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Introduction: Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods: MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results: In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion: The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
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BACKGROUND: Familial Hypercholesterolaemia (FH) is a greatly underdiagnosed and treatable genetic lipid disorder which significantly increases risk of premature cardiovascular disease. The prevalence of monogenic FH is thought to be 1 in 250-350. The NHS Long Term Plan aims to increase FH detection to at least 25% over 5 years in collaboration with primary care, supported by the NHS genomics programme. AIM: This systematic review evaluates systematic screening methods for FH in adults aged ≥18 years in primary care. METHOD: Seven databases [Cochrane, PubMed, Ovid, CINAHL, ProQuest, Web of Science, Scopus], four clinical trial registries [ISRCTN, ANZCTR, Clinicaltrials.gov, WHO-ICTRP] and relevant grey literature [OpenGrey] from March 2020 to May 2023 were searched. Only studies including adults were eligible. Risk of bias was assessed using ROBINS-I. RESULTS: 831 records were screened. No randomised, controlled studies were identified. From full-text review, five eligible non-randomised studies out of 57 (6.90%) were identified. The included studies all used automated FH case-identification from electronic medical records (EMR) and were high quality studies with a moderate risk of bias. Narrative synthesis reported outcomes which included three algorithmic studies, with a pooled detection rate, DR 14.4% (95%CI 11.67-16.62), one supervised Machine Learning [Ensemble] study, DR 15.5% (95%CI 15.47-15.53) and one study utilising a hybrid diagnostic EMR model and/or FH genotype confirmation DR 25.0% (95%CI 16.30-35.8). No adverse effects were reported in these studies. CONCLUSION: Incorporating automated case-finding from EMR with clinical follow-up in primary care can enhance FH identification. Pathways incorporating genotyping showed the best detection rate.