Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care.

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Mental health conditions and COVID-19 vaccine outcomes: A scoping review.

OBJECTIVE: Research shows that people with a history of mental health conditions were at increased risk of COVID-19 infection, hospitalisation, and mortality. However, the relationship between mental health conditions and COVID-19 vaccine outcomes such as vaccine intention, uptake and vaccine breakthrough is not yet well-understood. METHODS: We conducted a systematic search on the topics of COVID-19 vaccine intentions, vaccine uptake, and vaccine breakthrough, in relation to mental health conditions (e.g., depression, schizophrenia), in four databases: PubMed, MEDLINE, SCOPUS, and PsychINFO, and the publication lists of Clinical Practice Research Datalink (CPRD), The Health Improvement Network (THIN), OpenSAFELY, and QResearch. Inclusion criteria focussed on studies reporting any of the aforementioned COVID-19 vaccine outcomes among people with mental health conditions. RESULTS: Of 251 publications initially identified, 32 met our inclusion criteria. Overall, the evidence is inconclusive regarding the levels of intention to accept COVID-19 vaccines among people with mental health conditions. People with mental health conditions were more likely to have lower uptake of COVID-19 vaccines, compared to people without. Common barriers to COVID-19 vaccine uptake included concerns about the safety, effectiveness, and side effects of the vaccines. Limited evidence also suggests that vaccine breakthrough may be a particular risk for those with substance use disorder. CONCLUSIONS: Evidence for the association between COVID-19 vaccine intentions and mental health conditions is mixed. Vaccine uptake might be lower in people with mental health conditions compared to people without, yielding interventions to encourage vaccine uptake in this population. Our understanding of COVID-19 vaccine breakthrough in this population also needs enhancing.

Advancing familial hypercholesterolaemia (FH) screening in primary care: an updated systematic review of systematic screening methods for identificaton of FH.

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.

Assessment of ethnic inequalities in diagnostic coding of familial hypercholesterolaemia (FH): A cross-sectional database study in Lambeth, South London.

BACKGROUND AND AIMS: Differences in the perceived prevalence of familial hypercholesterolemia (FH) by ethnicity are unclear. In this study, we aimed to assess the prevalence, determinants and management of diagnostically-coded FH in an ethnically diverse population in South London. METHODS: A cross-sectional analysis of 40 practices in 332,357 adult patients in Lambeth was undertaken. Factors affecting a (clinically coded) diagnosis of FH were investigated by multi-level logistic regression adjusted for socio-demographic and lifestyle factors, co-morbidities, and medications. RESULTS: The age-adjusted FH % prevalence rate (OR, 95%CI) ranged from 0.10 to 1.11, 0.00-1.31. Lower rates of FH coding were associated with age (0.96, 0.96-0.97) and male gender (0.75, 0.65-0.87), p < 0.001. Compared to a White British reference group, a higher likelihood of coded FH was noted in Other Asians (1.33, 1.01-1.76), p = 0.05, with lower rates in Black Africans (0.50, 0.37-0.68), p < 0.001, Indians (0.55, 0.34-0.89) p = 0.02, and in Black Caribbeans (0.60, 0.44-0.81), p = 0.001. The overall prevalence using Simon Broome criteria was 0.1%; we were unable to provide ethnic specific estimates due to low numbers. Lower likelihoods of FH coding (OR, 95%CI) were seen in non-native English speakers (0.66, 0.53-0.81), most deprived income quintile (0.68, 0.52-0.88), smokers (0.68,0.55-0.85), hypertension (0.62, 0.52-0.74), chronic kidney disease (0.64, 0.41-0.99), obesity (0.80, 0.67-0.95), diabetes (0.31, 0.25-0.39) and CVD (0.47, 0.36-0.63). 20% of FH coded patients were not prescribed lipid-lowering medications, p < 0.001. CONCLUSIONS: Inequalities in diagnostic coding of FH patients exist. Lower likelihoods of diagnosed FH were seen in Black African, Black Caribbean and Indian ethnic groups, in contrast to higher diagnoses in White and Other Asian ethnic groups. Hypercholesterolaemia requiring statin therapy was associated with FH diagnosis, however, the presence of cardiovascular disease (CVD) risk factors lowered the diagnosis rate for FH.

A case report of heterozygous familial hypercholesterolaemia with LDLR gene mutation complicated by premature coronary artery disease detected in primary care.

Background: Familial hypercholesterolaemia (FH) is an autosomal dominant genetic condition predominantly caused by the low-density lipoprotein receptor (LDLR) gene mutation. Case summary: This is the case of a 54-year-old Malay woman with genetically confirmed FH complicated by premature coronary artery disease (PCAD). She was clinically diagnosed in primary care at 52 years old, fulfilling the Simon Broome Criteria (possible FH), Dutch Lipid Clinic Criteria (score of 8: probable FH), and Familial Hypercholesterolaemia Case Ascertainment Tool (relative risk score of 9.51). Subsequently, she was confirmed to have a heterozygous LDLR c.190+4A>T intron 2 pathogenic variant at the age of 53 years. She was known to have hypercholesterolaemia and was treated with statin since the age of 25. However, the lipid-lowering agent was not intensified to achieve the recommended treatment target. The delayed FH diagnosis has caused this patient to have PCAD and percutaneous coronary intervention (PCI) at the age of 29 years and a second PCI at the age of 49 years. She also has a very strong family history of hypercholesterolaemia and PCAD, where seven out of eight of her siblings were affected. Despite this, FH was not diagnosed early, and cascade screening of family members was not conducted, resulting in a missed opportunity to prevent PCAD. Discussion: Familial hypercholesterolaemia can be clinically diagnosed in primary care to identify those who may require genetic testing. Multidisciplinary care focuses on improving identification, cascade screening, and management of FH, which is vital to improving prognosis and ultimately preventing PCAD.

Attitudes and Preferences Towards Screening for Dementia with a Focus on Ethnic Minority and Low Socio-Economic Groups: A Systematic Review of Research Studies Written in the English Language.

Background: Increased understanding of dementia risk-reduction and early detection of Alzheimer's disease and related disorders has spurred interest in the identification of risks for dementia, underlying putative biologies, or dementia itself. Implementation of such approaches require acceptability to the public. Research prior to 2012 indicated limited acceptability for population dementia screening. The changing landscape of dementia prevention research may influence recent perceptions. Additionally, perspectives from underserved populations, such as ethnic minorities and low socio-economic groups, are lacking. Objective: In this systematic review, we sought published studies since 2012 on attitudes and preferences of people with dementia, carers and the general public from ethnic minorities and low socio-economic groups regarding dementia screening. Methods: This review was preregistered on PROSPERO (CRD42023384115) and followed PRISMA guidelines. Key search terms were entered into five databases. Articles were included if they focused on population or risk screening for dementia via primary/community care-based assessments, and which included majority ethnic minority or low socio-economic groups or discretely considered these groups in data analysis. Data were synthesized narratively. Results: Seven studies reported perspectives of ethnic minorities regarding dementia screening; one study included people from low socio-economic groups. Results indicated that participants from ethnic minorities were willing to undergo dementia screening. Predictors of willingness included belief in benefits, desire to boost diversity, and to implement lifestyle changes. Unwillingness was associated with anxiety regarding results. Conclusions: Although there seems to be high acceptability for screening in the studied groups, more research is necessary to explore the practical considerations for screening such as cultural and economic barriers, trust, and post-screening actions.

Implementing HLA-B*58:01 testing prior to allopurinol initiation in Malaysian primary care setting: A qualitative study from doctors' and patients' perspective.

INTRODUCTION: Allopurinol, the first-line treatment for chronic gout, is a common causative drug for severe cutaneous adverse reactions (SCAR). HLA-B*58:01 allele was strongly associated with allopurinol-induced SCAR in Asian countries such as Taiwan, Japan, Thailand and Malaysia. HLA-B*58:01 screening before allopurinol initiation is conditionally recommended in the Southeast-Asian population, but the uptake of this screening is slow in primary care settings, including Malaysia. This study aimed to explore the views and experiences of primary care doctors and patients with gout on implementing HLA-B*58:01 testing in Malaysia as part of a more extensive study exploring the feasibility of implementing it routinely. METHODS: This qualitative study used in-depth interviews and focus group discussions to obtain information from patients with gout under follow-up in primary care and doctors who cared for them. Patients and doctors shared their gout management experiences and views on implementing HLA-B*58:01 screening in primary care. Data were coded and analysed using thematic analysis. RESULTS: 18 patients and 18 doctors from three different healthcare settings (university hospital, public health clinics, private general practitioner clinics) participated. The acceptability to HLA-B*58:01 screening was good among the doctors and patients. We discovered inadequate disclosure of severe side effects of allopurinol by doctors due to concerns about medication refusal by patients, which could potentially be improved by introducing HLA-B*58:01 testing. Barriers to implementation included out-of-pocket costs for patients, the cost-effectiveness of this implementation, lack of established alternative treatment pathway besides allopurinol, counselling burden and concern about genetic data security. Our participants preferred targeted screening for high-risk populations instead of universal screening. CONCLUSION: Implementing HLA-B*58:01 testing in primary care is potentially feasible if a cost-effective, targeted screening policy on high-risk groups can be developed. A clear treatment pathway for patients who test positive should be made available.

What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review.

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.