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Additional file 1. Supplementary online file showing cardiovascular disease diagnostic Read codes (list of diagnostic Read codes for coronary artery disease, peripheralvascular disease, cerebrovascular accident (Stroke) and transient ischaemic attack (TIA) and congestive cardiac failure (heart failure).

Additional file 2: Table S1. BIC for body mass index GBTM according to number of groups and trajectory shapes. Table S2. Average posterior probability and odds of correct classification for body mass index GBTM groups. Table S3. Sensitivity analyses of body mass index measures at baseline, 2, 5,8 and 10 years, by trajectory group (Analyses done using only BMI records available in GP records* (n = 260,962). Table S4. Risk of cardiovascular disease, heart failure and mortality in BMI trajectory groups 2, 3 and 4 compared to group 1. Sensitivity analyses restricted to individuals with CPRD data linked to hospital episode statistics and office of national statistics death records (n = 138,755). Figure S1. Sensitivity analyses of body mass index (BMI) trajectories using BMI measures at baseline and then follow-up at 2 years, 5 years, 8 years and 10 years (Analyses done using only records available in GP records (n = 260,962)).

Background: Familial hypercholesterolaemia is a common inherited condition that is associated with premature cardiovascular disease. The increased cardiovascular morbidity and mortality, resulting from high levels of cholesterol since birth, can be prevented by starting lipid?lowering therapy. However, the majority of patients in the UK and worldwide remain undiagnosed. Established diagnostic criteria in current clinical practice are the Simon?Broome and Dutch Lipid Clinical network criteria and patients are classified as having probable, possible or definite familial hypercholesterolaemia.Objectives: To assess the effectiveness of healthcare interventions strategies to systematically improve identification of familial hypercholesterolaemia in primary care and other community settings compared to usual care (incidental approaches to identify familial hypercholesterolaemia in primary care and other community settings).Search methods: We searched the Cochrane Inborn Errors of Metabolism Trials Register. Date of last search: 13 September 2021.We also searched databases (Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MEDLINE, PubMed, Embase, CINAHL, Web of Science, and SCOPUS) as well as handsearching relevant conference proceedings, reference lists of included articles, and the grey literature. Date of last searches: 05 March 2020. Selection criteria: As per the Effective Practice and Organisation of Care (EPOC) Group guidelines, we planned to include randomised controlled trials (RCTs), cluster?RCTs and non?randomised studies of interventions (NRSI). Eligible NRSI were non?randomised controlled trials, prospective cohort studies, controlled before?and?after studies, and interrupted?time?series studies.We planned to selected studies with healthcare interventions strategies that aimed to systematically identify people with possible or definite clinical familial hypercholesterolaemia, in primary care and other community settings. These strategies would be compared with usual care or no intervention.We considered participants of any age from the general population who access primary care and other community settings.Data collection and analysis: Two authors planned to independently select studies according to the inclusion criteria, to extract data and assess for risk of bias and the certainty of the evidence (according to the GRADE criteria). We contacted corresponding study authors in order to obtain further information for all the studies considered in the review.Main results: No eligible RCTs or NRSIs were identified for inclusion, however, we excluded 28 studies.Authors' conclusions: Currently, there are no RCTs or controlled NRSI evidence to determine the most appropriate healthcare strategy to systematically identify possible or definite clinical familial hypercholesterolaemia in primary care or other community settings. Uncontrolled before?and?after studies were identified, but were not eligible for inclusion. Further studies assessing healthcare strategies of systematic identification of familial hypercholesterolaemia need to be conducted with diagnosis confirmed by genetic testing or validated through clinical phenotype (or both).

Additional file 1.

Additional file 2.

Definition of conditions being present; flow chart describing study cohort selection; additional figures S2-S18. (DOCX 4106 kb)

Background and Aims: Familial hypercholesterolaemia (FH) is a major cause of premature heart disease but remains unrecognised in most patients. This study investigated if a systematic primary care-based approach to identify and manage possible FH improves recommended best clinical practice.Methods: Pre- and post-intervention study in six UK general practices (population 45,033) which invited patients with total cholesterol >7.5 mmol/L to be assessed for possible FH. Compliance with national guideline recommendations to identify and manage possible FH (repeat cholesterol; assess family history of heart disease; identify secondary causes and clinical features; reduce total & LDL-cholesterol; statin prescribing; lifestyle advice) was assessed by calculating the absolute difference in measures of care pre- and six months post-intervention.Results: The intervention improved best clinical practice in 118 patients consenting to assessment (of 831 eligible patients): repeat cholesterol test (+75.4%, 95% CI 66.9-82.3); family history of heart disease assessed (+35.6%, 95% CI 27.0-44.2); diagnosis of secondary causes (+7.7%, 95% CI 4.1-13.9), examining clinical features (+6.0%, 95% CI 2.9-11.7). For 32 patients diagnosed with possible FH using Simon-Broome criteria, statin prescribing significantly improved (18.8%, 95% CI 8.9-35.3) with non-significant mean reductions in cholesterol post-intervention (total: -0.16 mmol/L, 95% CI -0.78-0.46; LDL: -0.12 mmol/L, 95% CI -0.81-0.57). Conclusions: Within six months, this simple primary care intervention improved both identification and management of patients with possible FH, in line with national evidence-based guidelines. Replicating and sustaining this approach across the country could lead to substantial improvement in health outcomes for these individuals with very high cardiovascular risk.

Introduction: The increasing prevalence of type 2 diabetes (T2DM) presents a significant burden on affected individuals and health-care systems internationally. There is, however, no agreed validated measure to infer diabetes severity from electronic health records (EHRs). We aim to quantify T2DM severity and validate it using clinical adverse outcomes.Methods and Analysis: Primary care data from the Clinical Practice Research Datalink (CPRD), linked hospitalisation and mortality records between April-2007 and March-2017 for T2DM patients in England will be used to develop a clinical algorithm to grade T2DM severity. The EHR-based algorithm will incorporate main risk factors (severity domains) for adverse outcomes to stratify T2DM cohorts by baseline and longitudinal severity scores. Provisionally, T2DM severity domains, identified through a systematic review and expert opinion are: diabetes duration, HbA1c, microvascular complications, comorbidities, and co-prescribed treatments. Severity scores will be developed by two approaches: i) calculating a count score of severity domains; ii) through hierarchical stratification of complications. Regression models estimates will be used to calculate domains weights. Survival analysis for the association between weighted severity scores and future outcomes: cardiovascular events; hospitalisation (diabetes-related, cardiovascular); and mortality (diabetes-related, cardiovascular, all-cause mortality) will be performed as a statistical validation. The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions. We anticipate that the developed algorithm will be a practical tool for practitioners, aid clinical management decision-making, inform stratified medicine, support future clinical trials and contribute to more effective service-planning and policy-making.Ethics and Dissemination: The study protocol was approved by the Independent Scientific Advisory Committee (ISAC). Some data were presented at the NIHR SPCR Showcase, September-2017, Oxford,UK; the Diabetes UK Professional Conference March-2018, London,UK. The study findings will be disseminated in relevant academic conferences and peer-reviewed journals.

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:\ud \ud The purpose of this review is to assess the effectiveness of interventions to systematically improve identification of FH in non-specialist settings compared to usual care (incidental approaches to identify FH in non-specialist settings).

Aims: Despite the increasing prevalence of Type 2 diabetes, whichpresents a significant burden on healthcare resources, there is noagreed validated UK measure to infer Type 2 diabetes severity fromelectronic health records (EHRs). This study aimed to identifyimportant clinical severity domains, recorded in EHRs, whichcould be used to quantify the severity of Type 2 diabetes.Methods: Data from the Clinical Practice Research Datalink(CPRD) and hospitalisation records for people with Type 2diabetes, registered with linked English general practices, to beused to develop a clinical decision algorithm to grade diabetesseverity between 2006 and 2016. At this stage of the study, wehave identified clinically relevant severity domains (main riskfactors for adverse outcomes) to be incorporated in the severityalgorithm.Results: The main severity domains for Type 2 diabetes identifiedthrough a systematic review and expert opinion include age,diabetes duration, HbA1c, hypoglycaemia, microvascular complications,cardiovascular (CV) disease (heart failure, coronary heartdisease, revascularisation interventions), cerebrovascular disease,renal disease, patterns of prescribed antidiabetic and cardiovasculartreatments, hospital admissions (any-cause, diabetes-relatedand CV disease-related admissions).Summary: All identified severity domains, recorded in routinelycollected real-world EHRs, are clinically relevant in our work todefine the severity of Type 2 diabetes. This first step forms a platformon which to develop the severity tool, which will be informative topractitioners, could stratify clinical management of people withType 2 diabetes, support commissioning and public health programmesand inform the methodology of measuring the severity ofother chronic conditions managed in primary care.