Risk estimation and the prevention of cardiovascular disease : a national clinical guidleine
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We undertook a sensitivity analysis using all patients including those with missing data. Ten-year cardiovascular risk was calculated from each patient's risk factors using the modified Framingham equation advocated in UK guidelines . This method calculates cardiovascular risk as the sum of coronary heart disease risk and stroke risk, multiplying by 1. For each patient, treatment eligibility was determined from their cardiovascular risk, diabetic status, total cholesterol and HDL cholesterol levels. For this analysis, patients were considered to be eligible for lipid lowering drugs they met the relevant criteria in the principal relevant UK guidelines.
In addition, patients were considered eligible for lipid lowering drugs if they had familial hypercholesterolaemia  ,  ,  ,  , . Familial hypercholesterolaemia is poorly coded in electronic primary care records.
Global Risk Assessment to Guide Blood Pressure Management in Cardiovascular Disease Prevention
This definition may overestimate the prevalence of hypercholesterolaemia, as the condition affects 1 in of the population . We first describe prescribing in relation to eligibility under UK clinical guidelines. For subsequent analyses continuous variables were categorised. In the multivariable logistic model, we included all risk factors. We also carried out stratified analyses for patients eligible and not eligible for lipid lowering drugs under UK guidelines in order to investigate the relationship between predictors in the model and patient eligibility.
We used robust standard errors throughout to account for dependency between patients clustered within the same practice. We also undertook secondary analyses to examine the role of variation between practices, because some practices may differ in their overall propensity to prescribe lipid lowering drugs.
International evidence on CVD risk estimation
We performed all analyses using multilevel random intercept logistic regression models with patients nested in practices and with robust standard errors. To determine whether cardiovascular risk factors might have an additional influence on prescribing beyond their contribution to the cardiovascular risk equation, further analysis was undertaken including ten year cardiovascular risk as well as individual risk factors.
There were 1,, patients without clinical evidence of cardiovascular disease who were not on lipid lowering drugs at baseline, after exclusion of those without records of blood pressure, total cholesterol or HDL there were , complete cases for analysis Figure 1.
Table 1 shows the characteristics of the study population divided into those eligible and ineligible for lipid lowering drugs. Overall 6. In total, Therefore In univariable analysis increasing age, diabetic status, prescription of antihypertensive drugs, frequent blood pressure measurements and eligibility for lipid lowering drugs were all strong predictors of treatment.
Eligible patients were more likely to be treated than those not eligible. Table 2. Table 3 Other characteristics, such as male sex, receiving a prescription for antihypertensive drugs, smoking status and family history of premature coronary heart disease were moderately associated with prescribing lipid lowering drugs.
Biomarkers and Cardiovascular Risk Assessment for Primary Prevention: An Update
There was a modest trend towards increased prescribing to patients in more deprived areas. Multivariable analyses were also performed separately on subgroup patients who were eligible and ineligible for lipid lowering drugs Table 4. Predictors of treatment were very similar in eligible and ineligible patients but some characteristics were more strongly associated with prescribing among ineligible patients. Among eligible patients the odds ratio for diabetes was 3. Among ineligible patients the odds ratio for diabetes was 9. Among ineligible patients male sex, receiving a prescription for antihypertensive drugs, smoking status and family history of premature coronary heart disease were slightly more strongly associated with prescribing lipid lowering drugs.
A trend towards increased prescribing to patients from more deprived areas was present in both eligible and ineligible patients. Table 5 Adding year cardiovascular risk to the individual risk factors model made little difference to the odds ratios data not shown.
The analysis was repeated including all 1,, patients. In this analysis, In multivariable analysis the same factors predicted prescribing of lipid lowering drugs and odds ratios for predictors were very similar to those found with the complete case analysis. In the analysis including patients with missing data the predictors of prescribing were similar in eligible and ineligible patients.
Patients with missing blood pressure or cholesterol measurements were much less likely to be prescribed lipid lowering drugs. Over half of patients without cardiovascular disease who were started on lipid lowering therapy were ineligible for treatment. Many eligible patients were not started on treatment. Eligible patients who were non-diabetic and those with infrequent blood pressure measurements were unlikely to be started on treatment.
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The frequency of opportunities to prescribe appears to influence prescribing. We found no evidence of inequitable prescribing, as patients in deprived areas were slightly more likely to be prescribed lipid lowering drugs. However this finding should be treated with caution as deprivation was assessed by postcode of residence and allocated to quintiles. Frequent blood pressure measurements a proxy for cardiovascular related contacts were also associated with treatment. Although all guidelines recommend treatment above a risk threshold and there is universal access to electronic risk calculators in UK primary care, cardiovascular risk was not the main predictor of prescribing.
The patient characteristics associated with prescribing were similar in eligible and ineligible patients. The analysis uses a large dataset of electronic primary care records from across the whole of the UK and is representative of usual clinical care in measurement and recording of risk factors.
We determined predictors of physician rather than patient behaviour as we are unable to identify whether prescribed drugs were collected or taken. Absolute contraindications to lipid lowering drugs are uncommon and are unlikely to influence findings. We have no information on patients' treatment preferences, which are not predictable from patients' age, sex or risk factor status and may not accord with guideline recommendations  , . However there is little evidence that general practitioners take account of patients' preferences when starting preventive treatments .
Our findings concur with previous studies reporting underuse of statins in primary care and greater prescribing of statins in patients with more risk factors  , . We confirmed that total cholesterol level and family history of premature coronary heart disease are predictors of statin prescribing  ,  , . We found no evidence of socioeconomic inequity in prescribing. There is little gradient in statin use across UK civil servants of different grades who were eligible for treatment .
Others found higher statin prescribing in more deprived UK communities . We found diabetes to be a strong predictor of prescribing. Case vignette studies have demonstrated that both UK and Australian GPs are more likely to prescribe statins to eligible diabetics than eligible non-diabetics  , .
LDL cholesterol levels above a threshold have been found to be an important of treatment in eligible patients . Raised LDL cholesterol also predicts treatment in ineligible patients . Our finding of a relationship between prescribing and frequency of consultation is consistent with clinical inertia, a tendency to delay the decision to prescribe until the next visit  , .
As patients aged 30 to 74 consult on average 5. We also confirmed a link between antihypertensive prescribing and statin prescribing  , . We found that most statins are prescribed to patients who are not eligible for treatment. Overtreatment with statins has been reported from the USA, with a majority of those on treatment not meeting eligible under guidelines .
A study at a similar time reported overuse of statins in Norway .
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However guidelines have changed substantially since this time. More recently, overuse of statins has been reported in the Netherlands, where a study reported that most patients on statins for primary prevention were not eligible and in Spain about one third were ineligible  ,  , . The result is that there is a poor match between eligibility for lipid lowering treatment and being prescribed it.
Guidelines and Clinical Documents - American College of Cardiology
Previous studies have shown variation in adherence to guidance in routine clinical practice . Guidelines for assessment and follow up may be impractical . Addressing the patient's primary concern may be a higher priority than prevention . Clinicians and their patients may judge the costs and benefits of treatment differently to guideline authors. The Framingham equation overestimates the risk in populations with low CVD rates, which could justify lower use of statins . Degree of adherence to guidelines may vary by health care centres . Physicians who trained more recently are more likely to be guideline adherent .
As our anonymised data includes no information on general practitioner characteristics, we are unable to investigate the relationship between physician characteristics and prescribing. The cost and cost-effectiveness implications of divergence from statins guidelines may be substantial . Improving guideline compliance therefore has considerable potential to improve the cost effectiveness of prevention. We should investigate whether poor discrimination in prescribing lipid lowering drugs extends to secondary prevention and to antihypertensive prescribing. Our findings are adjusted for the effects of practice, but the role of practice and GP characteristics on guideline adherence requires further analysis.
While analysis of this kind can identify the importance of patient characteristics in influencing prescribing behaviour, it does not explain why or how these patient characteristics exert an influence. Divergence between prescribing behaviour and guidelines may reflect GPs' considered views about the effectiveness or adverse effects of treatment in relation to specific patient characteristics e.
If so these beliefs should be identified and tested against empirical evidence. If divergence between GP prescribing behaviour and guidelines may reflects lack of awareness of existing evidence e. There is now strong evidence for the effectiveness of statins in primary prevention . If the problem is mainly practical — e. GPs only remembering to consider lipid lowering drugs in patients on antihypertensive treatment or having their blood pressure measured the solutions may be practical steps such as electronic reminders. This analysis is therefore a first step in understanding why evidence based clinical guidelines do not translate into prescribing behaviour and represents a model for investigating the prescribing impact of other clinical guidelines.
In order to compare these four models, we first identified the exact definition of each composite endpoint from the original publication describing the development of the model [ 11 — 14 ]. We then, standardized the composite endpoints using ICD codes. This was necessary since the published articles often only described the outcomes in words, e.
The MORGEN cohort is a large Dutch general population cohort which includes men and women aged 20 to 74 years at baseline, recruited from the general population between and [ 15 ].