Long-term cardiovascular risks and statin treatment impact on socioeconomic inequalities: microsimulation model

Background: UK cardiovascular disease (CVD) incidence and mortality have declined in recent decades but socioeconomic inequalities persist. Aims: We present a new CVD model and project health outcomes and impact of guideline-recommended statin treatment across quintiles of socioeconomic deprivation in UK. Design and Setting: Lifetime microsimulation model developed using 117,896 participants in 16 statin trials and 501,854 UK Biobank (UKB) participants and quality of life data from national health surveys. Method: We developed a CVD microsimulation model using risk equations for myocardial infarction, stroke, coronary revascularisation, cancer, vascular and nonvascular death, estimated using trial data. We calibrated and further developed this model in the UKB cohort, including further characteristics and a diabetes risk equation, and validated the model in UKB and Whitehall II cohorts. We used the model to predict CVD incidence, life expectancy, quality-adjusted life years (QALYs) and impact of UK guideline-recommended statin treatment across quintiles of socioeconomic deprivation. Results: Age, sex, socioeconomic deprivation, smoking, hypertension, diabetes and cardiovascular events were key CVD risk determinants. Model-predicted event rates corresponded well to observed rates across participant categories. The model projected strong gradients in remaining life expectancy, with 4-to-5 years (5-to-8 QALYs) gaps between the least and most socioeconomically deprived quintiles. Guideline-recommended statin treatment was projected to increase QALYs with larger gains in quintiles of higher deprivation. Conclusions: The study demonstrated the potential of guideline-recommended statin treatment to reduce socioeconomic inequalities. This CVD model is a novel resource for individualised long-term projections of health outcomes and effects of CVD treatments

Long-term monitoring in primary care for chronic kidney disease and chronic heart failure: a multi-method research programme

Background: Long-term monitoring is important in chronic condition management. Despite considerable costs of monitoring, there is no or poor evidence on how, what and when to monitor. The aim of this study was to improve understanding, methods, evidence base and practice of clinical monitoring in primary care, focusing on two areas: chronic kidney disease and chronic heart failure. Objectives: The research questions were as follows: does the choice of test affect better care while being affordable to the NHS? Can the number of tests used to manage individuals with early-stage kidney disease, and hence the costs, be reduced? Is it possible to monitor heart failure using a simple blood test? Can this be done using a rapid test in a general practitioner consultation? Would changes in the management of these conditions be acceptable to patients and carers? Design: Various study designs were employed, including cohort, feasibility study, Clinical Practice Research Datalink analysis, seven systematic reviews, two qualitative studies, one cost-effectiveness analysis and one cost recommendation. Setting: This study was set in UK primary care. Data sources: Data were collected from study participants and sourced from UK general practice and hospital electronic health records, and worldwide literature. Participant: The participants were NHS patients (Clinical Practice Research Datalink: 4.5 million patients), chronic kidney disease and chronic heart failure patients managed in primary care (including 750 participants in the cohort study) and primary care health professionals. Interventions: The interventions were monitoring with blood and urine tests (for chronic kidney disease) and monitoring with blood tests and weight measurement (for chronic heart failure). Main outcome measures: The main outcomes were the frequency, accuracy, utility, acceptability, costs and cost-effectiveness of monitoring. Results: Chronic kidney disease: serum creatinine testing has increased steadily since 1997, with most results being normal (83% in 2013). Increases in tests of creatinine and proteinuria correspond to their introduction as indicators in the Quality and Outcomes Framework. The Chronic Kidney Disease Epidemiology Collaboration equation had 2.7% greater accuracy (95% confidence interval 1.6% to 3.8%) than the Modification of Diet in Renal Disease equation for estimating glomerular filtration rate. Estimated annual transition rates to the next chronic kidney disease stage are ≈ 2% for people with normal urine albumin, 3–5% for people with microalbuminuria (3–30 mg/mmol) and 3–12% for people with macroalbuminuria (> 30 mg/mmol). Variability in estimated glomerular filtration rate-creatinine leads to misclassification of chronic kidney disease stage in 12–15% of tests in primary care. Glycaemic-control and lipid-modifying drugs are associated with a 6% (95% confidence interval 2% to 10%) and 4% (95% confidence interval 0% to 8%) improvement in renal function, respectively. Neither estimated glomerular filtration rate-creatinine nor estimated glomerular filtration rate-Cystatin C have utility in predicting rate of kidney function change. Patients viewed phrases such as ‘kidney damage’ or ‘kidney failure’ as frightening, and the term ‘chronic’ was misinterpreted as serious. Diagnosis of asymptomatic conditions (chronic kidney disease) was difficult to understand, and primary care professionals often did not use ‘chronic kidney disease’ when managing patients at early stages. General practitioners relied on Clinical Commissioning Group or Quality and Outcomes Framework alerts rather than National Institute for Health and Care Excellence guidance for information. Cost-effectiveness modelling did not demonstrate a tangible benefit of monitoring kidney function to guide preventative treatments, except for individuals with an estimated glomerular filtration rate of 60–90 ml/minute/1.73 m2, aged < 70 years and without cardiovascular disease, where monitoring every 3–4 years to guide cardiovascular prevention may be cost-effective. Chronic heart failure: natriuretic peptide-guided treatment could reduce all-cause mortality by 13% and heart failure admission by 20%. Implementing natriuretic peptide-guided treatment is likely to require predefined protocols, stringent natriuretic peptide targets, relative targets and being located in a specialist heart failure setting. Remote monitoring can reduce all-cause mortality and heart failure hospitalisation, and could improve quality of life. Diagnostic accuracy of point-of-care N-terminal prohormone of B-type natriuretic peptide (sensitivity, 0.99; specificity, 0.60) was better than point-of-care B-type natriuretic peptide (sensitivity, 0.95; specificity, 0.57). Within-person variation estimates for B-type natriuretic peptide and weight were as follows: coefficient of variation, 46% and coefficient of variation, 1.2%, respectively. Point-of-care N-terminal prohormone of B-type natriuretic peptide within-person variability over 12 months was 881 pg/ml (95% confidence interval 380 to 1382 pg/ml), whereas between-person variability was 1972 pg/ml (95% confidence interval 1525 to 2791 pg/ml). For individuals, monitoring provided reassurance; future changes, such as increased testing, would be acceptable. Point-of-care testing in general practice surgeries was perceived positively, reducing waiting time and anxiety. Community heart failure nurses had greater knowledge of National Institute for Health and Care Excellence guidance than general practitioners and practice nurses. Health-care professionals believed that the cost of natriuretic peptide tests in routine monitoring would outweigh potential benefits. The review of cost-effectiveness studies suggests that natriuretic peptide-guided treatment is cost-effective in specialist settings, but with no evidence for its value in primary care settings. Limitations: No randomised controlled trial evidence was generated. The pathways to the benefit of monitoring chronic kidney disease were unclear. Conclusions: It is difficult to ascribe quantifiable benefits to monitoring chronic kidney disease, because monitoring is unlikely to change treatment, especially in chronic kidney disease stages G3 and G4. New approaches to monitoring chronic heart failure, such as point-of-care natriuretic peptide tests in general practice, show promise if high within-test variability can be overcome

PomBase – the scientific resource for fission yeast

The fission yeast Schizosaccharomyces pombe has become well established as a model species for studying conserved cell-level biological processes, especially the mechanics and regulation of cell division. PomBase integrates the S. pombe genome sequence with traditional genetic, molecular and cell biological experimental data as well as the growing body of large datasets generated by emerging high-throughput methods. This chapter provides insight into the curation philosophy and data organization at PomBase, and provides a guide to using PomBase for infrequent visitors and anyone considering exploring S. pombe in their research

The impact of social disadvantage in moderate-to-severe chronic kidney disease: an equity-focused systematic review.

It is unclear whether a social gradient in health outcomes exists for people with moderate-to-severe chronic kidney disease (CKD). We critically review the literature for evidence of social gradients in health and investigate the 'suitability' of statistical analyses in the primary studies. In this equity-focused systematic review among adults with moderate-to-severe CKD, factors of disadvantage included gender, race/ethnicity, religion, education, socio-economic status or social capital, occupation and place of residence. Outcomes included access to healthcare, kidney disease progression, cardiovascular events, all-cause mortality and suitability of analyses. Twenty-four studies in the pre-dialysis population and 34 in the dialysis population representing 8.9 million people from 10 countries were included. In methodologically suitable studies among pre-dialysis patients, a significant social gradient was observed in access to healthcare for those with no health insurance and no home ownership. Low income and no home ownership were associated with higher cardiovascular event rates and higher mortality [HR 1.94, 95% confidence interval (CI) 1.27-2.98; HR 1.28, 95% CI 1.04-1.58], respectively. In methodologically suitable studies among dialysis patients, females, ethnic minorities, those with low education, no health insurance, low occupational level or no home ownership were significantly less likely to access cardiovascular healthcare than their more advantaged dialysis counterparts. Low education level and geographic remoteness were associated with higher cardiovascular event rates and higher mortality (HR 1.54, 95% CI 1.01-2.35; HR 1.21, 95% CI 1.08-1.37), respectively. Socially disadvantaged pre-dialysis and dialysis patients experience poorer access to specialist cardiovascular health services, and higher rates of cardiovascular events and mortality than their more advantaged counterparts

The impact of social disadvantage in moderate-to-severe chronic kidney disease: an equity-focused systematic review.

It is unclear whether a social gradient in health outcomes exists for people with moderate-to-severe chronic kidney disease (CKD). We critically review the literature for evidence of social gradients in health and investigate the 'suitability' of statistical analyses in the primary studies. In this equity-focused systematic review among adults with moderate-to-severe CKD, factors of disadvantage included gender, race/ethnicity, religion, education, socio-economic status or social capital, occupation and place of residence. Outcomes included access to healthcare, kidney disease progression, cardiovascular events, all-cause mortality and suitability of analyses. Twenty-four studies in the pre-dialysis population and 34 in the dialysis population representing 8.9 million people from 10 countries were included. In methodologically suitable studies among pre-dialysis patients, a significant social gradient was observed in access to healthcare for those with no health insurance and no home ownership. Low income and no home ownership were associated with higher cardiovascular event rates and higher mortality [HR 1.94, 95% confidence interval (CI) 1.27-2.98; HR 1.28, 95% CI 1.04-1.58], respectively. In methodologically suitable studies among dialysis patients, females, ethnic minorities, those with low education, no health insurance, low occupational level or no home ownership were significantly less likely to access cardiovascular healthcare than their more advantaged dialysis counterparts. Low education level and geographic remoteness were associated with higher cardiovascular event rates and higher mortality (HR 1.54, 95% CI 1.01-2.35; HR 1.21, 95% CI 1.08-1.37), respectively. Socially disadvantaged pre-dialysis and dialysis patients experience poorer access to specialist cardiovascular health services, and higher rates of cardiovascular events and mortality than their more advantaged counterparts

Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis

Background: Evidence for kidney function monitoring intervals in primary care is weak, and based mainly on expert opinion. In the absence of trials of monitoring strategies, an approach combining a model for the natural history of kidney function over time combined with a cost-effectiveness analysis offers the most feasible approach for comparing the effects of monitoring under a variety of policies. This study aimed to create a model for kidney disease progression using routinely collected measures of kidney function. Methods: This is an open cohort study of patients aged ≥18 years, registered at 643 UK general practices contributing to the Clinical Practice Research Datalink between 1 April 2005 and 31 March 2014. At study entry, no patients were kidney transplant donors or recipients, pregnant or on dialysis. Hidden Markov models for estimated glomerular filtration rate (eGFR) stage progression were fitted to four patient cohorts defined by baseline albuminuria stage; adjusted for sex, history of heart failure, cancer, hypertension and diabetes, annually updated for age. Results: Of 1,973,068 patients, 1,921,949 had no recorded urine albumin at baseline, 37,947 had normoalbuminuria (andgt;3mg/mmol), 10,248 had microalbuminuria (3–30mg/mmol), and 2,924 had macroalbuminuria (andgt;30mg/mmol). Estimated annual transition probabilities were 0.75–1.3%, 1.5–2.5%, 3.4–5.4% and 3.1–11.9% for each cohort, respectively. Misclassification of eGFR stage was estimated to occur in 12.1% (95%CI: 11.9–12.2%) to 14.7% (95%CI: 14.1–15.3%) of tests. Male gender, cancer, heart failure and age were independently associated with declining renal function, whereas the impact of raised blood pressure and glucose on renal function was entirely predicted by albuminuria. Conclusions: True kidney function deteriorates slowly over time, declining more sharply with elevated urine albumin, increasing age, heart failure, cancer and male gender. Consecutive eGFR measurements should be interpreted with caution as observed improvement or deterioration may be due to misclassification.</ns4:p

Statistical models for the deterioration of kidney function in a primary care population: a retrospective database analysis

Background: Evidence for kidney function monitoring intervals in primary care is weak, and based mainly on expert opinion. In the absence of trials of monitoring strategies, an approach combining a model for the natural history of kidney function over time combined with a cost-effectiveness analysis offers the most feasible approach for comparing the effects of monitoring under a variety of policies. This study aimed to create a model for kidney disease progression using routinely collected measures of kidney function. Methods: This is an open cohort study of patients aged ≥18 years, registered at 643 UK general practices contributing to the Clinical Practice Research Datalink between 1 April 2005 and 31 March 2014. At study entry, no patients were kidney transplant donors or recipients, pregnant or on dialysis. Hidden Markov models for estimated glomerular filtration rate (eGFR) stage progression were fitted to four patient cohorts defined by baseline albuminuria stage; adjusted for sex, history of heart failure, cancer, hypertension and diabetes, annually updated for age. Results: Of 1,973,068 patients, 1,921,949 had no recorded urine albumin at baseline, 37,947 had normoalbuminuria (&gt;3mg/mmol), 10,248 had microalbuminuria (3–30mg/mmol), and 2,924 had macroalbuminuria (&gt;30mg/mmol). Estimated annual transition probabilities were 0.75–1.3%, 1.5–2.5%, 3.4–5.4% and 3.1–11.9% for each cohort, respectively. Misclassification of eGFR stage was estimated to occur in 12.1% (95%CI: 11.9–12.2%) to 14.7% (95%CI: 14.1–15.3%) of tests. Male gender, cancer, heart failure and age were independently associated with declining renal function, whereas the impact of raised blood pressure and glucose on renal function was entirely predicted by albuminuria. Conclusions: True kidney function deteriorates slowly over time, declining more sharply with elevated urine albumin, increasing age, heart failure, cancer and male gender. Consecutive eGFR measurements should be interpreted with caution as observed improvement or deterioration may be due to misclassification.</ns4:p

A model of lifetime health outcomes in cardiovascular disease based on clinical trials and large cohorts

Abstract Background and purpose Cardiovascular disease (CVD) risk of individuals depends on their socio-demographic characteristics, clinical risk factors, and treatments, and strongly influences their quality of life and survival. Individual-based long-term disease models, which aim to more accurately calculate the lifetime consequences, can help to target treatments, develop disease management programmes, and assess the value of new therapies. We present a new micro-simulation CVD model. Methods This micro-simulation model was developed using individual participant data from the Cholesterol Treatment Trialists' collaboration (CTT: 118,000 participants; 15 trials) and calibrated (with added socioeconomic deprivation, ethnicity, physical activity, mental illness, cancer and incident diabetes) in the UK Biobank cohort (UKB: 502,000 participants). Parametric survival models estimated risks of key endpoints (myocardial infarction (MI), stroke, coronary revascularisation (CRV), diabetes, cancer and vascular (VD) and nonvascular death (NVD) using participants' age, sex, ethnicity, physical activity, socioeconomic deprivation, smoking history, lipids, blood pressure, creatinine, previous cardiovascular diseases, diabetes, mental illness and cancer at entry and non-fatal incidents of the key endpoints during follow-up. The model integrates the risk equations and enables annual projection of endpoints and survival over individuals' lifetimes. The model was used to project remaining life expectancy across UK Biobank participants. Results Nonfatal cardiovascular events and age were the major determinants of CVD risk and, together with incident diabetes and cancer, of individuals' survival. The cumulative incidence of the key endpoints predicted by the CTT-UKB model corresponded well to their observed incidence in the UK Biobank cohort, overall (Figure 1) and in categories of participants by age, sex, prior CVD and CVD risk. Predicted remaining life expectancy across UK Biobank participants without history of CVD ranged between 22 and 43 years in men and between 24 and 46 years in women, depending on their age and CVD risk (Figure 2). Among UK Biobank participants with history of CVD, depending on their age, predicted remaining life expectancy ranged from 20 to 32 years in men and from 26 to 38 years in women. Conclusion This new lifetime CVD model accurately predicts morbidity and mortality in a large UK population cohort. It will be made available to provide individualised projections of expected lifetime health outcomes and benefits of treatments. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), British Heart Foundation Figure 1. Predicted (in black) versus observed (95% CI; in red) incidence of major clinical outcomes in the UK Biobank. Figure 2. Predicted remaining life expectancy of participants in UK Biobank cohort, by age and CVD risk or previous CVD at entry. QRISK, a 10-year CVD risk scoring algorithm for people without previous CVD, recommended for use in the UK National Health Service. </jats:sec