Sodium-based paracetamol: impact on blood pressure, cardiovascular events, and all-cause mortality.

BACKGROUND AND AIMS: Effervescent formulations of paracetamol containing sodium bicarbonate have been reported to associate with increased blood pressure and a higher risk of cardiovascular diseases and all-cause mortality. Given the major implications of these findings, the reported associations were re-examined. METHODS: Using linked electronic health records data, a cohort of 475 442 UK individuals with at least one prescription of paracetamol, aged between 60 and 90 years, was identified. Outcomes in patients taking sodium-based paracetamol were compared with those taking non-sodium-based formulations of the same. Using a deep learning approach, associations with systolic blood pressure (SBP), major cardiovascular events (myocardial infarction, heart failure, and stroke), and all-cause mortality within 1 year after baseline were investigated. RESULTS: A total of 460 980 and 14 462 patients were identified for the non-sodium-based and sodium-based paracetamol exposure groups, respectively (mean age: 74 years; 64% women). Analysis revealed no difference in SBP [mean difference -0.04 mmHg (95% confidence interval -0.51, 0.43)] and no association with major cardiovascular events [relative risk (RR) 1.03 (0.91, 1.16)]. Sodium-based paracetamol showed a positive association with all-cause mortality [RR 1.46 (1.40, 1.52)]. However, after further accounting of other sources of residual confounding, the observed association attenuated towards the null [RR 1.08 (1.01, 1.16)]. Exploratory analyses revealed dysphagia and related conditions as major sources of uncontrolled confounding by indication for this association. CONCLUSIONS: This study does not support previous suggestions of increased SBP and an elevated risk of cardiovascular events from short-term use of sodium bicarbonate paracetamol in routine clinical practice.

Blood pressure lowering and risk of new-onset type 2 diabetes: an individual participant data meta-analysis.

BACKGROUND: Blood pressure lowering is an established strategy for preventing microvascular and macrovascular complications of diabetes, but its role in the prevention of diabetes itself is unclear. We aimed to examine this question using individual participant data from major randomised controlled trials. METHODS: We performed a one-stage individual participant data meta-analysis, in which data were pooled to investigate the effect of blood pressure lowering per se on the risk of new-onset type 2 diabetes. An individual participant data network meta-analysis was used to investigate the differential effects of five major classes of antihypertensive drugs on the risk of new-onset type 2 diabetes. Overall, data from 22 studies conducted between 1973 and 2008, were obtained by the Blood Pressure Lowering Treatment Trialists' Collaboration (Oxford University, Oxford, UK). We included all primary and secondary prevention trials that used a specific class or classes of antihypertensive drugs versus placebo or other classes of blood pressure lowering medications that had at least 1000 persons-years of follow-up in each randomly allocated arm. Participants with a known diagnosis of diabetes at baseline and trials conducted in patients with prevalent diabetes were excluded. For the one-stage individual participant data meta-analysis we used stratified Cox proportional hazards model and for the individual participant data network meta-analysis we used logistic regression models to calculate the relative risk (RR) for drug class comparisons. FINDINGS: 145 939 participants (88 500 [60·6%] men and 57 429 [39·4%] women) from 19 randomised controlled trials were included in the one-stage individual participant data meta-analysis. 22 trials were included in the individual participant data network meta-analysis. After a median follow-up of 4·5 years (IQR 2·0), 9883 participants were diagnosed with new-onset type 2 diabetes. Systolic blood pressure reduction by 5 mm Hg reduced the risk of type 2 diabetes across all trials by 11% (hazard ratio 0·89 [95% CI 0·84-0·95]). Investigation of the effects of five major classes of antihypertensive drugs showed that in comparison to placebo, angiotensin-converting enzyme inhibitors (RR 0·84 [95% 0·76-0·93]) and angiotensin II receptor blockers (RR 0·84 [0·76-0·92]) reduced the risk of new-onset type 2 diabetes; however, the use of ß blockers (RR 1·48 [1·27-1·72]) and thiazide diuretics (RR 1·20 [1·07-1·35]) increased this risk, and no material effect was found for calcium channel blockers (RR 1·02 [0·92-1·13]). INTERPRETATION: Blood pressure lowering is an effective strategy for the prevention of new-onset type 2 diabetes. Established pharmacological interventions, however, have qualitatively and quantitively different effects on diabetes, likely due to their differing off-target effects, with angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers having the most favourable outcomes. This evidence supports the indication for selected classes of antihypertensive drugs for the prevention of diabetes, which could further refine the selection of drug choice according to an individual's clinical risk of diabetes. FUNDING: British Heart Foundation, National Institute for Health Research, and Oxford Martin School.

How Much Lowering of Blood Pressure Is Required to Prevent Cardiovascular Disease in Patients With and Without Previous Cardiovascular Disease?

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). 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.

Antihypertensive treatment and risk of cancer: an individual participant data meta-analysis.

BACKGROUND: Some studies have suggested a link between antihypertensive medication and cancer, but the evidence is so far inconclusive. Thus, we aimed to investigate this association in a large individual patient data meta-analysis of randomised clinical trials. METHODS: We searched PubMed, MEDLINE, The Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from Jan 1, 1966, to Sept 1, 2019, to identify potentially eligible randomised controlled trials. Eligible studies were randomised controlled trials comparing one blood pressure lowering drug class with a placebo, inactive control, or other blood pressure lowering drug. We also required that trials had at least 1000 participant years of follow-up in each treatment group. Trials without cancer event information were excluded. We requested individual participant data from the authors of eligible trials. We pooled individual participant-level data from eligible trials and assessed the effects of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), ß blockers, calcium channel blockers, and thiazide diuretics on cancer risk in one-stage individual participant data and network meta-analyses. Cause-specific fixed-effects Cox regression models, stratified by trial, were used to calculate hazard ratios (HRs). The primary outcome was any cancer event, defined as the first occurrence of any cancer diagnosed after randomisation. This study is registered with PROSPERO (CRD42018099283). FINDINGS: 33 trials met the inclusion criteria, and included 260 447 participants with 15 012 cancer events. Median follow-up of included participants was 4·2 years (IQR 3·0-5·0). In the individual participant data meta-analysis comparing each drug class with all other comparators, no associations were identified between any antihypertensive drug class and risk of any cancer (HR 0·99 [95% CI 0·95-1·04] for ACEIs; 0·96 [0·92-1·01] for ARBs; 0·98 [0·89-1·07] for ß blockers; 1·01 [0·95-1·07] for thiazides), with the exception of calcium channel blockers (1·06 [1·01-1·11]). In the network meta-analysis comparing drug classes against placebo, we found no excess cancer risk with any drug class (HR 1·00 [95% CI 0·93-1·09] for ACEIs; 0·99 [0·92-1·06] for ARBs; 0·99 [0·89-1·11] for ß blockers; 1·04 [0·96-1·13] for calcium channel blockers; 1·00 [0·90-1·10] for thiazides). INTERPRETATION: We found no consistent evidence that antihypertensive medication use had any effect on cancer risk. Although such findings are reassuring, evidence for some comparisons was insufficient to entirely rule out excess risk, in particular for calcium channel blockers. FUNDING: British Heart Foundation, National Institute for Health Research, Oxford Martin School.

Multi-morbidity and blood pressure trajectories in hypertensive patients: A multiple landmark cohort study.

BACKGROUND: Our knowledge of how to better manage elevated blood pressure (BP) in the presence of comorbidities is limited, in part due to exclusion or underrepresentation of patients with multiple chronic conditions from major clinical trials. We aimed to investigate the burden and types of comorbidities in patients with hypertension and to assess how such comorbidities and other variables affect BP levels over time. METHODS AND FINDINGS: In this multiple landmark cohort study, we used linked electronic health records from the United Kingdom Clinical Practice Research Datalink (CPRD) to compare systolic blood pressure (SBP) levels in 295,487 patients (51% women) aged 61.5 (SD = 13.1) years with first recorded diagnosis of hypertension between 2000 and 2014, by type and numbers of major comorbidities, from at least 5 years before and up to 10 years after hypertension diagnosis. Time-updated multivariable linear regression analyses showed that the presence of more comorbidities was associated with lower SBP during follow-up. In hypertensive patients without comorbidities, mean SBP at diagnosis and at 10 years were 162.3 mm Hg (95% confidence interval [CI] 162.0 to 162.6) and 140.5 mm Hg (95% CI 140.4 to 140.6), respectively; in hypertensive patients with ≥5 comorbidities, these were 157.3 mm Hg (95% CI 156.9 to 157.6) and 136.8 mm Hg (95% 136.4 to 137.3), respectively. This inverse association between numbers of comorbidities and SBP was not specific to particular types of comorbidities, although associations were stronger in those with preexisting cardiovascular disease. Retrospective analysis of recorded SBP showed that the difference in mean SBP 5 years before diagnosis between those without and with ≥5 comorbidities was -9 mm Hg (95% CI -9.7 to -8.3), suggesting that mean recorded SBP already differed according to the presence of comorbidity before baseline. Within 1 year after the diagnosis, SBP substantially declined, but subsequent SBP changes across comorbidity status were modest, with no evidence of a more rapid decline in those with more or specific types of comorbidities. We identified factors, such as prescriptions of antihypertensive drugs and frequency of healthcare visits, that can explain SBP differences according to numbers or types of comorbidities, but these factors only partly explained the recorded SBP differences. Nevertheless, some limitations have to be considered including the possibility that diagnosis of some conditions may not have been recorded, varying degrees of missing data inherent in analytical datasets extracted from routine health records, and greater measurement errors in clinical measurements taken in routine practices than those taken in well-controlled clinical study settings. CONCLUSIONS: BP levels at which patients were diagnosed with hypertension varied substantially according to the presence of comorbidities and were lowest in patients with multi-morbidity. Our findings suggest that this early selection bias of hypertension diagnosis at different BP levels was a key determinant of long-term differences in BP by comorbidity status. The lack of a more rapid decline in SBP in those with multi-morbidity provides some reassurance for BP treatment in these high-risk individuals.

Blood pressure-lowering treatment for the prevention of cardiovascular events in patients with atrial fibrillation: An individual participant data meta-analysis.

BACKGROUND: Randomised evidence on the efficacy of blood pressure (BP)-lowering treatment to reduce cardiovascular risk in patients with atrial fibrillation (AF) is limited. Therefore, this study aimed to compare the effects of BP-lowering drugs in patients with and without AF at baseline. METHODS AND FINDINGS: The study was based on the resource provided by the Blood Pressure Lowering Treatment Trialists' Collaboration (BPLTTC), in which individual participant data (IPD) were extracted from trials with over 1,000 patient-years of follow-up in each arm, and that had randomly assigned patients to different classes of BP-lowering drugs, BP-lowering drugs versus placebo, or more versus less intensive BP-lowering regimens. For this study, only trials that had collected information on AF status at baseline were included. The effects of BP-lowering treatment on a composite endpoint of major cardiovascular events (stroke, ischaemic heart disease or heart failure) according to AF status at baseline were estimated using fixed-effect one-stage IPD meta-analyses based on Cox proportional hazards models stratified by trial. Furthermore, to assess whether the associations between the intensity of BP reduction and cardiovascular outcomes are similar in those with and without AF at baseline, we used a meta-regression. From the full BPLTTC database, 28 trials (145,653 participants) were excluded because AF status at baseline was uncertain or unavailable. A total of 22 trials were included with 188,570 patients, of whom 13,266 (7%) had AF at baseline. Risk of bias assessment showed that 20 trials were at low risk of bias and 2 trials at moderate risk. Meta-regression showed that relative risk reductions were proportional to trial-level intensity of BP lowering in patients with and without AF at baseline. Over 4.5 years of median follow-up, a 5-mm Hg systolic BP (SBP) reduction lowered the risk of major cardiovascular events both in patients with AF (hazard ratio [HR] 0.91, 95% confidence interval [CI] 0.83 to 1.00) and in patients without AF at baseline (HR 0.91, 95% CI 0.88 to 0.93), with no difference between subgroups. There was no evidence for heterogeneity of treatment effects by baseline SBP or drug class in patients with AF at baseline. The findings of this study need to be interpreted in light of its potential limitations, such as the limited number of trials, limitation in ascertaining AF cases due to the nature of the arrhythmia and measuring BP in patients with AF. CONCLUSIONS: In this meta-analysis, we found that BP-lowering treatment reduces the risk of major cardiovascular events similarly in individuals with and without AF. Pharmacological BP lowering for prevention of cardiovascular events should be recommended in patients with AF.

Association between cardiometabolic disease multimorbidity and all-cause mortality in 2 million women and men registered in UK general practices.

BACKGROUND: Myocardial infarction (MI), stroke and diabetes share underlying risk factors and commonalities in clinical management. We examined if their combined impact on mortality is proportional, amplified or less than the expected risk separately of each disease and whether the excess risk is explained by their associated comorbidities. METHODS: Using large-scale electronic health records, we identified 2,007,731 eligible patients (51% women) and registered with general practices in the UK and extracted clinical information including diagnosis of myocardial infarction (MI), stroke, diabetes and 53 other long-term conditions before 2005 (study baseline). We used Cox regression to determine the risk of all-cause mortality with age as the underlying time variable and tested for excess risk due to interaction between cardiometabolic conditions. RESULTS: At baseline, the mean age was 51 years, and 7% (N = 145,910) have had a cardiometabolic condition. After a 7-year mean follow-up, 146,994 died. The sex-adjusted hazard ratios (HR) (95% confidence interval [CI]) of all-cause mortality by baseline disease status, compared to those without cardiometabolic disease, were MI = 1.51 (1.49-1.52), diabetes = 1.52 (1.51-1.53), stroke = 1.84 (1.82-1.86), MI and diabetes = 2.14 (2.11-2.17), MI and stroke = 2.35 (2.30-2.39), diabetes and stroke = 2.53 (2.50-2.57) and all three = 3.22 (3.15-3.30). Adjusting for other concurrent comorbidities attenuated these estimates, including the risk associated with having all three conditions (HR = 1.81 [95% CI 1.74-1.89]). Excess risks due to interaction between cardiometabolic conditions, particularly when all three conditions were present, were not significantly greater than expected from the individual disease effects. CONCLUSION: Myocardial infarction, stroke and diabetes were associated with excess mortality, without evidence of any amplification of risk in people with all three diseases. The presence of other comorbidities substantially contributed to the excess mortality risks associated with cardiometabolic disease multimorbidity.

Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study.

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.

Learning multimorbidity patterns from electronic health records using Non-negative Matrix Factorisation.

Multimorbidity, or the presence of several medical conditions in the same individual, has been increasing in the population - both in absolute and relative terms. Nevertheless, multimorbidity remains poorly understood, and the evidence from existing research to describe its burden, determinants and consequences has been limited. Previous studies attempting to understand multimorbidity patterns are often cross-sectional and do not explicitly account for multimorbidity patterns' evolution over time; some of them are based on small datasets and/or use arbitrary and narrow age ranges; and those that employed advanced models, usually lack appropriate benchmarking and validations. In this study, we (1) introduce a novel approach for using Non-negative Matrix Factorisation (NMF) for temporal phenotyping (i.e., simultaneously mining disease clusters and their trajectories); (2) provide quantitative metrics for the evaluation of these clusters and trajectories; and (3) demonstrate how the temporal characteristics of the disease clusters that result from our model can help mine multimorbidity networks and generate new hypotheses for the emergence of various multimorbidity patterns over time. We trained and evaluated our models on one of the world's largest electronic health records (EHR) datasets, containing more than 7 million patients, from which over 2 million where relevant to, and hence included in this study.

Deep learning for electronic health records: A comparative review of multiple deep neural architectures.

Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories. While this can be a promising trend, vast paper-to-paper variability (from data sources and models they use to the clinical questions they attempt to answer) have hampered the field's ability to simply compare and contrast such models for a given application of interest. Thus, in this paper, we aim to provide a comparative review of the key deep learning architectures that have been applied to EHR data. Furthermore, we also aim to: (1) introduce and use one of the world's largest and most complex linked primary care EHR datasets (i.e., Clinical Practice Research Datalink, or CPRD) as a new asset for training such data-hungry models; (2) provide a guideline for working with EHR data for deep learning; (3) share some of the best practices for assessing the "goodness" of deep-learning models in clinical risk prediction; (4) and propose future research ideas for making deep learning models more suitable for the EHR data. Our results highlight the difficulties of working with highly imbalanced datasets, and show that sequential deep learning architectures such as RNN may be more suitable to deal with the temporal nature of EHR.

Diagnostic tests, drug prescriptions, and follow-up patterns after incident heart failure: A cohort study of 93,000 UK patients.

BACKGROUND: Effective management of heart failure is complex, and ensuring evidence-based practice presents a major challenge to health services worldwide. Over the past decade, the United Kingdom introduced a series of national initiatives to improve evidence-based heart failure management, including a landmark pay-for-performance scheme in primary care and a national audit in secondary care started in 2004 and 2007, respectively. Quality improvement efforts have been evaluated within individual clinical settings, but patterns of care across its continuum, although a critical component of chronic disease management, have not been studied. We have designed this study to investigate patients' trajectories of care around the time of diagnosis and their variation over time by age, sex, and socioeconomic status. METHODS AND FINDINGS: For this retrospective population-based study, we used linked primary and secondary health records from a representative sample of the UK population provided by the Clinical Practice Research Datalink (CPRD). We identified 93,074 individuals newly diagnosed with heart failure between 2002 and 2014, with a mean age of 76.7 years and of which 49% were women. We examined five indicators of care: (i) diagnosis care setting (inpatient or outpatient), (ii) posthospitalisation follow-up in primary care, (iii) diagnostic investigations, (iv) prescription of essential drugs, and (v) drug treatment dose. We used Poisson and linear regression models to calculate category-specific risk ratios (RRs) or adjusted differences and 95% confidence intervals (CIs), adjusting for year of diagnosis, age, sex, region, and socioeconomic status. From 2002 to 2014, indicators of care presented diverging trends. Outpatient diagnoses and follow-up after hospital discharge in primary care declined substantially (ranging from 56% in 2002 to 36% in 2014, RR 0.64 [0.62, 0.67] and 20% to 14%, RR 0.73 [0.65, 0.82], respectively). Primary care referral for diagnostic investigations and appropriate initiation of beta blockers and angiotensin-converting-enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs) both increased significantly (37% versus 82%, RR 2.24 [2.15, 2.34] and 18% versus 63%, RR 3.48 [2.72, 4.43], respectively). Yet, the average daily dose prescribed remained below guideline recommendations (42% for ACE-Is or ARBs, 29% for beta blockers in 2014) and was largely unchanged beyond the first 30 days after diagnosis. Despite increasing rates of treatment initiation, the overall dose prescribed to patients in the 12 months following diagnosis improved little over the period of study (adjusted difference for the combined dose of beta blocker and ACE-I or ARB: +6% [+2%, +10%]). Women and patients aged over 75 years presented significant gaps across all five indicators of care. Our study was limited by the available clinical information, which did not include exact left ventricular ejection fraction values, investigations performed during hospital admissions, or information about follow-up in community heart failure clinics. CONCLUSIONS: Management of heart failure patients in the UK presents important shortcomings that affect screening, continuity of care, and medication titration and disproportionally impact women and older people. National reporting and incentive schemes confined to individual clinical settings have been insufficient to identify these gaps and address patients' long-term care needs.

Patterns and temporal trends of comorbidity among adult patients with incident cardiovascular disease in the UK between 2000 and 2014: A population-based cohort study.

BACKGROUND: 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). METHODS AND FINDINGS: 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. CONCLUSIONS: 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.

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

BACKGROUND: 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. METHODS AND FINDINGS: 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. CONCLUSIONS: 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.

Age at menarche and risks of coronary heart and other vascular diseases in a large UK cohort.

BACKGROUND: Early menarche has been associated with increased risk of coronary heart disease (CHD), but most studies were relatively small and could not assess risk across a wide range of menarcheal ages; few have examined associations with other vascular diseases. We examined CHD, cerebrovascular disease, and hypertensive disease risks by age at menarche in a large prospective study of UK women. METHODS AND RESULTS: In 1.2 million women (mean±SD age, 56±5 years) without previous heart disease, stroke, or cancer, menarcheal age was reported to be 13 years by 25%, ≤10 years by 4%, and ≥17 years by 1%. After 11.6 years of follow-up, 73 378 women had first hospitalization for or death from CHD, 25 426 from cerebrovascular disease, and 249 426 from hypertensive disease. Using Cox regression, we calculated relative risks for each vascular outcome by single year of menarcheal age. The relationship was U-shaped for CHD. Compared with women with menarche at 13 years, the adjusted relative risk for CHD for menarche at ≤10 years of age was 1.27 (95% confidence interval, 1.22-1.31; P<0.0001) and for menarche at ≥17 years of age was 1.23 (95% confidence interval, 1.16-1.30; P<0.0001). U-shaped relationships were also seen for cerebrovascular and hypertensive disease, although the magnitudes of these risks for early and late menarche were smaller than those for CHD. CONCLUSIONS: In this cohort, the relation of age at menarche to vascular disease risk was U shaped, with both early and late menarche being associated with increased risk. Associations were weaker for cerebrovascular and hypertensive disease than for CHD.

Liver Fat Measured by MR Spectroscopy: Estimate of Imprecision and Relationship with Serum Glycerol, Caeruloplasmin and Non-Esterified Fatty Acids.

Magnetic resonance spectroscopy (MRS) is a non-invasive method for quantitative estimation of liver fat. Knowledge of its imprecision, which comprises biological variability and measurement error, is required to design therapeutic trials with measurement of change. The role of adipocyte lipolysis in ectopic fat accumulation remains unclear. We examined the relationship between liver fat content and indices of lipolysis, and determine whether lipolysis reflects insulin resistance or metabolic liver disease. Imprecision of measurement of liver fat was estimated from duplicate measurements by MRS at one month intervals. Patients provided fasting blood samples and we examined the correlation of liver fat with indices of insulin resistance, lipolysis and metabolic liver disease using Kendall Tau statistics. The coefficient of variation of liver fat content was 14.8%. Liver fat was positively related to serum insulin (T = 0.48, p = 0.042), homeostasis model assessment (HOMA)-B% (T = -0.48, p = 0.042), and body mass index (BMI) (T = 0.59, p = 0.012); and inversely related to HOMA-S% (T = -0.48, p = 0.042), serum glycerol (T = -0.59, p = 0.014), and serum caeruloplasmin (T = 0.055, p = 0.047). Our estimate of total variability in liver fat content (14.8%) is nearly twice that of the reported procedural variability (8.5%). We found that liver fat content was significantly inversely related to serum glycerol but not to non-esterified fatty acids (NEFA), suggesting progressive suppression of lipolysis. Reduction of caeruloplasmin with increasing liver fat may be a consequence or a cause of hepatic steatosis.

Marital status and ischemic heart disease incidence and mortality in women: a large prospective study.

BACKGROUND: Being married has been associated with a lower mortality from ischemic heart disease (IHD) in men, but there is less evidence of an association for women, and it is unclear whether the associations with being married are similar for incident and for fatal IHD. We examined the relation between marital status and IHD incidence and mortality in the Million Women Study. METHODS: A total of 734,626 women (mean age 60 years) without previous heart disease, stroke or cancer, were followed prospectively for hospital admissions and deaths. Adjusted relative risks (RRs) for IHD were calculated using Cox regression in women who were married or living with a partner versus women who were not. The role of 14 socio-economic, lifestyle and other potential confounding factors was investigated. RESULTS: 81% of women reported being married or living with a partner and they were less likely to live in deprived areas, to smoke or be physically inactive, but had a higher alcohol intake than women who were not married or living with a partner. During 8.8 years of follow-up, 30,747 women had a first IHD event (hospital admission or death) and 2,148 died from IHD. Women who were married or living with a partner had a similar risk of a first IHD event as women who were not (RR = 0.99, 95% confidence interval (CI) 0.96 to 1.02), but a significantly lower risk of IHD mortality (RR = 0.72, 95% CI 0.66 to 0.80, P <0.0001). This lower risk of IHD death was evident both in women with and without a prior IHD hospital admission (respectively: RR = 0.72, 95% CI 0.60 to 0.85, P <0.0001, n = 683; and 0.70, 95% CI 0.62 to 0.78, P <0.0001, n = 1,465). These findings did not vary appreciably between women of different socio-economic groups or by lifestyle and other factors. CONCLUSIONS: After adjustment for socioeconomic, lifestyle and other factors, women who were married or living with a partner had a similar risk of developing IHD but a substantially lower IHD mortality compared to women who were not married or living with a partner.

Body mass index and incident coronary heart disease in women: a population-based prospective study.

BACKGROUND: A high body mass index (BMI) is associated with an increased risk of mortality from coronary heart disease (CHD); however, a low BMI may also be associated with an increased mortality risk. There is limited information on the relation of incident CHD risk across a wide range of BMI, particularly in women. We examined the relation between BMI and incident CHD overall and across different risk factors of the disease in the Million Women Study. METHODS: 1.2 million women (mean age=56 years) participants without heart disease, stroke, or cancer (except non-melanoma skin cancer) at baseline (1996 to 2001) were followed prospectively for 9 years on average. Adjusted relative risks and 20-year cumulative incidence from age 55 to 74 years were calculated for CHD using Cox regression. RESULTS: After excluding the first 4 years of follow-up, we found that 32,465 women had a first coronary event (hospitalization or death) during follow-up. The adjusted relative risk for incident CHD per 5 kg/m2 increase in BMI was 1.23 (95% confidence interval (CI) 1.22 to 1.25). The cumulative incidence of CHD from age 55 to 74 years increased progressively with BMI, from 1 in 11 (95% CI 1 in 10 to 12) for BMI of 20 kg/m2, to 1 in 6(95% CI 1 in 5 to 7) for BMI of 34 kg/m2. A 10 kg/m2 increase in BMI conferred a similar risk to a 5-year increment in chronological age. The 20 year cumulative incidence increased with BMI in smokers and non-smokers, alcohol drinkers and non-drinkers, physically active and inactive, and in the upper and lower socioeconomic classes. In contrast to incident disease, the relation between BMI and CHD mortality (n=2,431) was J-shaped. For the less than 20 kg/m2 and ≥35 kg/m2 BMI categories, the respective relative risks were 1.27 (95% CI 1.06 to 1.53) and 2.84 (95% CI 2.51 to 3.21) for CHD deaths, and 0.89 (95% CI 0.83 to 0.94) and 1.85 (95% CI 1.78 to 1.92) for incident CHD. CONCLUSIONS: CHD incidence in women increases progressively with BMI, an association consistently seen in different subgroups. The shape of the relation with BMI differs for incident and fatal disease.

Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk.

OBJECTIVE: In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated. METHODS AND ANALYSIS: In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55-90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK. The association between SBP and risk of cardiovascular end points (composite of ischaemic heart disease, heart failure, stroke and cardiovascular death) was analysed using a deep learning approach. RESULTS: In the selected cohort (46.5% women, median age 69 years), 10 987 cardiovascular events were observed over a median follow-up period of 3.9 years. The association between SBP and risk of cardiovascular end points was found to be monotonic; the lowest SBP exposure group of <120 mm Hg presented nadir of risk. With respect to reference SBP (between 120 and 129 mm Hg), adjusted risk ratios for the primary outcome were 0.99 (95% CI 0.93 to 1.05) for SBP of <120 mm Hg, 1.02 (0.97 to 1.07) for SBP between 130 and 139 mm Hg, 1.07 (1.01 to 1.12) for SBP between 140 and 149 mm Hg, 1.11 (1.05 to 1.17) for SBP between 150 and 159 mm Hg and 1.16 (1.10 to 1.22) for SBP ≥160 mm Hg. CONCLUSION: Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD.