High risk plaque, high risk patient or high risk procedure?

SAPPHIRE, a randomised trial of endarterectomy versus angioplasty in 'high-risk' patients, concluded that angioplasty was 'not inferior' to surgery. This has subsequently been translated to mean that angioplasty was 'preferable' or 'advisable' in patients considered high-risk for surgery, with no further discrimination between symptomatic and asymptomatic individuals. Moreover, there have been suggestions that the accepted procedural risks may have to be increased in these patients. In fact, 71% of patients in SAPPHIRE were asymptomatic in whom there was an average 6% 30-day death/stroke rate. At this level of risk, neither surgery nor angioplasty could ever prevent long-term stroke. The concept of identifying high-risk patients is laudable, but they should be high risk for stroke (i.e. symptomatic). There is currently little systematic evidence to include asymptomatic patients within this definition.     

Breast cancer risk assessment and risk reduction

Until recently, the primary message of breast health awareness programs was that early detection is a woman's best protection against breast cancer, because there was no way to prevent it. Currently, however, tamoxifen is approved for chemoprevention of breast cancer in high-risk women, and studies are underway evaluating other medications that may decrease breast cancer risk. Data have also become available regarding the efficacy of surgical strategies to reduce breast cancer risk. Any prevention method, however, will have associated risk of complications or adverse effects, and determining the net risk/benefit ratio depends on the ability to accurately quantify a woman's baseline likelihood of developing breast cancer. This article reviews available methods for assessing and reducing breast cancer risk.     

Beta risk: an unrecognized risk of statistical error

Data collected in a medical study should, from a methodological point of view, be considered as a sample taken from a larger population. The purpose of the statistical analysis is to check whether the differences in the experimental results observed in different subgroups are related to chance or not. The risks of error must be known to assess the validity of the conclusions. The first order risk, also called the alpha risk, is the risk of announcing a wrongly positive conclusion, that is to conclude that there is a significant difference that in reality does not exist. By convention, an alpha risk of 5 p. 100 is generally accepted. This means that it is acceptable to announce a statistically positive test when no difference exists in at most 5 p. 100 of the cases. After recording and processing the data, the statistical analysis produces a value called p that is the exact value of the first order risk in the given situation. If p is less than or equal to the alpha risk accepted before the study, it can be concluded that the observed difference is statistically significant at the chosen alpha level and that the p value represents the risk of first order risk in the given situation. If p is greater than the initially accepted alpha, the observed difference is not considered to be significant at the alpha level. But the assertion that two samples are equivalent, also involves a second order risk, also called the beta risk, that must be known. The beta risk is the risk of announcing wrongly negative results, that is to conclude that two samples are equivalent while in reality they are different. The number of elements in each sample necessary to demonstrate a difference becomes greater as the size of the difference becomes smaller. The beta risk increases as the alpha risk decreases, the number of cases becomes smaller, and the difference to detect becomes smaller. If a difference is not statistically significant at the chosen alpha level, the beta risk of an erroneous conclusion of equivalence is generally less than or equal to 20 p. 100. In most cases, the beta risk is not determined before the study but after, being calculated from the alpha risk, the sample size, and the non-significant difference observed. If the beta risk is found to be greater than 20 p. 100, no conclusion can be drawn and the study data are useless. It is therefore preferable to define both the alpha and beta risk and the smallest clinically pertinent difference, and to calculate the necessary sample size, before initiating the study. Let us take a numerical example where two different treatments, A and B, are given to two groups of 100 patients each. Treatment A produced success in 70 cases and treatment B in 80 cases. The chi-squared test yields a p value of 0.10. The observed difference is thus not statistically significant at an alpha level of 5 p. 100. In this case, the calculated beta risk is 54 p. 100. With 200 patients and a beta risk of 20 p. 100, a difference of 20 p. 100 in the success rates between the two groups cannot be detected. If it is accepted that a difference of 10 p. 100 between the success rates is clinically pertinent, to have an acceptable beta risk of 20 p. 100 and detect the difference, the study would have to include 500 patients instead of 200. In conclusion, when a comparative study concludes that there is no significant difference between two groups, one cannot deduct that these two groups are identical unless the beta risk is less than 20 p. 100. If the beta risk is greater than 20 p. 100, or if it is not mentioned, one cannot conclude that the two groups are equivalent.     

Cardiovascular risk estimates and risk factors in renal transplant recipients

Cardiovascular morbidity, including coronary artery disease and left ventricular hypertrophy, and mortality are high in patients following renal transplantation. Cardiovascular disease is thought to be due to traditional (hypertension, hyperlipidemia, diabetes mellitus and smoking) as well as nontraditional cardiovascular risk factors (microinflammation). Furthermore, immunosuppressive drugs, namely, calcineurin inhibitors, sirolimus, and steroids, have been reported to adversely affect cardiovascular risk factors (e.g., hypertension, hyperlipidemia, hyperglycemia). Evidence from comparative trials and from conversion studies suggest that blood pressure, hyperlipidemia, and hyperglycemia after renal transplantation may be differentially affected by the calcineurin inhibitors cyclosporine and tacrolimus. In the European Tacrolimus versus Cyclosporin A Microemulsion Renal Transplantation Study, 557 patients were randomly allocated to therapy with tacrolimus (n = 286) versus cyclosporine (n = 271). In addition, to blood pressure, serum cholesterol, HDL cholesterol, triglycerides, and blood glucose, we estimated the 10-year risk of coronary heart disease (Framingham risk score). Tacrolimus resulted in a significantly lower time-weighted average of serum cholesterol (P < .001), and mean arterial blood pressure (P < .05), but a higher time-weighted average of blood glucose (P < .01) than cyclosporine. Mean 10-year coronary artery disease risk estimate was significantly lower in men treated with tacrolimus, (10.0% versus 13.2%; P < .01) but was unchanged in women (4.7% versus 7.0%). Tacrolimus and cyclosporine microemulsion have compound-specific effects on cardiovascular risk factors that differentially affect the predicted rate of coronary artery disease.     

Utility of clinical risk predictors for preoperative cardiovascular risk prediction

Cardiovascular risk prediction using clinical risk factors is integral to both the European and the American algorithms for preoperative cardiac risk assessment and perioperative management for non-cardiac surgery. We have reviewed these risk factors and their ability to guide clinical decision making. We examine their limitations and attempt to identify factors which may improve their performance when used for clinical risk stratification. To improve the performance of the clinical risk factors, it is necessary to create uniformity in the definitions of both cardiovascular outcomes and the clinical risk factors. The risk factors selected should reflect the degree of organ dysfunction rather than a historical diagnosis. Parsimonious model design should be applied, making use of a minimal number of continuous variables rather than creating overfitted models. The inclusion of age in the model may assist partly in controlling for the duration of risk factor exposure. Risk assignment should occur throughout the perioperative period and the risk factors chosen for model inclusion should vary depending on when the assignment occurs (before operation, intraoperatively, or after operation).     

Multifactorial cardiovascular risk

IMPORTANCE OF RISK ASSESSMENT: The prevention of cardiovascular disease is a major public health goal. Cardiovascular diseases are the number one cause of mortality in industrialized countries and account for an important part of health care expenditures. In this context, assessment of the cardiovascular risk for a given subject based on epidemiological data and individual risk factors can be used to determine his/her risk of ischemic heart disease or stroke. THE FRAMINGHAM FORMULA: The most widely used assessment method is the Framingham formula which integrates age, sex, blood pressure, smoking habits and presence or not of diabetes. This formula gives an objective, reproducible estimation of the cardiovascular risk and is a useful tool for therapeutic rationale and primary and secondary prevention. INTEREST AND LIMITATIONS: This new global approach to the individual patient has interesting practical and economic implications but remains imperfect due to certain limitations (other risk factors not taken into account because they are difficult to quantify or occurred recently). For daily practice however, it provides a useful tool appreciated by clinicians and patients.