The effectiveness of quitting smoking in coronary heart disease

The effectiveness of quitting smoking in coronary heart disease

This category includes 4 primary interventions: smoking cessation, control of dyslipidemia (DLP) and blood pressure, as well as prophylactic administration of drugs by individual patients. Each intervention should be seriously discussed with patients with CVD, diabetes, as well as with patients suitable for primary prevention. This discussion should be documented.

a) prevalence. In the US, cigarette consumption in terms of 1 person. grew dramatically in the first half of the twentieth century. By 1945,> 65% of men born in 1911-1920 smoked. The annual per capita consumption of cigarettes in 1963 reached 4,286 (> 200 packs per year), but has since dropped to 1875. The prevalence of smoking among men reached a peak in 1955, when> 50% of men smoked; in women, the peak came 10 years later.

Since then, the prevalence of smoking among Americans has decreased significantly (to 21%). Currently, 23% of men and 18% of women are smokers in the population> 18 years old. The frequency of smoking among older schoolchildren increased from 30% in the mid-1980s. to 36.5% in 1997, moreover at the expense of girls, but now it is gradually decreasing. The frequency of smoking is higher among people with low socioeconomic status and educational qualifications.

b) Associated risk of smoking. Smoking increases the risk of coronary heart disease (CHD). By the middle of the XX century. The first studies that linked smoking to heart disease were published.

A 1964 Surgeon General’s report confirmed this epidemiological link, and in 1983 Surgeon General’s firmly named cigarette smoking as the main preventive cause of cardiovascular diseases (CVD). The 1989 Surgeon General’s report provided accurate data from case-control observational studies and cohort studies, mostly among men. It is established that smoking increases the frequency of CHD by 2 times and by 50% the mortality from CHD, and this risk increases with age and the number of cigarettes smoked. A similar increase in the relative risk of KBS is observed among women.

In the US, cigarette smoking is the leading modifiable cause of deaths (438 thousand deaths annually, of which 35% is cardiovascular death) and losses> 5 million years of life. Worldwide, the frequency of smoking continues to grow, with the most in developing countries; in 2000, deaths from tobacco smoking were more than 1 million compared with 1990.

c) The benefits of stopping smoking. Evidence from large-scale, randomized, risk-reduction trials of smoking cessation is limited, but observational studies have shown a clear benefit of stopping smoking. People who quit smoking reduce the excess risk of coronary events during the first 2 years by 50%, with the greatest benefit observed in the first few months. This period is followed by a gradual decrease, and after 3-5 years the risk among those who smoked earlier approaches the risk of those who have never smoked.

Coronary heart disease as a multifactorial disease

Coronary heart disease as a multifactorial disease

Knowledge of the biological mechanisms of atherosclerosis led to an understanding of the progression of this disease and many of its etiological factors. Coronary heart disease (CHD) develops as a result of the interaction of many RFs.

Often, people with cardiovascular diseases (CVD) simultaneously have small changes in several risk factors (RF), rather than extreme deviations in the level of one. If these risk factors (RF) are not controlled, then atherosclerosis will continue to develop. Predisposing RF, for example, genetic, interact with behavioral RF, such as diet, alcohol consumption and physical activity.

The combination of these predisposing and behavioral factors can cause metabolic changes – dyslipidemia (DLP), hypertension, obesity and diabetes, which ultimately lead to a pronounced disease. The key point in identifying susceptibility to this disease may be various diagnostic tests. Some tests that assess physical changes in the arteries, such as the thickness of the TCIM CA or the presence of calcium in the CA, can be used to assess the severity of atherosclerosis, while an exercise test helps diagnose stenosis of the ischemia.

Other tests, such as determining the level of chrish, suggest the likelihood of a cardiovascular event (SSSob), but may not clearly correlate with the severity of atherosclerosis. This latent disease can manifest as a transient ischemic attack (TIA), MI, or another Ssob. DFs useful for prediction, both modifiable and non-modifiable, can be identified at any stage of this process. For example, FRs are hereditary predispositions for heart disease and smoking. Such a metabolic factor, such as a high level of LDL, also serves as a risk factor.

Migrated IM or MI is also a strong predictor of future events. This reflects the relationship between predisposing risk factors (RF) and disease markers, which may be important for identifying people at increased risk of developing clinical complications.

Risk assessment (RF) is useful, but sometimes they are conditional, i.e. they are difficult to classify as a specific category. For example, hereditary predisposition may be in the presence of certain genes in the descendants, but also in the lifestyle of the family, which passes from one generation to another.

It is not known whether hypertension is a consequence of the influence of such features of a family lifestyle as a diet predisposing to atherosclerosis, low physical activity (NFA), or is it a manifestation of ED, thus serving as a marker of atherosclerosis.

Risk assessment of coronary heart disease (CHD) in the office

Risk assessment of coronary heart disease (CHD) in the office

Clinicians can easily classify in their patients the long-term risk of coronary heart disease (CHD) and other cardiovascular events (SSSob) as very high, high, intermediate or low based on answers to a few simple questions and manipulations, such as measuring blood pressure. An algorithm was developed that allows to classify patients according to their total cardiovascular risk (SSR).

The patient’s cardiovascular status is the first issue of the algorithm. If the patient has cardiovascular disease (CVD), then it is necessary to determine whether it is stable. If yes, then further classification is not necessary, because this patient by definition has a very high long-term risk and needs an “aggressive” modification of a risk factor (RF).

The presence of instability, such as unstable angina (NS), indicates a high short-term risk. Unstable patients need immediate referral for appropriate diagnostic studies and interventions. Even after the stabilization of the debt state, the acute risk of these patients is very high.

If the patient does not have an established CVD, but there are worrisome symptoms (for example, recurring chest pain, suggesting CVD or another CVD), then this patient also needs to be evaluated using appropriate diagnostic tests to detect high short-term risk. If CVD is diagnosed, the patient should undergo further examination, for example, catheterization or other intervention (if necessary), and further should be assigned to a very high risk group. If the results of this examination are negative, the patient is returned to the primary prevention group for risk assessment.

For those who do not have CVD, the key point is the presence of diabetes. If diabetes is diagnosed, then the long-term risk is high and the patient should be classified as a high-risk group for primary prevention. According to the algorithm, patients without obvious CVD or diabetes must undergo a test to determine the 10-year risk of CVD using a simple prognostic tool, such as the Framingham Risk Scale or another similar scale.

Patients with very high Framingham Risk Scores (CSD risk for 10 years> 20%) are also at high risk. On the other side of the spectrum are patients whose risk factors (DF) are <2 or the Framingham risk score is <5%. These patients are classified as low risk.

According to the algorithm, for patients who fall into the intermediate group or are between high and intermediate risk (10-year risk is 5-15%), clinicians may collect additional information for better stratification: conduct a secondary screening if it is unclear how “aggressive” there should be an intervention aimed at FR. The next step for such patients is to determine the level of CRP, and a more expensive and complicated method may be a test for TFN or EDT to determine the calcium content in the coronary arteries (DND and USPSTF approved the prescription of the FN test for a prevention decision).

Each of these tests provides additional information to the Framingham Risk Scale. CRP may be helpful, but it is unlikely that intervention will be needed, the significance of which in asymptomatic patients is unknown. The end result will be a more accurate risk assessment and the assignment of the patient to the relevant group (high, intermediate or low risk). Such a general risk classification is useful in choosing an intervention and its intensity.

Risk factors

Risk factors

In 2003, the New Zealand Guidelines Group was updated, creating tables that estimate the 5-year total SSR based on age, gender, ethnic group (Maori or non-Maori), smoking status, lipid levels, glucose and blood pressure (Fig. 45- five). Patients are stratified as having a very high, high, moderate, and low risk. Patients with established CVD, genetic lipid disorders, diabetes, or kidney disease constitute a very high risk group because have a> 20% risk of a follow-up SWS for 5 years. “Aggressive” intervention is recommended for patients with SSR> 15%.

There are small differences between different prognostic scales, they classify patients equally and are an inexpensive initial method of risk assessment. Life-long risk is an important method of risk-reflecting, especially for the young, for whom an early lifestyle change is essential.

Diagnostic tests and new biochemical markers designed for patients with specific complaints can enhance the predictive capabilities of simple scales. Such diagnostic tests are the determination of calcium mass by CRT and the exercise tolerance test (TFN). EchoCG and cardiac catheterization can provide additional prognostic information, but these tests are quite expensive and have potential harmful effects, and their use in prevention remains controversial.

A marker of inflammation CRP may increase the prognostic value of initial screening.

The Women’s Health Study recently showed that adding such simple variables as family history, HbA1C levels in diabetes and CRP, allows almost 50% of women in the intermediate-risk group to qualify for a higher or lower group. This reclassification turned out to be correct in almost all cases, at least according to comparison with the traditional risk criteria ATP III.

Thus, improved risk prediction algorithms should help ensure that preventive treatment, based on both lifestyle changes and drug therapy, is correctly applied in the appropriate group of patients.

General and individual risk scales

General and individual risk scales

Due to the fact that many predictors of risk correlate with each other, risk can often be predicted based on information about several risk factors (RF). In most cases, many risk factors (RF) can be identified during initial screening, but it is sufficient to identify several easily measurable risk factors (RF) to calculate the overall risk for coronary heart disease (CHD).

For those who, at the initial screening, the risk is very low or very high, the measurement and evaluation of additional risk factors will give very little useful information, i.e. additional screening will add valuable information only in patients with intermediate risk. Evaluating individual absolute risk will allow you to select cost-effective intervention.

As evidence of the importance of assessing individual risk, NCEP, ATP III, JNC-7, and USPSTF suggested several options for assessing individual risk to determine the intensity of various interventions. The American Diabetes Association also recommends an absolute risk based treatment approach.

Usually, the presence or absence of cardiovascular disease (CVD) is sufficient for the distribution of patients with high or low risk. Patients with established CVD, such as damage to the coronary artery, cerebrovascular or peripheral arteries, constitute the first high-risk group. They always have a higher average risk than those without CVD. Approximately 80% of patients with established CVD will die from this disease, while among those without an established CVD, the death rate will be only 50% of the mortality rates from CVD.

As discussed later in this chapter, people with CVD usually need more “aggressive” interventions. Reducing the risk in these patients refers to secondary prevention, and among those without obvious CVD to the primary.

Patients with diabetes

Patients with diabetes

Patients with diabetes constitute the second high-risk group. The frequency of cardiovascular events (SSSob) and mortality among patients with diabetes is much higher than in the general population, so these patients need “aggressive” preventive interventions. The third group of patients who are at high risk for SCSS and death are patients with CKD, many of whom suffer from diabetes.

For patients without CVD and diabetes, several risk determination strategies based on risk factors (RF) have been developed. Early versions of some manuals recommended a simple calculation of DF. The Framingham Heart Study researchers have developed a handy tool for assessing the risk of a first SSSob, taking into account age, gender, cholesterol, LDL, LDL, GL, DAD, diabetes, and smoking. Points are assigned if and depending on the level of each FR.

After the summation of points, the absolute risk of coronary heart disease (CHD) is assessed over the next 10 years. The National Heart, Lung, and Blood Institute posted an affordable online 10-year risk calculator. Researchers at the Framingham Heart Study also developed scales for determining the risk of secondary prevention of MI and MI. However, due to the fact that patients with CVDs already have a high risk of recurrent CVDs and need “aggressive” prevention, the benefits of this tool remain unclear.

There are several alternatives to the Framingham risk scale. The HeartScore project (Heart Systematic Coronary Risk Evaluation) was created by a European working group based on cohort studies involving> 200 thousand people in 12 European countries.

HeartScore replaced the earlier risk stratification patterns common to the European Society of Cardiology, and shifted the focus from warning KBS to warning CVD. On the basis of age, sex, SAD, CHF, or HCV HDL ratio, HeartScore calculates the 10-year risk of death from CVD, rather than the risk of individual cardiovascular events (SSSob). SD in this scale was not included, because he was not studied in the cohorts used to create the scale. For patients with a 10-year risk of fatal events> 5%, aggressive intervention is recommended.

Another risk assessment tool was created based on the PROCAM study, which for a long time (from 1979 to 1985) monitored for> 5 thousand men aged 35–65 years. In the PROCAM FR algorithm, there were smoking, GARDEN, LDL CH and HDL cholesterol, fasting TG, as well as diabetes, MI in the family history and age. Answers in points to questions regarding these DFs are summarized, as in the Framingham risk scale, and the 10-year absolute risk of fatal or nonfatal IM BCC is determined by the results.

Risk assessment at individual level

Risk assessment at individual level

In contrast to community-based public health interventions, clinicians are responsible for preventive advice for a particular patient. An important step in determining an individual preventive strategy is to assess the risk of developing a clinically significant outcome, because indicators of the cost-effectiveness ratio of any intervention vary depending on the overall risk of the individual or population.

Since the absolute risk in people with an established disease is high, in order to save one life or prevent one cardiovascular event (SSSob) among them, fewer people with high risk should be treated compared with the number of people with lower risk, even if the decrease in relative risk is the same in both groups.

To illustrate this point, suppose that intervention reduces mortality by 25% in primary and secondary prevention, then a high-risk patient with coronary heart disease (CHD) has a chance to die from cardiovascular disease (CVD) over the next 10 years equal to 20%, while in a patient with a low risk, the chance of dying for the same period is 1%.

To save one life among high-risk individuals, only 20 patients need to be treated for 10 years, and 4 of them will die. Thus, reducing the relative risk by 25% will save one life (3 death instead of 4). In the case of a low risk, so that a 25% reduction in relative risk would lead to 3 deaths instead of 4, 400 people will have to be treated, of whom 4 will also die.

Thus, the total cost of one life saved is significantly lower (1/20 of the cost) among those with a high absolute risk. Costly interventions are usually cost-effective only for high-risk individuals, but with a low intervention cost, the cost-effectiveness relationship can be cost-effective even in low-risk populations.

When forecasting a risk, the “ideal” risk factor (DF) is one whose prevalence prevails in a population that can be easily and safely measured and which has a great predictive value.

The measurement should be inexpensive because cost is a major constraint on the use of imaging techniques such as electron beam tomography (CRT) or MRI. In addition, the frequency of false positives should be low to avoid unnecessary and potentially dangerous consequences. Age and gender are examples of non-modifiable risk factors (RF) that meet these criteria.

Blood pressure and smoking are examples of modifiable risk factors (RF) that are easy to identify. The results of diagnostic tests can also serve as predictors of future cardiovascular events (SSSob).

Risk factor assessment at the population level

Risk factor assessment at the population level

To conduct sound public policy, it is necessary to assess the contribution of various risk factors (RF) at the population level. The population risk depends not only on the strength of the factor-disease association and the benefits of the intervention, but also on how widespread this risk factor (RF) is in the population.

For evaluation, indicators such as the frequency of new cases, prevalence, and population attributive risk are used. The frequency of new cases is the emergence of new cases of disease or risk factors (RF) for a certain period of time; prevalence is the proportion of people with a specific disease or risk factor (RF) at a given time.

The population attributive risk shows what amount of risk is caused by a given risk factor (RF), and depends on the proportion of people with this risk factor and on the magnitude of the associated risk.

Population attributive risk also reflects the relationship between exposure to a risk factor (RF) and a disease. Many factors linearly increase risk, so a population attributable risk can be calculated relative to the ideal standard or to an individual with a low risk.

For example, the relationship between hypertension and heart disease or cerebral stroke (MI) is linear, so a decrease in blood pressure at any elevated level reduces the risk. In contrast, the shape of the risk curve for obesity is not linear, the risk increases logarithmically, i.e. Each kilogram gained is associated with a higher risk for those who are already overweight.

Population attributive risk is an important criterion in determining the resources needed for carrying out various preventive interventions and determining priorities in measures to improve public health, such as anti-smoking campaigns. However, this chapter focuses on individual risk assessment for predicting future cardiovascular events (SSSob).

The use of risk factors in clinical practice

The use of risk factors in clinical practice

Regardless of how risk factors (RF) affect the progression of atherosclerosis, they can be divided into 2 broad categories depending on their use in clinical practice:

(1) factors that are useful for risk prediction (risk predictors);
(2) factors that are targets for risk reduction interventions.

Such risk factors (RF), such as smoking and blood pressure, fall into both categories. Even if a particular factor has predictive value, it cannot be argued that modifying it will reduce the risk. If the benefit of an intervention is substantially greater than any of its risks and costs, then the intervention should be used in the appropriate population. So, how do you decide which risk factor (DF) to use as a predictor of risk and what will be the target for risk reduction?

The approaches to using a risk factor (RF) for predicting or reducing risk will be defined below. This article discusses only those risk factors (RF) that affect intermediate or long-term risk. Interventions used to quickly reduce short-term risk, such as aspirin or thrombolysis in acute myocardial infarction (AMI).

Forecasting and risk assessment. Risk prediction can be applied both to the population as a whole and to the individual. Information about the population can be obtained by studying a representative population sample in order to establish the frequency of various risk factors (RF) and plan public health objectives and resources for screening programs.

Individual risk assessment is carried out in order to identify in the population of a separate part of individuals who need a more intensive risk reduction program.

The articles on the site will briefly describe the assessment of the risk factor (RF) and the frequency of events in the general population, and further – a detailed assessment of the individual risk.

Types of evidence on risk factors

Types of evidence on risk factors

Evidence on risk factors (RF) is obtained from various sources. Studies on autopsy have shown that atherosclerosis can begin to develop even at an early age, if there are the same RF CVDs as in adults. Establishing a link between cause and effect is a major step in determining predictors, and the results of several studies are needed to select a preventive intervention. Fundamental studies of human physiology made it possible to penetrate into the mechanisms of atherogenesis and helped to establish the biological probability of a potential intervention in order to change these effects.

Observational studies involving people (cohort, prospective, case-control) are extremely useful in determining the attributive risk of a particular factor. Randomized trials can help confirm a causal relationship and are necessary for choosing interventions to reduce risk.

Each of these strategies has strengths and weaknesses. Descriptive studies (for example, the description of a single observation, a series of observations, cross-sectional, cross-cultural studies, the study of population temporal trends) have considerable value because of the ability to generate hypotheses. However, their design does not adequately control potential factors that may obscure obvious associations. Observational studies (eg, cohort, prospective, case-control) can better control potential inaccuracies.

Observational studies are particularly important in determining the attributable risk of a particular factor, when this factor has a great effect, as in the case of smoking and lung cancer. However, when small or moderate effects are studied in observational studies, the number of uncontrollable distorting factors can be as great as the probable risk itself.

In such cases, randomized studies are needed to confirm causality. When the causal relationship between RF and the disease is confirmed, appropriate intervention should be selected and applied. Even if the causal relationship is beyond doubt, research will help quantify the effect of the intervention. When the question arises about the choice between risk and benefit of intervention, randomized studies are needed to determine its net clinical effect.

This provision is important because the degree of associated risk is not necessarily related to the magnitude of the benefits obtained as a result of the intervention. This lack of correlation may be due to the inability of a specific intervention to achieve the desired effect, or the magnitude of the change may not lead to a corresponding change in risk. An example is the difference between the risk of an increase in blood pressure pa 1 mm Hg. st. and less than expected benefit for CHD while reducing blood pressure by the same amount. Similarly, elevated Gmc is considered to be FR KBS, and folic acid reduces Gmc levels, but randomized studies have shown that lowering Gmc levels with folic acid does not reduce the risk of KBS.

Meta-analysis allows a better assessment of the risk associated with a specific risk factor (RF), or the benefit of an intervention. For example, an assessment of the benefits of aspirin in secondary prophylaxis was obtained as a result of a large meta-analysis of data from 300 clinical studies, which demonstrated that in patients with CVD, aspirin reduces the risk of major SSSob by 25%.

After obtaining acceptable assessments of the benefits and risks for a specific risk factor (RF), a cost-effectiveness analysis can help develop guidance for an intervention. To compare interventions, a single currency is used, calculating QALY or a year of life adjusted for disability (disability-adjusted life-year, DALY). The estimates obtained from this analysis depend on the assumptions made in this analysis. Due to the fact that preventive measures are long-lasting (lasting for decades), the consequences of the initial assumptions regarding these measures may be more important than with short-term interventions. However, the cost-effectiveness indicators of interventions for CVD prevention are important because the prevalence of CHD and the cost of treating it are high.

The cost-effectiveness indicator is calculated as the ratio of the net cost to the increase in life expectancy. Interventions with a cost-effectiveness ratio <$ 40,000 for QALY are comparable to other permanent interventions, such as control of hypertension and hemodialysis. Interventions with a cost-effectiveness indicator of <$ 20 thousand for QALY are welcome, while with an indicator of> $ 40 thousand for QALY are usually perceived by insurers as intervention above an acceptable level. The economic costs of ineffective primary prevention measures for persons with modifiable DFs> 2 in the United States annually amount to $ 13.2 billion.