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We aimed to apply and compare the QRISK3 and the adjusted Global AntiPhospholipid Syndrome (APS) Score (aGAPSS) in a cohort of systemic lupus erythematosus (SLE) patients, with and without a concomitant diagnosis of APS, in order to assess their augmented risk of developing cardiovascular diseases (CVDs).Patients (25-85 yo) with a diagnosis of SLE and/or of Secondary APS (SAPS) were included. QRISK3 was calculated using the official online calculator; aGAPSS using the validated point-values based on aPL-profile and independent risk factors.The cohort included 142 SLE patients: 34 SAPS (23.9%) and 108 SLE patients without APS (76.1%).When considering all the cohort, patients with cerebrovascular/coronary events showed higher values of aGAPSS (10.1 ± 6.2 vs. 5.8 ± 6.1; p = 0.007), but not of the QRISK3. Furthermore, a significant association was observed between the occurrence of these events and high-risk aGAPSS: p = 0.03 for aGAPSS≥8, p = 0.01 for aGAPSS ≥9, p = 0.008 for aGAPSS ≥10. aGAPSS strongly correlated with the occurrence of any thrombotic event, both at the uni- and multivariate analysis (p = 0.012 and p = 0.009). Male gender also resulted to positively correlate with the occurrence of any thrombotic event at both uni- and multivariate analysis (p = 0.017 and p = 0.03). Focusing on aPL-profile, regardless the diagnosis, we found a statistical significance only for aGAPSS (aPL+ =9.6 ± 6.3 vs. aPL- = 4.1 ± 5.1; p 0.001).Despite QRISK3 being more accurate than traditional risk score in predicting CVD risk in SLE patients, aGAPSS appears to be the most valuable tool for this purpose.

Background. Cardiovascular disease (CVD) is the most prevalent cause of death from disease and disability in the world. Reliable markers are needed to assess and reduce cardiovascular risk. This study aimed to determine if insulin resistance indexes, triglycerides to HDL-cholesterol ratio (TG/HDL-C), and triglyceride glucose index (TyG) are biomarkers for lifetime cardiovascular risk (CVR). Methods. This analytical cross-sectional study was performed on health personnel from Huaycan Hospital in Peru. The QRISK model was used to measure lifetime CVR. The association and diagnostic accuracy for TyG calculated as Ln (TG (mg/dL) × glucose (mg/dL)/2) and TG/HDL-C ratio were determined using Poisson regression models and ROC curves with Youden index. Results. In total, 291 adults (207 women and 84 men) were analyzed. In the adjusted Poisson models, each unit of TG/HDL-C increased 1.22-fold and 1.16-fold the probability of high lifetime CVR in men and women, respectively. However, each unit of TyG increased 1.98-fold in men and 3.25-fold in women the probability of high lifetime CVR. The optimal cutoff values of TG/HDL-C were 2.64 (AUC: 0.77), 3.90 (AUC: 0.80), and 2.64 (AUC: 0.74) for the overall population, men, and women, respectively. Likewise, the optimal cutoff values of TyG were 9.04 (AUC: 0.80), 8.95 (AUC: 0.79), and 9.04 (AUC: 0.80) for the overall population, men, and women, respectively. Conclusion. TG/HDL-C and TyG presented a significant association with lifetime CVR. However, TyG presented a stronger association than TG/HDL-C. Both TG/HDL-C and TyG are shown to be reliable markers for CVR in adults.

ObjectiveIdentify whether participants with lower education are less likely to report taking statins for primary cardiovascular prevention than those with higher education, but an equivalent increase in underlying cardiovascular risk.MethodsUsing data from a large prospective cohort study, UK Biobank, we calculated a QRISK3 cardiovascular risk score for 472 097 eligible participants with complete data on self-reported educational attainment and statin use (55% female participants; mean age 56 years). We used logistic regression to explore the association between (i) QRISK3 score and (ii) educational attainment on self-reported statin use. We then stratified the association between QRISK3 score and statin use, by educational attainment to test for interactions.ResultsThere was evidence of an interaction between QRISK3 score and educational attainment. Per unit increase in QRISK3 score, more educated individuals were more likely to report taking statins. In women with ≤7 years of schooling, a one unit increase in QRISK3 score was associated with a 7% higher odds of statin use (OR 1.07, 95% CI 1.07 to 1.07). In women with ≥20 years of schooling, a one unit increase in QRISK3 score was associated with an 14% higher odds of statin use (OR 1.14, 95% CI 1.14 to 1.15). Comparable ORs in men were 1.04 (95% CI 1.04 to 1.05) for ≤7 years of schooling and 1.08 (95% CI 1.08, 1.08) for ≥20 years of schooling.ConclusionPer unit increase in QRISK3 score, individuals with lower educational attainment were less likely to report using statins, likely contributing to health inequalities.

BackgroundIn 2014, the National Institute for Health and Care Excellence (NICE) changed the recommended threshold for initiating statins from a 10-year risk of cardiovascular disease (CVD) of 20% to 10% (Clinical Guideline 181), making 4.5 million extra people eligible for treatment.AimTo evaluate the impact of this guideline change on statin prescribing behaviour.Design and settingA descriptive study using data from Clinical Practice Research Datalink (CPRD), a primary care database in England.MethodPeople aged 25–84 years being initiated on statins for the primary prevention of CVD were identified. CVD risk predictions were calculated for every person using data in their medical record (calculated risks), and were extracted directly from their medical record if a QRISK score was recorded (coded risks). The 10-year CVD risks of people initiated on statins in each calendar year were compared.ResultsThe average ‘calculated risk’ of all people being initiated on statins was 20.65% in the year before the guideline change, and 20.27% after. When considering only the ‘coded risks’, the average risk was 21.85% before the guideline change, and 18.65% after. The proportion of people initiating statins that had a coded risk score in their medical record increased significantly from 2010–2017.ConclusionCurrently available evidence, which only considers people with coded risk scores in their medical record, indicates the guideline change had a large impact on statin prescribing. However, that analysis likely suffers from selection bias. This new evidence indicates only a modest impact of the guideline change. Further qualitative research about the lack of response to the guideline change is needed.

Background: Cardiovascular diseases (CVD) such as hypertension and ischemic heart diseases cause 35 to 40% of deaths every year in Pakistan. Several lifestyle factors such as dietary habits, lack of exercise, mental stress, body habitus (i.e., body mass index, waist), personal habits (smoking, sleep, fitness) and clinical conditions (i.e., diabetes, dyslipidemia and hypertension) have been shown to be strongly associated with the etiology of CVD. Epidemiological studies in Pakistan have shown poor adherence of people to healthy lifestyle and lack of knowledge in adopting healthy alternatives. There are well validated cardiovascular risk es- timation tools (QRISK model) that cn predict the probability of future cardiac events. The existing tools are based on laboratory investigations of biochemical test but there is no widely accepted tool available that predicts the CVD risk probability based on lifestyle factors. Aims: Aim of the current study was to develop alternative CVD risk estimation model based on lifestyle factors and physical attributes (without using laboratory investigation) using QRISK model as the gold standard. Study Design: Clinical and lifestyle data of one hundred and sixty subjects were collected to formulate a regression model for predicting CVD risk probability. Methods: Lifestyle factors as independent variables (IV) include BMI, waist circumference, physical activities (stamina, strength, flexibility, posture), smoking, general illnesses, dietary intake, stress and physical characteristics. CVD risk probability of QRISK Intervention computed through clinical variables was used as a dependent variable (DV) in present research. Chronological age was also included in analysis in addition to selected lifestyle factors. Regression analysis, principal component analysis and bivar- iate correlations were applied to assess the relationship among predictor variables and cardiovascular risk score. Results: Chronological age, waist circumference, BMI and strength showed significant effect on CVD risk probability. The pro- posed model can be used to calculate CVD risk probability with 72.9% accuracy for the targeted population. Conclusion: The model involves only those features which can be measured without any clinical test. The proposed model is rapid and less costly hence appropriate for use in developing countries like Pakistan. Keywords: Chronological age; cardiovascular diseases; disease risk factor; modifiable lifestyle factors; risk score.

BACKGROUND Gamma-glutamyl transferase (GGT) is associated with the risk of cardiovascular disease (CVD) in the general population. AIM To identify the association of baseline GGT level and QRISK2 score among patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD). METHODS This was a retrospective study involving 1535 biopsy-proven NAFLD patients from 10 Asian centers in 8 countries using data collected by the Gut and Obesity in Asia (referred to as "GO ASIA") workgroup. All patients with available baseline GGT levels and all 16 variables for the QRISK2 calculation (QRISK2-2017; developed by researchers at the United Kingdom National Health Service; https://qrisk.org/2017/; 10-year cardiovascular risk estimation) were included and compared to healthy controls with the same age, sex, and ethnicity. Relative risk was reported. QRISK2 score > 10% was defined as the high-CVD-risk group. Fibrosis stages 3 and 4 (F3 and F4) were considered advanced fibrosis. RESULTS A total of 1122 patients (73%) had complete data and were included in the final analysis; 314 (28%) had advanced fibrosis. The median age (interquartile range [IQR]) of the study population was 53 (44-60) years, 532 (47.4%) were females, and 492 (43.9%) were of Chinese ethnicity. The median 10-year CVD risk (IQR) was 5.9% (2.6-10.9), and the median relative risk of CVD over 10 years (IQR) was 1.65 (1.13-2.2) compared to healthy individuals with the same age, sex, and ethnicity. The high-CVD-risk group was significantly older than the low-risk group (median [IQR]: 63 [59-67] vs 49 [41-55] years; P < 0.001). Higher fibrosis stages in biopsy-proven NAFLD patients brought a significantly higher CVD risk (P < 0.001). Median GGT level was not different between the two groups (GGT [U/L]: Median [IQR], high risk 60 [37-113] vs low risk 66 [38-103], P = 0.56). There was no correlation between baseline GGT level and 10-year CVD risk based on the QRISK2 score (r = 0.02). CONCLUSION The CVD risk of NAFLD patients is higher than that of healthy individuals. Baseline GGT level cannot predict CVD risk in NAFLD patients. However, advanced fibrosis is a predictor of a high CVD risk.

Background The cardiovascular (CV) risk of patients with Type 2 diabetes (T2D) of Indo-Asian descent has never been objectively assessed, although it is documented that they have a higher prevalence of CV disease (CVD). Aims To identify groups of Indian patients with asymptomatic T2D who are at high risk of CVD as per the QRISK calculator. Method After an adequate power calculation, a nation-wide study of patients with asymptomatic T2D was conducted. The QRISK3 scores of these patients were used to derive a 10-year risk of CV events. High CVD risk was defined as ≥20% risk of CV event in 10 years. Results For a total of 1538 patients across 154 outpatient departments, the QRISK3 scores were collated. Median 10-year CVD risk was 22.2%. Mean 10-year CVD risk was 28.4% (standard deviation 22.1%), representing a 5.7-fold increase vs. controls (i.e., matched healthy adults). Absolute CVD risk increased linearly with age. Over 50% of T2D males aged above 45 years had a high (>20%) CVD risk. Women aged more than 55 years had a high risk of CVD. More than 50% of patients with a T2D duration of more than 5 years had a high risk of CVD as per the QRISK3 calculator.

ObjectiveThe study aims to explore the use of regression discontinuity analysis (RDA) to examine effects of prescription of statins on total cholesterol and adverse outcomes (type 2 diabetes, rhabdomyolysis and myopathy, myalgia and myositis, liver disease, CVD and mortality).Study Design and SettingWe conducted a prospective cohort study using the Clinical Practice Research Datalink including patients with QRISK scores of 10-30 in 2010-2013 who were last followed-up in October 2016. Comparing patients with QRISK>20 and QRISKResultsRDA confirmed statin prescription reduced total cholesterol (Mean difference (MD) -1.33 mmol/l, 95%Confidence Interval (CI) -1.93 to -0.73). RDA provided little evidence for adverse effects on diabetes, myalgia and myositis, liver disease, CVD, or mortality. The RDA analysis findings are similar to RCT results. Findings from non-RDA analysis agree with published observational studies.ConclusionsRDA can be used with large routine clinical datasets to provide evidence on effects of medications which are prescribed according to a threshold. Testable RDA assumptions were satisfied, but confidence intervals were wide, partly due to the low compliance with the prescribing threshold.

Background: Patients with IPF live longer with anti-fibrotic therapy but have high cardiovascular risk. This may increase mortality independent of the severity of their lung disease. The QRISK-2 score is validated in the UK. Using demographic (age, smoking history, ethnicity) and personal risk-factors (presence of hypercholesterolaemia/hypertension/renal/diabetic disease) it predicts the risk of a primary cardiovascular event within 10 years. A score of ≥10% indicates need for primary prevention and ≥20% is high risk. Aim: Assess the relationship between QRISK-2, IPF severity (TLCO, FVC and GAP-score) at diagnosis and all-cause mortality in IPF patients from a single UK-centre. Methods: Data for the study was taken from patient records. Individuals were followed-up until death or a cardiovascular event. Results: Mean follow up for 248 patients (table 1) was 3.1 years. 141 patients died. There were 11 cardiovascular events. Mean QRISK score was 27.0% (SD 12.3). 70% (n=172) had a QRISK-2 >20%. There was no survival difference when patients were stratified by diagnosis TLCO, FVC or GAP-stage. Patients with a QRISK 20% (fig 1). Conclusion: Multimorbidity in IPF patients affects mortality. The QRISK-2 may be a useful marker of this. TLCO and FVC at diagnosis did not predict mortality. The effect of anti-fibrotic therapy may have been contributory.

Background Despite reductions in the rates of cardiovascular disease in high income countries, individuals who are the most socioeconomically deprived remain at the highest risk of disease. Although intermediate lifestyle and behavioural risk factors explain some of this, much of the effect remains unexplained. It is not known whether differences in risk adjusted use of statins between educational groups may contribute to these inequalities. Methods Using data from a large prospective cohort study, UK Biobank, we calculated a QRISK3 cardiovascular risk score for 472 097 eligible participants with complete data on self-reported educational attainment and statin use (55% female; mean age, 56). We used logistic regression to explore the association between i) QRISK3 score and ii) educational attainment on self-report statin use. We then stratified the association between QRISK3 score, and statin use by educational attainment to test for interactions. We then replicated analyses using QRISK or QRISK2 scores recorded in primary care data and statin prescriptions recorded in primary care prescription records. Results There was evidence of an interaction between QRISK3 scores and education. For an equivalent QRISK3 score, more educated individuals were more likely to report taking statins. In women with 7 years of schooling, a one unit increase in QRISK3 score was associated with a 7% higher odds of statin use (odds ratio (OR) 1.07, 95% CI 1.07, 1.07). In women with 20 years of schooling, a one unit increase in QRISK3 score was associated with an 14% higher odds of statin use (OR 1.14, 95% CI 1.14, 1.15). Comparable ORs in men were 1.04 (95% CI 1.04, 1.05) for 7 years of schooling and 1.08 (95% CI 1.08, 1.08) for 20 years of schooling. These inequalities were also present in analyses using primary care data. Conclusion For the same level of cardiovascular risk, individuals with lower educational attainment are less likely to receive statins, likely contributing to cardiovascular inequalities. The mechanisms leading to these differences are unknown, but both health seeking behaviours and clinical factors may contribute.

Background The NHS Health Check is a national cardiovascular disease prevention programme. There is a lack of evidence on how health checks are conducted, how cardiovascular disease risk is communicated to foster risk-reducing intentions or behaviour, and the impact on communication of using different cardiovascular disease risk calculators. Objectives RIsk COmmunication in Health Check (RICO) study aimed to explore practitioner and patient understanding of cardiovascular disease risk, the associated advice or treatment offered by the practitioner, and the response of the patients in health checks supported by either the QRISK®2 or the JBS3 lifetime risk calculator. Design This was a qualitative study with quantitative process evaluation. Setting Twelve general practices in the West Midlands of England, stratified on deprivation of the local area (bottom 50% vs. top 50%), and with matched pairs randomly allocated to use QRISK2 or JBS3 during health checks. Participants A total of 173 patients eligible for NHS Health Check and 15 practitioners. Interventions The health check was delivered using either the QRISK2 10-year risk calculator (usual practice) or the JBS3 lifetime risk calculator, with heart age, event-free survival age and risk score manipulation (intervention). Results Video-recorded health checks were analysed quantitatively (n = 173; JBS3, n = 100; QRISK2, n = 73) and qualitatively (n = 128; n = 64 per group), and video-stimulated recall interviews were undertaken with 40 patients and 15 practitioners, with 10 in-depth case studies. The duration of the health check varied (6.8–38 minutes), but most health checks were short (60% lasting Limitations The main limitations were under-recruitment in some general practices and the resulting imbalance between groups. Conclusions Communication of cardiovascular disease risk during health checks was brief, particularly when using QRISK2. Patient understanding of and responses to cardiovascular disease risk information were limited. Practitioners need to better engage patients in discussion of and action-planning for their cardiovascular disease risk to reduce misunderstandings. The use of heart age, visual representation of risk and risk score manipulation was generally seen to be a useful way of doing this. Future work could focus on more fundamental issues of practitioner training and time allocation within health check consultations. Trial registration Current Controlled Trials ISRCTN10443908. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 50. See the NIHR Journals Library website for further project information.

Background: Epicardial adipose tissue (EAT) mimics visceral fat which is associated with metabolic derangements and coronary artery disease (CAD). EAT volume (EAT-V) measured by CT scan had shown good correlation with CAD. QRISK3 score is a validated risk predictor of future cardiovascular events but has limitations. We assessed whether EAT thickness (EAT-T) measured by echocardiography, a simple and widely available tool, correlated with EAT-V, and whether EAT-T is a predictor of CAD independently of QRISK3 scores. Methods: We enrolled 97 patients who underwent CTA for evaluation of chest pain. EAT-T was measured by 2D-echocardiography in parasternal long axis (PLAX) and parasternal short axis (PSAX) views. We evaluated association of EAT-T with EAT-V and CAD (≥50% stenosis on CTA); and independent predictive value of EAT-T for CAD after adjusting for QRISK 3 scores. Results: EAT-T was significantly more in patients with CAD (PLAX: 4.82 ± 1.31 mm vs. 4.06 ± 1.25 mm, p=0.005). EAT-T correlated strongly with EAT-V (r=0.75, p80% sensitivity. These findings need to be validated in larger prospective cohort studies.

Cardiometabolic disease is associated with mortality and requirement for ventilation in COVID-19. Symptomatic atherosclerotic cardiovascular disease (CVD) implies the presence of significant endothelial dysfunction and is a risk factor for severe COVID-19. However, many patients with no history of CVD exhibit severe COVID-19. We hypothesised that CVD risk calculators could serve as a proxy for significant endothelial dysfunction and predict COVID-19 outcomes in those without symptomatic CVD. We reviewed 230 patients aged 35 to 65 years, hospitalized with COVID-19 at our institution. 190 patients with no history of symptomatic CVD were included. 23% developed severe respiratory failure (RF, requiring mechanical or non-invasive ventilation), while 4% (7/164) of those without prior end-stage renal disease required renal replacement therapy (RRT). In univariate analysis, age (β = 0.057 ± 0.02, p= 0.02), BMI (β = 0.05 ± 0.03, p = 0.049) and T2DM (β = 0.81 ± 0.41, p = 0.048) predicted RF. In keeping with a link between insulin resistance and severe COVID-19, an association was observed between triglycerides and RF (β = 0.26 ± 0.14, P = 0.058). CVD risk indices QRISK3 (0.07 ± 0.02, P We observed a threshold effect for QRISK3: when 10 year risk of a CV event was > 5% the risk of RF markedly increased (OR: 6.86 (95% CI: 2.30 - 20.5, P5%. A similar trend was observed when analysis was limited to those with T2DM (Ventilatory support in T2DM: QRISK5%: 11/30). All cases of RRT occurred in those with QRISK >5%. CVD risk calculators predict severe COVID-19, supporting a role for subclinical CVD in severe COVID-19. Patients hospitalized with QRISK3 >5% may be candidates for early intervention and enhanced monitoring. Disclosure S. Lockhart: None. A. Sheikh: None. H. Griffiths: None. J. Calvo latorre: None. A. B. Daly: None. L. Sibal: None. Funding National Institute for Health Research

Background Risk prediction models are commonly used in practice to inform decisions on patients’ treatment. Uncertainty around risk scores beyond the confidence interval is rarely explored. We conducted an uncertainty analysis of the QRISK prediction tool to evaluate the robustness of individual risk predictions with varying modelling decisions. Methods We derived a cohort of patients eligible for cardiovascular risk prediction from the Clinical Practice Research Datalink (CPRD) with linked hospitalisation and mortality records (N = 3,792,474). Risk prediction models were developed using the methods reported for QRISK2 and 3, before adjusting for additional risk factors, a secular trend, geographical variation in risk and the method for imputing missing data when generating a risk score (model A–model F). Ten-year risk scores were compared across the different models alongside model performance metrics. Results We found substantial variation in risk on the individual level across the models. The 95 percentile range of risks in model F for patients with risks between 9 and 10% according to model A was 4.4–16.3% and 4.6–15.8% for females and males respectively. Despite this, the models were difficult to distinguish using common performance metrics (Harrell’s C ranged from 0.86 to 0.87). The largest contributing factor to variation in risk was adjusting for a secular trend (HR per calendar year, 0.96 [0.95–0.96] and 0.96 [0.96–0.96]). When extrapolating to the UK population, we found that 3.8 million patients may be reclassified as eligible for statin prescription depending on the model used. A key limitation of this study was that we could not assess the variation in risk that may be caused by risk factors missing from the database (such as diet or physical activity). Conclusions Risk prediction models that use routinely collected data provide estimates strongly dependent on modelling decisions. Despite this large variability in patient risk, the models appear to perform similarly according to standard performance metrics. Decision-making should be supplemented with clinical judgement and evidence of additional risk factors. The largest source of variability, a secular trend in CVD incidence, can be accounted for and should be explored in more detail. Electronic supplementary material The online version of this article (10.1186/s12916-019-1368-8) contains supplementary material, which is available to authorized users.

Purpose Psoriasis is one of the most common lifelong lasting dermatologic diseases. According to the latest studies, psoriatic patients have a higher risk of developing cardiovascular diseases. Psoriasis is considered as a systemic inflammatory disease. Several oxidative stress markers have been shown to be elevated in psoriasis. However, a panel of biomarkers has not been used yet. This study was aimed at exploring the connection between a panel of biomarkers (C-reactive protein, asymmetric dimethylarginine, uric acid, total antioxidant capacity, malondialdehyde, and orosomucoid [ORM]) and cardiovascular risk in psoriatic patients. Patients and methods The inclusion criterion was the onset of psoriasis with skin lesions. Exclusion criteria were impaired renal function (eGFR

Background Normal weight obesity (NWO) is defined by a normal body mass index (BMI) but a high percentage body fat (%BF) composition. We aimed to examine the hypothesis that subjects having NWO are at higher risk for cardio metabolic abnormalities compared to normal body weight subjects with low body fat content. Method In this cross sectional study 2439 adults' normal body Mass Index were recruited as part of the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study. We measured 6 cardio-metabolic risk factors: Low-density lipoprotein cholesterol (LDL-C), triglycerides (TGs), and high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), systolic and diastolic blood pressure, and serum C-reactive protein (hs-CRP). Two models were evaluated: in model A, the categorization of individuals utilized cut off values of BMI: 18.5–25 and %BF: > 25 in men and >30 in women; in model B, the cut off values used were BMI: 18.5–25 and BF% with cut-offs depending on age: 20–39 years, >19% and >32%; 40–59 years, >21% and 33% and 60–79 years, > 24% and 35% for men and women were used, respectively. Cardiovascular risk estimates were made using QRISK2. SPSS version 18(SPSS Inc. Chicago, IL, USA) was used for all statistical analyses. GraphPad Prism 6 for figures was used. Result Of the 2439 individuals with a normal BMI, 38.5% were found to be in high group and 61.5% were placed in normal weight groups for %BF in model A. In model B, 53.8% and 46.2% were found to be in the normal and high groups for %BF. The odds ratio for the presence of a metabolic abnormality for the high %BF group was 1.82 (95% CI, 1.42–2.34) and for high QRISK was 5.79 (95%CI, 4.17–8.02) after adjusted for age, in model A. In model B, the odds ratio for the presence of a metabolic abnormality for the high %BF group was 2.02 (95% CI, 1.68–2.42) and for high QRISK was 6.03 (95%CI, 4.45–8.08) after adjusted for sex. Conclusion It can be stated that if body fat analysis among normal weight individuals can have an important contribution to the prevention of cardiovascular disease and other obesity-related conditions.

The burden of obesity and risk of cardiovascular (CV) disease amongst an oncology population receiving active treatment is ill-defined. We performed a retrospective analysis assessing the incidence of obesity and cardiovascular (CV) risk factors in this group (grp) of patients as well as the predicted 10-year risk of a CV event. Data from all patients (pts) receiving intravenous chemotherapy in an Irish oncology satellite unit over an 18-month period was extracted from chemotherapy prescriptions and electronic patient records. To calculate patients’ 10-year risk of developing CV disease, we used QRISK, a predictive risk calculator. The prevalence of obesity (BMI > 30) amongst the total population was 19% (n = 21), with 26% (n = 28) overweight (BMI, 25–

Objective To evaluate the long-term risks of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) secondary to weight gain and clinical obesity associated with the initiation of integrase strand transfer inhibitors and tenofovir alafenamide (TAF) in the ADVANCE trial using validated risk equation tools. Design Retrospective data analysis. Methods In ADVANCE, 1053 treatment-naive participants in South Africa (99% black, 59% female) were randomized to 96 weeks of TAF/emtricitabine + dolutegravir (TAF/FTC + DTG), tenofovir disoproxil fumarate/FTC + DTG (TDF/FTC + DTG), or TDF/FTC + efavirenz (TDF/FTC/EFV). The 5 and 10-year risks of CVD were calculated using D:A:D, QRISK and Framingham, and T2DM risk using QDiabetes, Cambridge Diabetes and Leicester Practice Risk scores. Participants were included in this analysis if they were above 30 years old at baseline. Results A total of 217 (TAF/FTC + DTG), 218 (TDF/FTC + DTG), and 215 (TDF/FTC/EFV) participants had 96-week data available. Weight gain was +8.1, +4.2, and +2.4 kg on TAF/FTC + DTG, TDF/FTC + DTG, and TDF/FTC/EFV, respectively. Participants on TAF/FTC + DTG had greatest risk scores for CVD (using QRISK) and T2DM, driven by weight changes. Differences were statistically significant between TAF/FTC + DTG and TDF/FTC/EFV for CVD risk using the QRISK equation, equivalent to one extra case per 1000 people treated over 10 years, and between all treatment groups for T2DM risk. Six extra T2DM cases were predicted on TAF/FTC + DTG vs. TDF/FTC + DTG using QDiabetes. Conclusion Obesity, especially with TAF/FTC + DTG, drove increased risk of T2DM, with some evidence of greater CVD risk. However, predictive tools have not been validated in the HIV-positive and black African population.

Cardiovascular disease (CVD) remains a major problem for people with type 2 diabetes (T2DM), and the leading cause of death worldwide. We aimed to determine cardiovascular benefits of weight loss with or without remission of diabetes, and to assess utility of plasma biomarkers. 29 people with T2DM were studied at baseline and after dietary weight loss. Change in plasma adipokines and lipid related markers was examined in relation to weight loss, diabetes remission, 10-year cardiovascular risk (QRISK), and duration of diabetes. QRISK decreased markedly after weight loss (18.9 ± 2.2 to 11.2 ± 1.6%, p &lt, 0.0001) in both responders and non-responders, but non-responders remained at higher risk (15.0 ± 2.0 vs. 5.8 ± 1.6%, p &lt, 0.0001). At baseline, plasma GDF-15 was higher in longer diabetes duration (1.19 ± 0.14 vs. 0.82 ± 0.09 ng/mL, p = 0.034), as was the QRISK (22.8 ± 2.6 vs. 15.3 ± 3.4%, p = 0.031). Leptin, GDF-15 and FGF-21 decreased whereases adiponectin increased after weight loss in responders and non-responders. However, the level of FGF-21 remained higher in non-responders (0.58 [0.28–0.71] vs. 0.25 [0.15–0.42] ng/mL, p = 0.007). QRISK change correlated with change in plasma VLDL-TG (r = 0.489, p = 0.007). There was a positive correlation between rise in HDL cholesterol and the decrease in leptin (r = 0.57, p = 0.001), or rise in adiponectin (r = 0.58, p = 0.001) levels. In conclusion, weight loss markedly decreases cardiometabolic risk particularly when remission of diabetes is achieved. Leptin, adiponectin, GDF-15 and FGF-21 changes were related to weight loss not remission of diabetes. Normalization of 10-year cardiovascular risk and heart age is possible after substantial dietary weight loss and remission of T2DM.

Supplemental Digital Content is available in the text., Systolic interarm differences in blood pressure have been associated with all-cause mortality and cardiovascular disease. We undertook individual participant data meta-analyses to (1) quantify independent associations of systolic interarm difference with mortality and cardiovascular events; (2) develop and validate prognostic models incorporating interarm difference, and (3) determine whether interarm difference remains associated with risk after adjustment for common cardiovascular risk scores. We searched for studies recording bilateral blood pressure and outcomes, established agreements with collaborating authors, and created a single international dataset: the Inter-arm Blood Pressure Difference - Individual Participant Data (INTERPRESS-IPD) Collaboration. Data were merged from 24 studies (53 827 participants). Systolic interarm difference was associated with all-cause and cardiovascular mortality: continuous hazard ratios 1.05 (95% CI, 1.02–1.08) and 1.06 (95% CI, 1.02–1.11), respectively, per 5 mm Hg systolic interarm difference. Hazard ratios for all-cause mortality increased with interarm difference magnitude from a ≥5 mm Hg threshold (hazard ratio, 1.07 [95% CI, 1.01–1.14]). Systolic interarm differences per 5 mm Hg were associated with cardiovascular events in people without preexisting disease, after adjustment for Atherosclerotic Cardiovascular Disease (hazard ratio, 1.04 [95% CI, 1.00–1.08]), Framingham (hazard ratio, 1.04 [95% CI, 1.01–1.08]), or QRISK cardiovascular disease risk algorithm version 2 (QRISK2) (hazard ratio, 1.12 [95% CI, 1.06–1.18]) cardiovascular risk scores. Our findings confirm that systolic interarm difference is associated with increased all-cause mortality, cardiovascular mortality, and cardiovascular events. Blood pressure should be measured in both arms during cardiovascular assessment. A systolic interarm difference of 10 mm Hg is proposed as the upper limit of normal. Registration: URL: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42015031227

BackgroundSLE is an independent risk factor for cardiovascular disease (CVD). This study aimed to determine which among QRISK2, QRISK3, Framingham Risk Score (FRS), modified Framingham Risk Score (mFRS) and SLE Cardiovascular Risk Equation (SLECRE) best predicts CVD.MethodsThis is a single-centre analysis on 1887 patients with SLE followed prospectively according to a standard protocol. Tools’ scores were evaluated against CVD development at/within 10 years for patients with CVD and without CVD. For patients with CVD, the index date for risk score calculation was chosen as close to 10 years prior to CVD event. For patients without CVD, risk scores were calculated as close to 10 years prior to the most recent clinic appointment. Proportions of low-risk (20%) patients for developing CVD according to each tool were determined, allowing sensitivity, specificity, positive/negative predictive value and concordance (c) statistics analysis.ResultsAmong 1887 patients, 232 CVD events occurred. QRISK2 and FRS, and QRISK3 and mFRS, performed similarly. SLECRE classified the highest number of patients as intermediate and high risk. Sensitivities and specificities were 19% and 93% for QRISK2, 22% and 93% for FRS, 46% and 83% for mFRS, 47% and 78% for QRISK3, and 61% and 64% for SLECRE. Tools were similar in negative predictive value, ranging from 89% (QRISK2) to 92% (SLECRE). FRS and mFRS had the greatest c-statistics (0.73), while QRISK3 and SLECRE had the lowest (0. 67).ConclusionmFRS was superior to FRS and was not outperformed by the QRISK tools. SLECRE had the highest sensitivity but the lowest specificity. mFRS is an SLE-adjusted practical tool with a simple, intuitive scoring system reasonably appropriate for ambulatory settings, with more research needed to develop more accurate CVD risk prediction tools in this population.

Background: The current cardiovascular disease (CVD) primary prevention guidelines prioritize risk stratification by using clinical risk scores. However, subclinical atherosclerosis may rest long term undetected. This study aimed to evaluate multiple subclinical atherosclerosis parameters in relation to several CV risk scores in asymptomatic individuals. Methods: A cross-sectional, single-center study included 120 asymptomatic CVD subjects. Four CVD risk scores were computed: SCORE, Framingham, QRISK, and PROCAM. Subclinical atherosclerosis has been determined by carotid intima-media thickness (cIMT), pulse wave velocity (PWV), aortic and brachial augmentation indexes (AIXAo, respectively AIXbr), aortic systolic blood pressure (SBPao), and ankle-brachial index (ABI). Results: The mean age was 52.01 ± 10.73 years. For cIMT—SCORE was more sensitive, for PWV—Framingham score was more sensitive, for AIXbr—QRISK and PROCAM were more sensitive while for AIXao—QRISK presented better results. As for SBPao—SCORE presented more sensitive results. However, ABI did not correlate with any CVD risk score. Conclusions: All four CV risk scores are associated with markers of subclinical atherosclerosis in asymptomatic population, except for ABI, with specific particularities for each CVD risk score. Moreover, we propose specific cut-off values of CV risk scores that may indicate the need for subclinical atherosclerosis assessment.

Objectives Cardiovascular diseases (CVD) are the leading cause of death and disability worldwide. The aim of this study was to assess the association and diagnostic value of a novel uric acid index (UA index) to cardiovascular risk (CVR). Design and Methods: An analytical cross-sectional study was performed. We analyzed data from the Plan for Prevention and Surveillance of Communicable and Non-Communicable Diseases at the Hospital de Huaycan, Peru. The QRISK model was used to measure the CVR. Stepwise regression models were performed to determine significant factors to predict CVR and formulate the UA index, then the association of UA index and high CVR was evaluated by Poisson regression models, and the diagnostic accuracy was verified through ROC curves. Results In total 291 participants (206 women and 85 men) were analyzed. The correlation between UA index to CVR was stronger (R2:0.31, p, Highlights • The assessment of lifetime risk for CVD is considered the best approach in a young adult and adult population. • The novel uric acid index (UA index) presented a good diagnostic accuracy and association to cardiovascular risk. • UA index is a reliable marker to assess cardiovascular risk and supplement the QRISK in the primary health care.

AimsTo assess implementation of Trust Policy (Smoke Free Policy) on the acute adult mental health unit To evaluate barriers to implementation of local standards and NICE guidelines To evaluate if Q-Risk score is being calculated and noted.BackgroundThere are about 34,000 people residents in mental health facilities in England and Wales on any one day (Commission for Healthcare Audit and Inspection 2005) and many of them smoke.Smoke free policy implemented in the GMMH since 1st of July 2018.Smoking is single largest preventable cause of ill health & premature mortality in England.Smoking prevalence is significantly higher among people admitted to hospital due to the mental illness i.e. 70%According to WHO SHS (second hand smoking), is a human carcinogen to which there is no safe level.MethodAn audit tool questionnaire was used to collect the data on the Acute Mixed mental health ward setting i.e. Bronte Ward, Laureate House, Wythenshawe HospitalIdentified method: interview with each patient, PARIS documentation review and Patient's Kardex review.Sample size: 23 and on re-audit 12.Method of data input: Microsoft ExcelData were analyzed by calculating percentageResultThe majority of the patients that took part in the Audit were smokers (91%), a high percentage overall. This indicate how important it is for a plan to be in place regarding smoking on the ward since there is a smoke free policy now in the GMMH. Our results showed that not everyone was asked regarding their smoking status (87%).An important figure that came out from the results was that only 50% of the patients asked about their smoking status were told that there is a smoke free policy.For a smoke free policy ward only 33% of the smokers that took part in the audit were provided with brief advice regarding smoking cessation which shows that there might be a need of a more precise implementation regarding support to receive brief intervention for smoking cessation, NRT and specialist advice.The results also showed that the QRisk is not calculated, a useful marker of cardiovascular risk.ConclusionGive leaflets regarding smoking cessation on admission, offer support and advice to all the patients being on the ward. And re-audit in due course to see the effect of this intervention.

AimsThe prevalence of cardiovascular diseases (CVD) in people with intellectual disability (ID) is around 14%, higher than the general population. However, CVD risk assessments are not consistently performed. Given the high risk of premature deaths in people with ID, it is important to identify preventable risk factors and follow evidence-based interventions. QRISK-3 is a validated risk-stratification tool, which calculates the 10-year risk of developing a heart attack or stroke (https://qrisk.org/three/index.php). There are no published studies on the use of QRISK-3 in people with ID. This project aimed to understand the use of QRISK-3 in an ID clinic and to quantify individual CVD risks to recommend appropriate management options.MethodA cross sectional study was performed on 143 patients open to an ID psychiatry clinic. Patients and carers were sent an accessible information leaflet on this study. Basic demographic data and information on psychiatric diagnoses were collected. Patients were grouped according to the presence of severe mental illness (SMI) defined as schizophrenia, bipolar disorder and other psychotic illnesses. QRISK-3 ≥ 10% was defined as elevated risk in accordance with NICE guidelines. Patients who had a high QRISK-3 score were advised to contact their GP.ResultOf 143 patients, 73 (51.0%) had a mild ID and the remaining had a moderate to severe ID. The mean age was 43.3 years, 53.1% were male. Overall, 28 (19.6%) participants had an elevated CVD risk, of whom 16 (57.1%) were not on statins, which is the recommended treatment. The mean QRISK-3 score was 6.31 (standard deviation [SD] 8.95), and the relative risk is 3.50 (SD 7.13). The proportion of QRISK-3 ≥ 10% and mean score were not significantly different in those with SMI, but those with SMI were more likely to be prescribed statins than those without (14 [31.1%] vs 10 [10.2%], p = 0.002). Statins were given to 24 (16.8%) participants, of whom 12 (50%) had elevated CVD risk. 89% had a blood pressure recording within the past 5 years, 87% had height and 88% had weight recorded. 73% had lipid serology results recorded.ConclusionElevated CVD risk was common in this ID study population, and more than half with elevated QRISK-3 were not on the medical treatment recommended by national guidelines. QRISK-3 could feasibly be implemented in the outpatient setting. Increased routine CVD risk assessment and management should be considered as another measure to reduce morbidity and mortality.

ObjectiveHepatitis C virus (HCV) infection is common. Although treatment is effective, with oral antivirals curing >95% of patients, most individuals have comorbidities that persist long term. Therefore, our aim was to determine the prevalence of potentially modifiable health problems in patients with HCV and develop an HCV care bundle to identify and target comorbidities.DesignCross-sectional, observational single-centre study that recruited consecutive patients with HCV from our viral hepatitis clinics. Data were collected on cardiovascular (CV) risk factors, lifestyle behaviours, anthropometry and health-related quality of life (HRQoL). QRISK 3 was used to predict 10-year CV event risk.Results100 patients were recruited (67% male, 93% white, median age 52 years (range 24–80); 71% were treated for HCV; 34% had cirrhosis; 14% had diabetes; 61% had hypertension; 31% had metabolic syndrome; and 54% were smokers). The median 10-year CV event risk was 8.3% (range 0.3%–63%). 45% had a predicted 10-year CV event risk of >10%. Only 10% of individuals were treated with statins and 27% with antihypertensives. 92% had a predicted ‘heart age’ greater than their chronological age (median difference +7 (−4 to +26) years). HRQoL was reduced in all SF36v2 domains in the cohort. Factors independently associated with HRQoL included cirrhosis, metabolic syndrome, history of mental health disorder, sedentary behaviour and HCV viraemia.ConclusionA large proportion of patients with HCV presented with increased risk of CV events, and rates of smoking and sedentary behaviour were high, while prescribing of primary prophylaxis was infrequent. HRQoL was also reduced in the cohort. A ‘care bundle’ was developed to provide a structured approach to treating potentially modifiable health problems.

BackgroundThe most socioeconomically deprived individuals remain at the greatest risk of cardiovascular disease. Differences in risk adjusted use of statins between educational groups may contribute to these inequalities. We explore whether people with lower levels of educational attainment are less likely to take statins for a given level of cardiovascular risk.Methods and findingsUsing data from a large prospective cohort study, UK Biobank, we calculated a QRISK3 cardiovascular risk score for 472 097 eligible participants with complete data on self-reported educational attainment and statin use (55% female; mean age, 56). We used logistic regression to explore the association between i) QRISK3 score and self-report statin use and ii) educational attainment and self-report statin use. We then stratified the association of QRISK3 score, and statin use by strata of educational attainment to test for an interaction. In this sample, greater education was associated with lower statin use, whilst higher cardiovascular risk (assessed by QRISK3 score) was associated with higher statin use in both females and males. There was evidence of an interaction between QRISK3 and education, such that for the same QRISK3 score, people with more education were more likely to report taking statins. For example, in women with 7 years of schooling, equivalent to leaving school with no formal qualifications, a one unit increase in QRISK3 score was associated with a 7% higher odds of statin use (odds ratio (OR) 1.07, 95% CI 1.07, 1.07). In contrast, in women with 20 years of schooling, equivalent to obtaining a degree, a one unit increase in QRISK3 score was associated with an 14% higher odds of statin use (OR 1.14, 95% CI 1.14, 1.15). Comparable ORs in men were 1.04 (95% CI 1.04, 1.05) for men with 7 years of schooling and (95% CI 1.08, 1.08) for men with 20 years of schooling. Linkage between UK biobank and primary care data meant we were able to carry out a number of sensitivity analyses to test the robustness of our findings. However, a limitation of our study is that a number of assumptions were made when deriving QRISK3 scores which may overestimate the scores.ConclusionsFor the same level of cardiovascular risk, individuals with lower educational attainment are less likely to receive statins, likely contributing to health inequalities.SummaryWhat is already known on this topic?Despite reductions in the rates of cardiovascular disease in high income countries, individuals who are the most socioeconomically deprived remain at the highest risk.Although intermediate lifestyle and behavioural risk factors explain some of this, much of the effect remains unexplained.What does this study add?For the same increase in QRISK3 score, the likelihood of statin use increased more in individuals with high educational attainment compared with individuals with lower educational attainment.These results were similar when using UK Biobank to derive QRISK3 scores and when using QRISK scores recorded in primary care records, and when using self-reported statin prescription data or prescription data from linked primary care records.The mechanisms leading to these differences are unknown, but both health seeking behaviours and clinical factors may contribute.

Background: Estimation of absolute cardiovascular disease (CVD) risk and tailoring therapies according to the estimated risk is a fundamental concept in the primary prevention of CVD is assessed in this study. Numerous CVD risk scores are currently available for use in various populations but unfortunately, none exist for South Asians who have much higher CVD risk as compared to their western counterparts. Methods: A literature search was done using PubMed and Google search engines to prepare a narrative review on this topic. Results: Various currently available CVD risk scores and their pros and cons are summarized. The studies performed in native as well as migrant South Asians evaluating the accuracy of these risk scores for estimation of CVD risk are also summarized. The findings of these studies have generally been inconsistent, but it appears that the British risk scores (e.g. QRISK versions) may be more accurate because of inclusion of migrant South Asians in the derivation of these risk scores. However, the lack of any prospective study precludes our ability to draw any firm conclusions. Finally, the potential solution to these challenges, including the role of recalibration and subclinical atherosclerosis imaging, is also discussed. Conclusions: This review highlights the need to develop large, representative, prospectively followed databases of South Asians providing information on various CVD risk factors and their contribution to incident CVD. Such databases will not only allow the development of validated CVD risk scores for South Asians but will also enable application of machine-learning approaches to provide personalized solutions to CVD risk assessment and management in these populations.

Introduction Several cardiovascular risk calculators are available online to measure the probability of developing cardiovascular disease (CVD) without defining the appropriate population. In the current study, four risk assessment instruments were investigated with Saudi Arabian patients with CVD to identify the instrument with the best predictability. The chosen instruments were the Framingham Risk Score (FRS), Systematic Coronary Risk Evaluation (SCORE), American College of Cardiology/American Heart Association (ACC/AHA) Atherosclerotic Cardiovascular Disease Risk Estimator, and the United Kingdom score which is called QRISK®. Methods Saudi patients, 40 years and older, with acute coronary syndrome, were recruited. Data related to age, gender, ethnicity, height, weight, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL), smoking status, diabetes mellitus, rheumatoid arthritis, chronic kidney disease, atrial fibrillation, heart attack in a first-degree relative, and use of antihypertensive treatment were recorded. Results Out of 129 patients, the ACC/AHA had higher predictability with low risk (26.3%) and high risk (66.7%) groups. The QRISK® was highly applicable (95.3%); however, the SCORE was not considered applicable (22.5%). Conclusion The QRISK® is easy to implement and applicable in a population-based study, but the ACC/AHA is superior in predicting individuals with a high risk of CVD.

Aims‘All cause’ mortality is higher among patients with serious mental illness than the general population and a significant contributor from this is cardiovascular disease. Mean triglyceride levels have been shown to double and cholesterol levels to increase by at least 10% after 5 years’ treatment with clozapine. NICE guidelines state all patients should have their lipids measured at baseline, 3 months after starting treatment with a new antipsychotic, and then annually.The first aim of our audit was to identify whether patients who had been on clozapine for at least 3 months from our community mental health team (CMHT) who were not taking cholesterol lowering medication are having their lipid profile checked annually. The second aim was to see whether these patients have high total cholesterol levels and whether they had had a documented discussion about exercise, diet or lifestyle and a QRISK3 assessment.MethodWe constructed a list of 56 patients who were taking clozapine from the CMHT. We excluded 17 patients who were on cholesterol lowering medication and would have excluded any patients who had been on clozapine for less than 3 months. We then looked at whether the patients had had a lipid profile and identified patients with a cholesterol level >5.0 to indicate a ‘high cholesterol level.’ We then searched through the last year of each of the patient's case notes to see whether they had had a QRISK assessment or lifestyle advice by searching for the words ‘diet, exercise, lifestyle and QRISK’.Result36 of the 39 (92%) patients had lipid levels checked in the last 12 months. 21 of the 39 (54%) patients had a cholesterol over 5.0. 9 of the 39 (23%) patients had a documented discussion regarding lifestyle, diet or exercise in the last year. 0 of the 39 (0%) patients had a documented QRISK3 assessment.ConclusionMost (92%) patients from the CMHT had their lipid profile checked in the last year. 54% had total cholesterol level over 5.0. Only a small proportion (23%) had documented lifestyle discussion and none of the patients had a QRISK3 assessment. The results will be presented to the CMHT and we will organise teaching on giving lifestyle advice and QRISK3 assessments.

Although the importance of CVDs in COPD has been well established, COPD as a risk factor for CVDs is less well known. Not surprisingly, most major CVD consensus guidelines do not list COPD as an important risk factor for CVDs (5). Although some CVD risk prediction tools, such as QRISK (https://qrisk.org), include selected chronic inflammatory conditions such as rheumatoid arthritis, COPD is not included (6). Although previous studies have shown that reduced lung function is associated with a future risk of CV events, including myocardial infarction (MI), heart failure, and sudden death, even among lifetime nonsmokers, how (and why) this occurs is largely unknown (7). As described in this issue of the Journal, Kunisaki and colleagues (pp. 51–57) performed a secondary analysis of the SUMMIT (Study to Understand Mortality and Morbidity) trial to fill in some critical gaps in our knowledge regarding the relationship between COPD and CVDs (8). They showed that CVD events occurred mostly during periods of acute exacerbations (AECOPDs), with the highest risk occurring within the first 30 days after an AECOPD (relative risk, 3.8) and the risk returning to baseline levels at 1 year after the AECOPD. The risk was particularly notable when the presentation of the AECOPD event—a composite of the severity of the underlying COPD and the trigger—was severe enough to warrant a hospitalization. Remarkably, with these serious AECOPDs, the relative risk of a CV event was 10-fold higher in the first 30 days after hospitalization.

Huge amounts of healthcare data collected can be leveraged to predict the prognosis of a particular patient. Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. As of 2015, cardiovascular diseases contribute to 32% of global deaths. A number of models like Framingham, SCORE, and QRISK are used for risk prediction of these diseases. In this proposed method we are focusing on predicting the risk of having coronary artery disease. Using unstructured data such as clinical texts allows the inclusion of a variety of information like patient history and lifestyle. These unstructured data if used along with structured clinical reports can contribute to a better prediction.

Summary Ischaemic heart disease and stroke are the leading causes of death worldwide at 119 per 100,000 and 85 per 100,000 population. For the USA, heart disease is leading cause of death at 165 per 100,000 population. In developed countries, strokes and acute myocardial infarction in the general population have fallen from smoking reduction, lifestyle modifications and therapeutic interventions including statins. In a population-based stroke study in the UK involving primary care practices, of in-hospital strokes 90% were ischaemic, and 37% occurred within 1 week of an operation. Approximately 50% of the patients were not on a statin. In the UK, there is a national screening initiative for the prevention of atherosclerotic cardiovascular disease (ASCVD) offered to people aged 40-74 yr old. The QRISK3 tool calculates the risk of developing heart disease or stroke over 10 yr, from which recommendations are made on interventions for the prevention of ASCVD up to age 84 yr, with similar screening and assessment tools in Europe and the US. If the QRISK3 score tool for calculating cardiovascular risk is considered sufficiently robust for population screening in primary care, should anaesthetists not use the same screening for secondary care? We present a case for statin use over the perioperative period, to reduce early vascular adverse events based on statins' early pleiotropic actions, using the primary care QRISK tool for screening of ASCVD risk.

The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty.

The objective is to assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3). The study design is a longitudinal cohort study with a setting of 392 general practices (including 3.6 million patients) linked to hospital admission data. Variation in data quality was assessed using Saez stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices. There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10% for female was in a range between 7.1% and 9.0% when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7%, 62.7%, 57.8%, 64.8% and 62.1% for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients. In conclusion, the considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors., Comment: 2 tables 4 figures in the main manuscript, 1 table 3 figure in appendix. Online published in IJMI, license CC-BY-NC-ND

Background: Patients (pts) with axial spondyloarthritis (axSpA) have increased cardiovascular morbidity and mortality, compared to the general population. However, assessment of cardiovascular risk (CVR) in axSpA is complicated, as in age Objectives: To measure CVR in young pts with low activity of axSpA, and to evaluate the interrelation between CVR, measured with QRISK and gene predisposition. Methods: The study included 46 pts 25-40 years old with axSpA (ASAS criteria 2009), achieved inactive disease on TNF-α inhibitors. AxSpA activity was measured with ASDAS and BASDAI. CVR was calculated with the modified QRISK (QRISK3) (https://qrisk.org/three/). All parameters of QRISK3 were collected: smoking and diabetes statuses, angina or heart attack in a 1st degree relative Results: Mean age ± SD of included pts was 32.5±3.87 years (26% of pts aged 25 – 30, 28.3% – 30-35, 28.3% – 35-40 years), 36 (78.3%) patient were male, symptoms duration 8.71±4.72 years, duration of TNF-α inhibitor treatment 3.1±2.4 years, ASDAS 1.95±1.23, BASDAI 1.4±0.8. All three age groups were comparable (p≥0.05). Out of the 46 pts 25 – 40 years old 34 (75.6%) had a low CVR and 11 (23,9%) had increased CVR (fig.1). Average QRISK3 score of all the patients was 1.18±1.64%. In the 25-30 year old age QRISK3 was 1.51±0.25%, in pts 30-35years old – 1.09±0.6%, in 35-40 years old–2.31±1.0% (p All patients with increased CVR had a direct associations with homozygous in CC allele IL17F-11139 gene, heterozygous in GA allele of TNF-308 and heterozygous in CG allele IL6-174. Negative associations were found between CVR and heterozygosity in His/Arg and homozygous in His/His of IL17F7, homozygosity in GG allele TNF-308, homozygosity in CC IL6-174, table 1. Interrelation of CVR with another gene polymorphisms was not significant (data are not present). Conclusion: A quarter of young axSpA pts had an increased 10-year probability of cardiovascular events. Gene polymorphisms in IL-6, IL17F, and TNF-α locuses are strongly associated with increased CVR. QRISK3 and gene polymorphisms could be promising tools in CVR assessment in axSpA. Further larger studies are needed for evaluation of CVR factors in axSpA. Reference [1] Julia Hippisley-Cox, et al. BMJ2017; 357 doi: https://doi.org/10.1136/bmj.j2099. Disclosure of Interests: Elizaveta Vasilenko: None declared, Olga Nikolaeva: None declared, Anna Dadalova: None declared, Maxim Korolev: None declared, V Mazurov Grant/research support from: JSC BIOCAD, Inna Gaydukova Grant/research support from: JSC BIOCAD, Speakers bureau: paiment from Pfizer, Novartis, Abbvie, Biocad, Selgene, MSD, Sanofy does not exceed 10 000 euros

Cardiovascular risk (CVR) tends to be estimated in the short-term, which underestimates lifetime (LT)-CVR of young subjects. We determined whether LT-CVR is associated with a multimarker score of oxidative status in young adults and whether this association is independent of traditional CVR factors. Seventy-two young adults were stratified into: (1) low or (2) high LT-CVR, and (3) stable coronary artery disease (SCAD). CVR was estimated with QRisk and atherosclerotic CV disease (ASCVD) risk estimators, or second manifestations of arterial disease (SMART). Risk score. oxidative damage was determined by measuring carbonyls, oxidized LDL (oxLDL), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and xanthine oxidase activity. Antioxidant defence was determined by total antioxidant capacity (TAC), catalase (CAT) activity and superoxide dismutase (SOD) activity. Multimarker scores of systemic oxidative damage (OxyScore) and antioxidant defence (AntioxyScore) were computed as standardized variables. Subjects with high LT-CVR had significantly higher levels of oxLDL, 8-OHdG, TAC, and CAT activity than subjects with low LT-CVR or with SCAD. QRisk and ASCVD estimators correlated positively with oxLDL, TAC, and CAT activity, while SMART Risk Score correlated with carbonyls and SOD activity. OxyScore and AntioxyScore were significantly higher in subjects with high LT-CVR than with low LT-CVR or with SCAD. OxyScore, but not AntioxyScore, was associated with LT-CVR independently of each traditional CVR factor. This study for the first time demonstrates a positive association between oxidative stress and the risk of first and recurrent CV events in young adults. Sin financiación 5.411 JCR (2019) Q1, 2/29 Medical Laboratory Technology, 17/165 Medicine, General & Internal, 22/138 Medicine, Research & Experimental 1.780 SJR (2019) Q1, 4/64 Biochemistry (medical), 15/107 Physiology (medical), 30/559 Public Health, Environmental and Occupational Health, 183/2754 Medicine (miscellaneous) No data IDR 2019 UEM

Aim To provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors. Methods We performed a systematic review of CVD risk prediction models for women in the general population by updating a previous review. We searched Medline and Embase up to July 2017 and included studies in which; (a) a new model was developed, (b) an existing model was validated, or (c) a predictor was added to an existing model. Results A total of 285 prediction models for women have been developed, of these 160 (56%) were female-specific models, in which a separate model was developed solely in women and 125 (44%) were sex-predictor models. Out of the 160 female-specific models, 2 (1.3%) included one or more female-specific predictors (mostly reproductive risk factors). A total of 591 validations of sex-predictor or female-specific models were identified in 206 papers. Of these, 333 (56%) validations concerned nine models (five versions of Framingham, SCORE, Pooled Cohort Equations and QRISK). The median and pooled C statistics were comparable for sex-predictor and female-specific models. In 260 articles the added value of new predictors to an existing model was described, however in only 3 of these female-specific that no competing interests exist. predictors (reproductive risk factors) were added. Conclusions There is an abundance of models for women in the general population. Female-specific and sex-predictor models have similar predictors and performance. Female-specific predictors are rarely included. Further research is needed to assess the added value of female-specific predictors to CVD models for women and provide physicians with a well-performing prediction model for women.

Cardiovascular disease (CVD) is a well-recognized complication of diabetes. Although the association of type 2 diabetes with CVD has been well described, the mechanisms, risk stratification and screening strategies of CVD in type 1 diabetes (T1D) are less understood. This review aims to evaluate recent literature and guidelines regarding CVD in T1D. At the cellular level, the early stage of CVD is characterized by endothelial dysfunction. Recent studies have shown that endothelial function is unaffected in younger T1D patients but there is a significant degree of endothelial dysfunction in the older T1D population compared with healthy age-matched controls, highlighting the importance of the endothelial dysfunction in T1D as a major age-dependent cardiovascular risk factor. T1D risk assessment tools have been developed similar to those seen in type 2 diabetes. Foremost among these are the Danish Steno Type 1 risk engine, the Swedish T1D risk score, the Scottish T1D risk score and the QRISK risk calculator. The latter risk prediction tool is used for all patients but contains T1D as an independent risk variable and has the advantage of being derived from, and validated in, a large and diverse population. The latest version (QRISK3) is likely to be recommended for routine use in T1D patients in upcoming guidelines by the National Institute of Clinical Excellence. Mortality in adults with T1D is increasingly due to CVD. This is driven by hyperglycaemia-mediated oxidative stress and vascular inflammation, resulting in atherosclerosis and cardiac autonomic neuropathy. Coronary artery disease is the most significant contributor to CVD and in T1D, has a propensity towards a more silent and severe form. Routine screening of coronary artery disease does not alter outcomes and is therefore not recommended; however, risk prediction tools are being developed to aid identification of high-risk individuals for aggressive risk factor modification strategies.