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Background: Adults with severely elevated low-density lipoprotein cholesterol (LDL-c) may have familial hypercholesterolaemia (FH) and are at high risk of atherosclerotic cardiovascular disease (ASCVD). The prevalence of elevated LDL-c in primary care clinics in Malaysia is not known. Therefore, this study aimed to determine the prevalence and distributions of severely elevated LDL-c among adult patients attending public primary care clinics in Malaysia. Methods: A cross-sectional study was conducted at 11 public primary care clinics in the central states of Malaysia, among adults ≥18 years old with LDL-c recorded in the electronic medical record. Sociodemographic and LDL-c data from 2018 to 2020 were extracted. Severely elevated LDL-c was defined as ≥4 mmol/L, which were further classified into: 4.0–4.9, 5.0–5.9, 6.0–6.9 and ≥ 7 mmol/L. Results: Out of 139,702 patients, 44,374 (31.8 %) had severely elevated LDL-c of ≥4 mmol/L of which the majority were females (56.7 %). The mean (±SD) age of patients with severely elevated LDL-c was younger at 56.3 (±13.2) years compared to those with LDL-c of

BackgroundFamilial hypercholesterolemia (FH) is predominantly caused by mutations in the 4 FH candidate genes (FHCGs), namely, low-density lipoprotein receptor (LDLR), apolipoprotein B-100 (APOB-100), proprotein convertase subtilisin/kexin type 9 (PCSK9), and the LDL receptor adaptor protein 1 (LDLRAP1). It is characterized by elevated low-density lipoprotein cholesterol (LDL-c) levels leading to premature coronary artery disease. FH can be clinically diagnosed using established clinical criteria, namely, Simon Broome (SB) and Dutch Lipid Clinic Criteria (DLCC), and can be identified using the Familial Hypercholesterolemia Case Ascertainment Tool (FAMCAT), a primary care screening tool. ObjectiveThis study aims to (1) compare the detection rate of genetically confirmed FH and diagnostic accuracy between the FAMCAT, SB, and DLCC in the Malaysian primary care setting; (2) identify the genetic mutation profiles, including novel variants, in individuals with suspected FH in primary care; (3) explore the experience, concern, and expectation of individuals with suspected FH who have undergone genetic testing in primary care; and (4) evaluate the clinical utility of a web-based FH Identification Tool that includes the FAMCAT, SB, and DLCC in the Malaysian primary care setting. MethodsThis is a mixed methods evaluation study conducted in 11 Ministry of Health primary care clinics located at the central administrative region of Malaysia. In Work stream 1, the diagnostic accuracy study design is used to compare the detection rate and diagnostic accuracy of the FAMCAT, SB, and DLCC against molecular diagnosis as the gold standard. In Work stream 2, the targeted next-generation sequencing of the 4 FHCGs is used to identify the genetic mutation profiles among individuals with suspected FH. In Work stream 3a, a qualitative semistructured interview methodology is used to explore the experience, concern, and expectation of individuals with suspected FH who have undergone genetic testing. Lastly, in Work stream 3b, a qualitative real-time observation of primary care physicians using the “think-aloud” methodology is applied to evaluate the clinical utility of a web-based FH Identification Tool. ResultsThe recruitment for Work stream 1, and blood sampling and genetic analysis for Work stream 2 were completed in February 2023. Data collection for Work stream 3 was completed in March 2023. Data analysis for Work streams 1, 2, 3a, and 3b is projected to be completed by June 2023, with the results of this study anticipated to be published by December 2023. ConclusionsThis study will provide evidence on which clinical diagnostic criterion is the best to detect FH in the Malaysian primary care setting. The full spectrum of genetic mutations in the FHCGs including novel pathogenic variants will be identified. Patients’ perspectives while undergoing genetic testing and the primary care physicians experience in utilizing the web-based tool will be established. These findings will have tremendous impact on the management of patients with FH in primary care and subsequently reduce their risk of premature coronary artery disease. International Registered Report Identifier (IRRID)DERR1-10.2196/47911

Background: A more comprehensive understanding and measurement of adult social care need could contribute to efforts to develop more effective, holistic personalised care, particularly for those with multiple long-term conditions (MLTC). Progress in this area faces the challenge of a lack of clarity in the literature relating to how social care need is assessed and coded within variables included in primary care databases. Aim: To explore how social care need is assessed and coded within variables included in primary care databases. Design & setting: An exploratory rapid scoping review of peer-reviewed articles and grey literature. Method: Articles were screened and extracted onto a charting sheet and findings were summarised descriptively. Articles were included if published in English and related to primary and social care using data from national primary care databases. Results: The search yielded 4010 articles. Twenty-seven were included. Six articles used the term ‘social care need’, although related terminology was identified including ‘need factors’, ‘social support’, and ‘social care support’. Articles mainly focused on specific components of social care need, including levels of social care usage or service utilisation and costs incurred to social care, primary care, and other providers in addressing needs. A limited range of database variables were found measuring social care need. Conclusion: Further research is needed on how social care need has been defined in a UK context and captured in primary care big databases. There is potential scope to broaden the definition of social care need, which captures social service needs and wider social needs.

Bicuspid aortic valve disease (BAV) is the most common congenital heart condition, and early detection can improve outcomes for patients. In this case–control study, patients with a diagnosis of BAV were identified from their electronic primary-care records in the UK’s Clinical Practice Research Datalink (CPRD). Each case was propensity-score matched to up to five controls. The clinical features recorded before diagnosis were compared. The proposed clinical features shown to be associated with BAV (p < 0.05) were incorporated into a multivariable regression model. We identified 2898 cases. The prevalence of BAV in the CPRD was 1 in 5181, significantly lower than expected, suggesting that diagnosis and/or recording could be improved. The following biologically plausible clinical features were associated with a subsequent diagnosis of BAV: palpitations (OR: 2.86 (95% CI: 1.60, 3.16)), atrial fibrillation (AF) (OR: 2.25 (95% CI: 1.60, 3.16)) and hypertension (OR: 1.72 (1.48, 2.00)). The best model had an AUC of 0.669 (95% CI: 0.658 to 0.680), a positive predictive value (PPV) of 5.9% (95% CI: 4.0% to 8.7%) and a negative predictive value (NPV) of 99% (95% CI: 99% to 99%) at a population prevalence of 1%. This study indicates that palpitations, hypertension and AF should trigger a clinical suspicion of BAV and assessment via echocardiography. It also demonstrates the potential to develop a prediction model for BAV to stratify patients for echocardiography screening.

BackgroundVariability in low-density lipoprotein cholesterol (LDL-C) response to statins is underappreciated. We characterised patients by their statin response (SR), baseline risk of cardiovascular disease (CVD) and 10-year CVD outcomes.Methods and resultsA multivariable model was developed using 183,213 United Kingdom (UK) patients without CVD to predict probability of sub-optimal SR, defined by guidelines as ConclusionsPatients with sub-optimal response to statins experienced significantly more MACE, regardless of baseline CVD risk. To enhance cholesterol management for primary prevention, statin response should be considered alongside risk assessment.

Background: Clinical guidelines recommend specific targets for low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) for primary prevention of cardiovascular disease (CVD). Furthermore, individual variability in lipid response to statin therapy requires assessment of the association in diverse populations. Aim: To assess whether lower concentrations of LDL-C and non-HDL-C are associated with a reduced risk of major adverse cardiovascular events (MACE) in primary prevention of CVD. Design & setting: An international, new-user, cohort study will be undertaken. It will use data from three electronic health record databases from three global regions: Clinical Practice Research Datalink, UK; PREDICT-CVD, New Zealand (NZ); and the Clinical Data and Analysis Reporting System, Hong Kong (HK). Method: New statin users without a history of atherosclerotic CVD, heart failure, or chronic kidney disease, with baseline and follow-up lipid levels will be eligible for inclusion. Patients will be classified according to LDL-C (