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Background Cardiovascular risk burden is associated with dementia risk and neurodegeneration-related brain structure, while the role of genetics and incident cardiovascular disease (CVD) remains unclear. Aims To examine the association of overall cardiovascular risk burden with the risk of major dementia subtypes and volumes of related brain regions in a large sample, and to explore the role of genetics and CVD onset. Methods A prospective study among 354 654 participants free of CVD and dementia (2006-2010, mean age 56.4 years) was conducted within the UK Biobank, with brain magnetic resonance imaging (MRI) measurement available for 15 104 participants since 2014. CVD risk burden was evaluated by the Framingham General Cardiovascular Risk Score (FGCRS). Dementia diagnosis was ascertained from inpatient and death register data. Results Over a median 12.0-year follow-up, 3998 all-cause dementia cases were identified. Higher FGCRS was associated with increased all-cause dementia risk after adjusting for demographic, major lifestyle, clinical factors and the polygenic risk score (PRS) of Alzheimer's disease. Comparing the high versus low tertile of FGCRS, the odds ratios (ORs) and 95% confidence intervals (CIs) were 1.26 (1.12 to 1.41) for all-cause dementia, 1.67 (1.33 to 2.09) for Alzheimer's disease and 1.53 (1.07 to 2.16) for vascular dementia (all ptrend<0.05). Incident stroke and coronary heart disease accounted for 14% (95% CI: 9% to 21%) of the association between FGCRS and all-cause dementia. Interactions were not detected for FGCRS and PRS on the risk of any dementia subtype. We observed an 83% (95% CI: 47% to 128%) higher all-cause dementia risk comparing the high-high versus low-low FGCRS-PRS category. For brain volumes, higher FGCRS was associated with greater log-transformed white matter hyperintensities, smaller cortical volume and smaller grey matter volume. Conclusions Our findings suggest that the positive association of cardiovascular risk burden with dementia risk also applies to major dementia subtypes. The association of cardiovascular risk burden with all-cause dementia is largely independent of CVD onset and genetic predisposition to dementia. [ABSTRACT FROM AUTHOR]

Introduction Falls have major implications for quality-of-life, independence and cost to health services. Strength and balance training has been found to be effective in reducing the rate/risk of falls, as long as there is adequate fidelity to the evidence-based programme. The aims of this study were to: (1) assess the feasibility of using the 'Motivate Me' and 'My Activity Programme' intervention to support falls rehabilitation when delivered in practice (2) assess study design and trial procedures for the evaluation of the intervention. Methods A two-arm, pragmatic feasibility randomised controlled trial was conducted with five health service providers in the UK. Patients aged 50+ years eligible for a falls rehabilitation exercise programme from community services were recruited and received either: (1) standard service with a smartphone for outcome measurement only or (2) standard service plus the 'Motivate Me' and 'My Activity Programme' apps. The primary outcome was feasibility of the intervention, study design and procedures (including recruitment rate, adherence and drop-out). Outcome measures include balance, function, falls, strength, fear of falling, health related quality of life, resource use and adherence, measured at baseline, three and six month post-randomisation. Blinded assessors collected the outcome measures. Results 24 patients were randomised to control group, 26 to intervention group, mean age 77.6 (Range 62 to 92) years. We recruited 37.5% of eligible participants across the five clinical sites. 77% in the intervention group completed their full exercise programme (including the use of the app). Response rate for outcome measures at six months were 77%-80% across outcome measures, but this was effected by the COVID19 pandemic. There was a mean 2.6 ± 1.9 point difference between groups in change in BERG balance score from baseline to three months and mean 4.4 ± 2.7 point difference from baseline to six months in favour of the intervention group. Less falls (1.8 ± 2.8 vs 9.1 ± 32.6) and less injurious falls (0.1 ± 0.5 vs 0.4 ± 0.6) in the intervention group and higher adherence scores at three (17.7 ± 6.8 vs 13.1 ± 6.5) and six months (15.3 ± 7.8 vs 14.9 ± 7.8). There were no related adverse events. Health professionals and patients had few technical issues with the apps. Conclusions The motivational apps and trial procedures were feasible for health professionals and patients. There are positive indications from outcome measures in the feasibility trial and key criteria for progression to full trial were met. [ABSTRACT FROM AUTHOR]

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.