Objective: To systematically assess the association of circulating inflammation markers with the future risk of hypertension. Methods: We did a systematic literature search of PubMed and Scopus, from database inception to July 10, 2018. Prospective and retrospective cohort studies evaluating the association of circulating C reactive protein (CRP), high-sensitive CRP (hs-CRP), interleukin 6 (IL-6) and IL-1β to the risk of developing hypertension in the general population were included. The relative risks (RRs) for the top versus bottom tertiles of circulating biomarkers were calculated using a fixed-effects/random-effects model. A potential non-linear dose-response association was tested. Results: Fourteen prospective cohort studies, two retrospective cohort studies and five nested case-control studies involving 142 640 participants and 20 676 cases were identified. The RR for the third versus first tertiles of circulating CRP was 1.23 (95% CI 1.11 to 1.35; I²=59%, n=12). The association remained unchanged after adjustment for body mass index. The RRs for other biomarkers were as follows: hs-CRP (RR 1.20, 95% CI 1.02 to 1.37; I²=74%, n=7), IL-6 (RR 1.51, 95% CI 1.30 to 1.71; I²=0%, n=5), and IL-1β (RR 1.22, 95% CI 0.92 to 1.51; I²=0%, n=3). A non-linear dose-response meta-analysis demonstrated that the risk of hypertension increased linearly with increasing circulating inflammation markers, even within the low-risk and intermediate-risk categories. Conclusions: Higher levels of circulating CRP, hs-CRP and IL-6, but not IL-1β, were associated with the risk of developing hypertension. The association persisted in subgroups of studies defined by major sources of heterogeneity.

Aims : Aortic valve stenosis is commonly considered a degenerative disorder with no recommended preventive intervention, with only valve replacement surgery or catheter intervention as treatment options. We sought to assess the causal association between exposure to lipid levels and risk of aortic stenosis. Methods and results : Causality of association was assessed using two-sample Mendelian randomization framework through different statistical methods. We retrieved summary estimations of 157 genetic variants that have been shown to be associated with plasma lipid levels in the Global Lipids Genetics Consortium that included 188 577 participants, mostly European ancestry, and genetic association with aortic stenosis as the main outcome from a total of 432 173 participants in the UK Biobank. Secondary negative control outcomes included aortic regurgitation and mitral regurgitation. The odds ratio for developing aortic stenosis per unit increase in lipid parameter was 1.52 [95% confidence interval (CI) 1.22–1.90; per 0.98 mmol/L] for low density lipoprotein (LDL)-cholesterol, 1.03 (95% CI 0.80–1.31; per 0.41 mmol/L) for high density lipoprotein (HDL)-cholesterol, and 1.38 (95% CI 0.92–2.07; per 1 mmol/L) for triglycerides. There was no evidence of a causal association between any of the lipid parameters and aortic or mitral regurgitation. Conclusion : Lifelong exposure to high LDL-cholesterol increases the risk of symptomatic aortic stenosis, suggesting that LDL-lowering treatment may be effective in its prevention.

Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one. We used longitudinal data from linked electronic health records of 4.6 million patients aged 18–100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis). We also varied the prediction windows to 12, 36, 48, and 60 months after baseline and compared model performances. For internal validation, we used 5-fold cross-validation. When the initial set of variables was used, GBC outperformed RF and CPH, with an area under the receiver operating characteristic curve (AUC) of 0.779 (95% CI 0.777, 0.781), compared to 0.752 (95% CI 0.751, 0.753) and 0.740 (95% CI 0.739, 0.741), respectively. In external validation, we observed an AUC of 0.796, 0.736, and 0.736 for GBC, RF, and CPH, respectively. The addition of temporal information improved AUC across all models. In internal validation, the AUC rose to 0.848 (95% CI 0.847, 0.849), 0.825 (95% CI 0.824, 0.826), and 0.805 (95% CI 0.804, 0.806) for GBC, RF, and CPH, respectively, while the AUC in external validation rose to 0.826, 0.810, and 0.788, respectively. This enhancement also resulted in robust predictions for longer time horizons, with AUC values remaining at similar levels across all models. Overall, compared to the baseline reference CPH model, the final GBC model showed a 10.8% higher AUC (0.848 compared to 0.740) for prediction of risk of emergency admission within 24 months. GBC also showed the best calibration throughout the risk spectrum. Despite the wide range of variables included in models, our study was still limited by the number of variables included; inclusion of more variables could have further improved model performances. The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning.

Purpose of Review: To review the recent large-scale randomised evidence on pharmacologic reduction in blood pressure for the primary and secondary prevention of cardiovascular disease. Recent Findings: Based on findings of the meta-analysis of individual participant-level data from 48 randomised clinical trials and involving 344,716 participants with mean age of 65 years, the relative reduction in the risk of developing major cardiovascular events was proportional to the magnitude of achieved reduction in blood pressure. For each 5-mmHg reduction in systolic blood pressure, the risk of developing cardiovascular events fell by 10% (hazard ratio [HR] (95% confidence interval [CI], 0.90 [0.88 to 0.92]). When participants were stratified by their history of cardiovascular disease, the HRs (95% CI) in those with and without previous cardiovascular disease were 0.89 (0.86 to 0.92) and 0.91 (0.89 to 0.94), respectively, with no significant heterogeneity in these effects (adjusted P for interaction = 1.0). When these patient groups were further stratified by their baseline systolic blood pressure in increments of 10 mmHg from < 120 to ≥ 170 mmHg, there was no significant heterogeneity in the relative risk reduction across these categories in people with or without previous cardiovascular disease (adjusted P for interaction were 1.00 and 0.28, respectively). Summary: Pharmacologic lowering of blood pressure was effective in preventing major cardiovascular disease events both in people with or without previous cardiovascular disease, which was not modified by their baseline blood pressure level. Treatment effects were shown to be proportional to the intensity of blood pressure reduction, but even modest blood pressure reduction, on average, can lead to meaningful gains in the prevention of incident or recurrent cardiovascular disease.

Multimorbidity in people with cardiovascular disease (CVD) is common, but large-scale contemporary reports of patterns and trends in patients with incident CVD are limited. We investigated the burden of comorbidities in patients with incident CVD, how it changed between 2000 and 2014, and how it varied by age, sex, and socioeconomic status (SES). We used the UK Clinical Practice Research Datalink with linkage to Hospital Episode Statistics, a population-based dataset from 674 UK general practices covering approximately 7% of the current UK population. We estimated crude and age/sex-standardised (to the 2013 European Standard Population) prevalence and 95% confidence intervals for 56 major comorbidities in individuals with incident non-fatal CVD. We further assessed temporal trends and patterns by age, sex, and SES groups, between 2000 and 2014. Among a total of 4,198,039 people aged 16 to 113 years, 229,205 incident cases of non-fatal CVD, defined as first diagnosis of ischaemic heart disease, stroke, or transient ischaemic attack, were identified. Although the age/sex-standardised incidence of CVD decreased by 34% between 2000 to 2014, the proportion of CVD patients with higher numbers of comorbidities increased. The prevalence of having 5 or more comorbidities increased 4-fold, rising from 6.3% (95% CI 5.6%–17.0%) in 2000 to 24.3% (22.1%–34.8%) in 2014 in age/sex-standardised models. The most common comorbidities in age/sex-standardised models were hypertension (28.9% [95% CI 27.7%–31.4%]), depression (23.0% [21.3%–26.0%]), arthritis (20.9% [19.5%–23.5%]), asthma (17.7% [15.8%–20.8%]), and anxiety (15.0% [13.7%–17.6%]). Cardiometabolic conditions and arthritis were highly prevalent among patients aged over 40 years, and mental illnesses were highly prevalent in patients aged 30–59 years. The age-standardised prevalence of having 5 or more comorbidities was 19.1% (95% CI 17.2%–22.7%) in women and 12.5% (12.0%–13.9%) in men, and women had twice the age-standardised prevalence of depression (31.1% [28.3%–35.5%] versus 15.0% [14.3%–16.5%]) and anxiety (19.6% [17.6%–23.3%] versus 10.4% [9.8%–11.8%]). The prevalence of depression was 46% higher in the most deprived fifth of SES compared with the least deprived fifth (age/sex-standardised prevalence of 38.4% [31.2%–62.0%] versus 26.3% [23.1%–34.5%], respectively). This is a descriptive study of routine electronic health records in the UK, which might underestimate the true prevalence of diseases. The burden of multimorbidity and comorbidity in patients with incident non-fatal CVD increased between 2000 and 2014. On average, older patients, women, and socioeconomically deprived groups had higher numbers of comorbidities, but the type of comorbidities varied by age and sex. Cardiometabolic conditions contributed substantially to the burden, but 4 out of the 10 top comorbidities were non-cardiometabolic. The current single-disease paradigm in CVD management needs to broaden and incorporate the large and increasing burden of comorbidities.