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Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.

ABSTRACT: Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance.Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included.In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model.There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.

SUBMITTER: Tang EY

PROVIDER: S-EPMC4559315 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.

Tang Eugene Y H EY Harrison Stephanie L SL Errington Linda L Gordon Mark F MF Visser Pieter Jelle PJ Novak Gerald G Dufouil Carole C Brayne Carol C Robinson Louise L Launer Lenore J LJ Stephan Blossom C M BC

PloS one 20150903 9

<h4>Background</h4>Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model progno ...[more]

PMID: 26334524

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Project description:BACKGROUND:A previous Cochrane systematic review concluded there is insufficient evidence to support the routine use of 18F-FDG PET in clinical practice in people with mild cognitive impairment (MCI). OBJECTIVES:To update the evidence and reassess the accuracy of 18F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease (AD) dementia at follow-up. METHODS:A systematic review including comprehensive search of electronic databases from January 2013 to July 2017, to update original searches (1999 to 2013). All key review steps, including quality assessment using QUADAS 2, were performed independently and blindly by two review authors. Meta-analysis could not be conducted due to heterogeneity across studies. RESULTS:When all included studies were examined across all semi-quantitative and quantitative metrics, exploratory analysis for conversion of MCI to AD dementia (n = 24) showed highly variable accuracy; half the studies failed to meet four or more of the seven sets of QUADAS 2 criteria. Variable accuracy for all metrics was also found across eleven newly included studies published in the last 5 years (range: sensitivity 56-100%, specificity 24-100%). The most consistently high sensitivity and specificity values (approximately ?80%) were reported for the sc-SPM (single case statistical parametric mapping) metric in 6 out of 8 studies. CONCLUSION:Systematic and comprehensive assessment of studies of 18FDG-PET for prediction of conversion from MCI to AD dementia reveals many studies have methodological limitations according to Cochrane diagnostic test accuracy gold standards, and shows accuracy remains highly variable, including in the most recent studies. There is some evidence, however, of higher and more consistent accuracy in studies using computer aided metrics, such as sc-SPM, in specialized clinical settings. Robust, methodologically sound prospective longitudinal cohort studies with long (?5 years) follow-up, larger consecutive samples, and defined baseline threshold(s) are needed to test these promising results. Further evidence of the clinical validity and utility of 18F-FDG PET in people with MCI is needed.

Project description:BackgroundDementia is a devastating neurologic condition that is common in older adults. We previously reviewed the epidemiological evidence examining the hypothesis that long-term exposure to air pollution affects dementia risk. Since then, the evidence base has expanded rapidly.ObjectivesWith this update, we collectively review new and previously identified epidemiological studies on air pollution and late-life cognitive health, highlighting new developments and critically discussing the merits of the evidence.MethodsUsing a registered protocol (PROSPERO 2020 CRD42020152943), we updated our literature review to capture studies published through 31 December 2020, extracted data, and conducted a bias assessment.ResultsWe identified 66 papers (49 new) for inclusion in this review. Cognitive level remained the most commonly considered outcome, and particulate matter (PM) remained the most commonly considered air pollutant. Since our prior review, exposure estimation methods in this research have improved, and more papers have looked at cognitive change, neuroimaging, and incident cognitive impairment/dementia, though methodological concerns remain common. Many studies continue to rely on administrative records to ascertain dementia, have high potential for selection bias, and adjust for putative mediating factors in primary models. A subset of 35 studies met strict quality criteria. Although high-quality studies of fine particulate matter with aerodynamic diameter ≤2.5μm (PM2.5) and cognitive decline generally supported an adverse association, other findings related to PM2.5 and findings related to particulate matter with aerodynamic diameter ≤10μm (PM10, NO2, and NOx) were inconclusive, and too few papers reported findings with ozone to comment on the likely direction of association. Notably, only a few findings on dementia were included for consideration on the basis of quality criteria.DiscussionStrong conclusions remain elusive, although the weight of the evidence suggests an adverse association between PM2.5 and cognitive decline. However, we note a continued need to confront methodological challenges in this line of research. https://doi.org/10.1289/EHP8716.

Project description:ObjectiveTo provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases.DesignUmbrella systematic review.Data sourcesPubMed, Scopus, Cochrane Library.Eligibility criteriaSystematic reviews or meta-analysis examining and comparing performances of RPMs for CVDs, hypertension or diabetes in healthy adult (18-65 years old) population, published in English language.Data extraction and synthesisData were extracted according to the following parameters: number of studies included, intervention (RPMs applied/assessed), comparison, performance, validation and outcomes. A narrative synthesis was performed. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Study selection3612 studies were identified. After title/abstract screening and removal of duplicate articles, 37 studies met the eligibility criteria. After reading the full text, 13 were deemed relevant for inclusion. Three further papers from the reference lists of these articles were then added.Study appraisalThe methodological quality of the included studies was assessed using the AMSTAR tool.Risk of bias in individual studiesRisk of Bias evaluation was carried out using the ROBIS tool.ResultsSixteen studies met the inclusion criteria: six focused on diabetes, two on hypertension and eight on CVDs. Globally, prediction models for diabetes and hypertension showed no significant difference in effectiveness. Conversely, some promising differences among prediction tools were highlighted for CVDs. The Ankle-Brachial Index, in association with the Framingham tool, and QRISK scores provided some evidence of a certain superiority compared with Framingham alone.LimitationsDue to the significant heterogeneity of the studies, it was not possible to perform a meta-analysis. The electronic search was limited to studies in English and to three major international databases (MEDLINE/PubMed, Scopus and Cochrane Library), with additional works derived from the reference list of other studies; grey literature with unpublished documents was not included in the search. Furthermore, no assessment of potential adverse effects of RPMs was carried out.ConclusionsConsistent evidence is available only for CVD prediction: the Framingham score, alone or in combination with the Ankle-Brachial Index, and the QRISK score can be confirmed as the gold standard. Further efforts should not be concentrated on creating new scores, but rather on performing external validation of the existing ones, in particular on high-risk groups. Benefits could be further improved by supplementing existing models with information on lifestyle, personal habits, family and employment history, social network relationships, income and education.Prospero registration numberCRD42018088012.

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Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.

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Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review.

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