how certain we are of this finding, we estimate that, in The Reading Trial, sleep improved for between 8 and 22% more people in the intervention group compared to the control group. CONCLUSIONS: Reading a book in bed before going to sleep improved sleep quality, compared to not reading a book in bed. TRIAL REGISTRATION: ClinicalTrials.gov NCT04185818. Registered on 4 December 2019.