Henry A. Glick

Cholesterol and Coronary Heart Disease: Predicting Risks by Levels and Ratios

Table. SI Units and Abbreviations Clinical guidelines designed to decrease the risk for coronary heart disease have focused on identifying persons with increased levels of low-density lipoprotein (LDL) cholesterol and, to a lesser extent, increased levels of total cholesterol [1, 2]. Controversy exists about how to use high-density lipoprotein (HDL) cholesterol levels for risk assessment [3, 4]. Although early HDL cholesterol measurement was not included in the original guidelines from the National Cholesterol Education Program [1], the revised guidelines recommend HDL cholesterol measurements for initial screening. These guidelines consider a low HDL cholesterol level to be an added risk factor for coronary heart disease (if 0.91 mmol/L [ 35 mg/dL]) and a high HDL cholesterol level ( 1.55 mmol/L [ 60 mg/dL]) to negate the effect of an additional risk factor for coronary heart disease [2]. This HDL cholesterol effect is independent of the LDL cholesterol level, the primary cholesterol measure used for risk stratification [1, 2]. Observational data suggest that the total cholesterol/HDL ratio is a better predictor of subsequent coronary heart disease [5-10]. However, these data have not been incorporated into clinical guidelines, in part because their ability to discriminate the risk for coronary heart disease has not been rigorously compared in a statistical manner with other common cholesterol measures. Whether to incorporate HDL cholesterol directly into measures of risk for coronary heart disease (for example, the total cholesterol/HDL ratio) or to use HDL cholesterol to modify risk classification based on LDL cholesterol has important therapeutic implications. The primary initial intervention for increased levels of serum cholesterol is a low-fat diet, which tends to decrease levels of total cholesterol, LDL, and HDL and hardly changes the total cholesterol/HDL ratio [11, 12]. Thus post-diet risk assessment to determine subsequent management may misclassify persons if assessment is based on changes in LDL cholesterol levels alone. A total cholesterol/HDL ratio used for post-diet risk stratification, however, would consider persons who have the same ratio to be at similar risk, whether their total cholesterol level is 5.17 mmol/L or 7.24 mmol/L (200 mg/dL or 280 mg/dL). To better understand the value of the total cholesterol/HDL ratio as a summary measure of risk for coronary heart disease, we examined the ability of this ratio to predict persons who will and will not develop coronary heart disease in three populations, using statistical tests to compare this ratio with other cholesterol measures. Methods Patients We used data from three sources: the placebo group of the Lipid Research Clinics (LRC) Coronary Primary Prevention Trial (CPPT); persons enrolled in the Framingham Heart Study who participated in the 1970-1971 biennial examination; and persons without coronary heart disease at the inception of the LRC Population Prevalence Study. Analyses were done with primary data provided by the National Heart, Lung and Blood Institute. We used a high-risk population (CPPT) and a general population (Framingham Heart Study) that monitored the full spectrum of coronary heart disease to test the discriminating ability of different measures. We used two general populations (Framingham Heart Study and LRC Prevalence Study) to evaluate the risk classification abilities of the measures. The CPPT was a randomized, double-blind, placebo-controlled intervention trial that included 3806 men with primary hypercholesterolemia (type IIA) and without coronary heart disease, hypertension, hypertriglyceridemia (triglycerides >3.39 mmol/L [> 300 mg/dL]), or diabetes at trial inception. We restricted our analysis to the 1898 patients who received placebo and for whom there were complete data to minimize the confounding effects of the intervention on subsequent risk for coronary heart disease. The design of the CPPT and its results have been described previously [13, 14]. Data from this cohort of high-risk men were suitable for evaluating the ability of various cholesterol measures to discriminate among men according to their levels of risk for coronary heart disease, but these data could not be used for assessing the performance of those measures to classify risk for intervention in a more general population. Cholesterol levels (including LDL cholesterol levels) were directly measured by a central laboratory [13]. One of the general patient populations used in our analysis was made up of men and women in the Framingham Heart Study, a longitudinal observational study with biennial follow-up of 5209 men and women, 28 to 62 years of age at study inception. High-density lipoprotein and LDL cholesterol measurements were available only during the 1970-1971 examination, therefore, we studied 1025 men and 1442 women without coronary heart disease at that time who were all older than 50 years of age. The cohort study and methods for cholesterol measurement have been described previously [15]. Because the Framingham Study is a community-based inception cohort with excellent outcome assessment for the full spectrum of coronary heart disease events, data from the cohort are useful for evaluating the discriminating ability of various cholesterol measures as well as the performance of those measures in classifying risk for intervention in a population. The LRC Prevalence Study, conducted between 1972 and 1976, used a previously described two-stage screening procedure to assemble a mixed cohort with and without existing coronary heart disease [16-18]. Our second general patient population was the subsample of this cohort who were screened in the second stage (1767 women, 1911 men), were free of existing coronary heart disease at the time of assessment, and were available for follow-up. The risk-factor distribution for coronary heart disease in this sample was similar to that of persons in the general population who were 35 to 72 years of age. Low-density lipoprotein cholesterol was directly measured for all participants. Although this subsample is representative of the general population of persons who do not have coronary heart disease, restricting the outcome to death from coronary heart disease meant the LRC Prevalence Study had inadequate power to statistically test the discriminating ability of alternative cholesterol measures, but could be used to classify risk for intervention. Outcomes The outcomes we used were coronary heart disease observed during 8 years of follow-up in the CPPT and Framingham studies and death from coronary heart disease observed during 10 years of follow-up in the LRC Prevalence Study, diagnosed using previously described assessment methods [13-16]. Coronary heart disease events in the CPPT included primary myocardial infarction, angina requiring hospitalization, Rose-questionnaire-defined angina, positive findings on an exercise tolerance test, coronary revascularization, and resuscitated coronary collapse. Deaths were classified as either coronary heart disease or non-coronary heart disease, with coronary heart disease-related deaths subclassified as sudden or nonsudden. Events in the Framingham Heart Study included myocardial infarction, angina, coronary insufficiency, and sudden and nonsudden death from coronary heart disease. In the LRC Prevalence Study, events were restricted to mortality follow-up, with vital status reported for more than 99% of the cohort. Analysis Overview We tested the hypothesis that the total cholesterol/HDL ratio is a superior measure of risk for coronary heart disease when compared with the total cholesterol level or LDL cholesterol level and that the total cholesterol/HDL ratio is an equal or better measure when compared with the LDL/HDL ratio, using grouped (stratified) and logistic regression (parametric) analyses. To compare measures used for risk discrimination, we defined a superior measure as one that identifies low- and high-risk persons within risk groups identified by an inferior measure of risk for coronary heart disease. The corollary is that an inferior discriminating measure is unable to identify low- and high-risk persons within risk groups identified by a superior measure. To compare measures used for risk classification, we defined a superior cholesterol measure as one that identifies a larger proportion of a population at the same or greater risk than the current classification based on LDL cholesterol levels or as one that identifies the same proportion of the population at greater risk. Grouped (Stratified) Analysis We evaluated four pairs of cholesterol variables in the CPPT and the Framingham populations: total cholesterol compared with the total cholesterol/HDL ratio, LDL cholesterol compared with the LDL/HDL ratio, LDL cholesterol compared with the total cholesterol/HDL ratio, and the LDL/HDL ratio compared with the total cholesterol/HDL ratio. We developed two sets of three-way contingency tables for each pair of cholesterol measures. Thus, we divided the population into deciles using the first measure in the pair (for example, total cholesterol). We then divided the population in each decile into tertiles using the second measure in the pair (for example, within each total cholesterol decile, we divided observations according to the total cholesterol/HDL ratio). Within each tertile, patients were separated into those who had a coronary heart disease event and those who did not. We tested whether the total cholesterol/HDL ratio was able to identify different risks that were statistically significant within each of the deciles of total cholesterol or LDL cholesterol and whether any other measure identified differences in risk within deciles of the total cholesterol/HDL ratio, using a two-tailed P value of 0.05 for the Cochran-Mantel-Haenszel statistic [19]. Details of the grouped (stratified) analyses are provided in the Appendix. Logistic Regression Analyses

Transferability of economic evaluations across jurisdictions: ISPOR good research practices task force report

ABSTRACT A growing number of jurisdictions now request economic data in support of their decision-making procedures for the pricing and/or reimbursement of health technologies. Because more jurisdictions request economic data, the burden on study sponsors and researchers increases. There are many reasons why the cost-effectiveness of health technologies might vary from place to place. Therefore, this report of an ISPOR Good Practices Task Force reviews what national guidelines for economic evaluation say about transferability, discusses which elements of data could potentially vary from place to place, and recommends good research practices for dealing with aspects of transferability, including strategies based on the analysis of individual patient data and based on decision-analytic modeling.

Confidence Intervals for Cost-Effectiveness Ratios: A Comparison of Four Methods

We evaluated four methods for computing confidence intervals for cost-effectiveness ratios developed from randomized controlled trials: the box method, the Taylor series method, the nonparametric bootstrap method and the Fieller theorem method. We performed a Monte Carlo experiment to compare these methods. We investigated the relative performance of each method and assessed whether or not it was affected by differing distributions of costs (normal and log normal) and effects (10% absolute difference in mortality resulting from mortality rates of 25% versus 15% in the two groups as well as from mortality rates of 55% versus 45%) or by differing levels of correlation between the costs and effects (correlations of -0.50, -0.25, 0.0, 0.25 and 0.50). The principal criterion used to evaluate the performance of the methods was the probability of miscoverage. Symmetrical miscoverage of the intervals was used as a secondary criterion for evaluating the four methods. Overall probabilities of miscoverage for the nonparametric bootstrap method and the Fieller theorem method were more accurate than those for the other the methods. The Taylor series method had confidence intervals that asymmetrically underestimated the upper limit of the interval. Confidence intervals for cost-effectiveness ratios resulting from the nonparametric bootstrap method and the Fieller theorem method were more dependably accurate than those estimated using the Taylor series or box methods. Routine reporting of these intervals will allow individuals using cost-effectiveness ratios to make clinical and policy judgments to better identify when an intervention is a good value for its cost.

Avoiding bias in the conduct and reporting of cost-effectiveness research sponsored by pharmaceutical companies.

Because of the growing focus on containing health care costs, pharmaceutical companies are trying to demonstrate the cost effectiveness of their products relative to alternatives. In Europe and Aus...

Direct medical cost of overweight and obesity in the USA: a quantitative systematic review

To estimate per‐person and aggregate direct medical costs of overweight and obesity and to examine the effect of study design factors. PubMed (1968–2009), EconLit (1969–2009) and Business Source Premier (1995–2009) were searched for original studies. Results were standardized to compute the incremental cost per overweight person and per obese person, and to compute the national aggregate cost. A total of 33 US studies met review criteria. Among the four highest‐quality studies, the 2008 per‐person direct medical cost of overweight was

Patient preferences for stroke outcomes.

266 and of obesity was

The economic cost of hip fractures in community-dwelling older adults: A prospective study

1723. The aggregate national cost of overweight and obesity combined was

A Randomized Controlled Trial of Financial Incentives for Smoking Cessation

113.9 billion. Study design factors that affected cost estimates included use of national samples vs. more selected populations, age groups examined, inclusion of all medical costs vs. obesity‐related costs only, and body mass index cut‐offs for defining overweight and obesity. Depending on the source of total national healthcare expenditures used, the direct medical cost of overweight and obesity combined is approximately 5.0% to 10% of US healthcare spending. Future studies should include nationally representative samples, evaluate adults of all ages, report all medical costs and use standard body mass index cut‐offs.

Cost Effectiveness of Low-Dose Zidovudine Therapy for Asymptomatic Patients with Human Immunodeficiency Virus (HIV) Infection

In clinical trials stroke is reported as a major morbid outcome, but the impact of stroke on patients is not directly assessed. This study examines patient preferences for different outcomes of stroke, including death. Methods We presented patients with written case scenarios of stroke outcomes. The scenarios represented four categories of stroke severity (mild, moderate, severe, and fatal), and for nonfatal strokes the scenarios described motor, language, and cognitive deficits. Patients reported values for each of the 10 stroke scenarios using a rank-and-scale method over a 100-point range, with 100 representing perfect health and 0 corresponding to the worst possible health state. Results One hundred seventeen of 209 consecutive patients at risk for stroke participated in this study. Severe strokes were uniformly rated as having low preference weights (mean±SD [median]: 3±4 [1] for disabling hemiplegia, 8±9 [5] for confusion, and 15 ±14 [10] for global aphasia), and severe motor impairment (a disabling hemiplegia) was rated as significantly worse than death. Even mild deficits resulted in substantial loss to patients (54±21 [55] for dysarthria and 53±21 [50] for mild anomia). Conclusions Strokes may result in a wide variety of poststroke consequences for patients. Severe strokes may be viewed by patients as tantamount to or worse than death. Even mild strokes may cause significant declines in patient preferences for health states. These data are useful in interpreting studies that report stroke and death, in designing new studies that measure stroke in at-risk populations, and in helping patients reach treatment decisions about therapies designed to prevent strokes.

What is the Cost of Nephrotoxicity Associated with Aminoglycosides

OBJECTIVES: To evaluate the incremental cost in the year after hip fracture.