Racial differences in the burden of coronary artery calcium and carotid intima media thickness between Blacks and Whites

Background

Identification of racial differences in the burden and correlates of carotid intima media thickness (CIMT) and coronary artery calcium (CAC) may provide the basis for the development of race-specific cardiovascular disease (CVD) risk prediction algorithms.

Methods

In the Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) study, CIMT was measured by carotid ultrasonography in 792 individuals (35 % Black). CIMT >1 mm was considered significant. CAC was quantified by electron beam computed tomography in 776 individuals (46 % Black). CAC was considered significant if the Agatston score was >100. Cross-sectional associations between race, CIMT and CAC were assessed using logistic regression models.

Results

Blacks had greater CIMT (mean difference 0.033 mm, 95 % CI 0.005–0.06 mm; p = 0.02) and 1.5-fold (95 % CI 1.0–2.3) higher odds of having significant CIMT than Whites. Blacks had less CAC than Whites (mean Agatston score difference 66, [11–122]; p = 0.02) and 50 % lower odds of a significant CAC score compared with Whites (0.5 [0.3–0.7]). These associations were virtually unchanged after adjustment for CVD risk factors. Of the novel CVD risk markers assessed, small-dense low-density lipoprotein was independently associated with increased odds of significant CIMT, with the association being similar among Blacks and Whites (odds ratio [95 % CI]: 1.7 [1.2–2.5] and 1.4 [1.0–1.8] per 1-SD higher level, respectively). Interleukin-6 was significantly associated with CAC among Blacks (1.4 [1.0–2.0]).

Conclusion

Black race is independently associated with greater CIMT but less CAC than White race. CVD risk stratification strategies that incorporate these measures of subclinical atherosclerosis should consider race-specific algorithms.

Electronic supplementary material

The online version of this article (doi:10.1007/s12471-014-0610-4) contains supplementary material, which is available to authorized users.     

A new u-statistic with superior design sensitivity in matched observational studies

Summary In an observational or nonrandomized study of treatment effects, a sensitivity analysis indicates the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a naïve analysis that presumes adjustments for observed covariates suffice to remove all bias. The power of sensitivity analysis is the probability that it will reject a false hypothesis about treatment effects allowing for a departure from random assignment of a specified magnitude; in particular, if this specified magnitude is “no departure” then this is the same as the power of a randomization test in a randomized experiment. A new family of u‐statistics is proposed that includes Wilcoxon's signed rank statistic but also includes other statistics with substantially higher power when a sensitivity analysis is performed in an observational study. Wilcoxon's statistic has high power to detect small effects in large randomized experiments—that is, it often has good Pitman efficiency—but small effects are invariably sensitive to small unobserved biases. Members of this family of u‐statistics that emphasize medium to large effects can have substantially higher power in a sensitivity analysis. For example, in one situation with 250 pair differences that are Normal with expectation 1/2 and variance 1, the power of a sensitivity analysis that uses Wilcoxon's statistic is 0.08 while the power of another member of the family of u‐statistics is 0.66. The topic is examined by performing a sensitivity analysis in three observational studies, using an asymptotic measure called the design sensitivity, and by simulating power in finite samples. The three examples are drawn from epidemiology, clinical medicine, and genetic toxicology.     

Observational study to characterise 24-hour COPD symptoms and their relationship with patient-reported outcomes: results from the ASSESS study

Background

Few studies have investigated the 24-hour symptom profile in patients with COPD or how symptoms during the 24-hour day are inter-related. This observational study assessed the prevalence, severity and relationship between night-time, early morning and daytime COPD symptoms and explored the relationship between 24-hour symptoms and other patient-reported outcomes.

Methods

The study enrolled patients with stable COPD in clinical practice. Baseline night-time, early morning and daytime symptoms (symptom questionnaire), severity of airflow obstruction (FEV₁), dyspnoea (modified Medical Research Council Dyspnoea Scale), health status (COPD Assessment Test), anxiety and depression levels (Hospital Anxiety and Depression Scale), sleep quality (COPD and Asthma Sleep Impact Scale) and physical activity level (sedentary, moderately active or active) were recorded.

Results

The full analysis set included 727 patients: 65.8% male, mean ± standard deviation age 67.2 ± 8.8 years, % predicted FEV₁ 52.8 ± 20.5%.In each part of the 24-hour day, >60% of patients reported experiencing ≥1 symptom in the week before baseline. Symptoms were more common in the early morning and daytime versus night-time (81.4%, 82.7% and 63.0%, respectively). Symptom severity was comparable for each period assessed. Overall, in the week before baseline, 56.7% of patients had symptoms throughout the whole 24-hour day (3 parts of the day); 79.9% had symptoms in ≥2 parts of the 24-hour day. Symptoms during each part of the day were inter-related, irrespective of disease severity (all p < 0.001).Early morning and daytime symptoms were associated with the severity of airflow obstruction (p < 0.05 for both). Night-time, early morning and daytime symptoms were all associated with worse dyspnoea, health status and sleep quality, and higher anxiety and depression levels (all p < 0.001 versus patients without symptoms in each corresponding period). In each part of the 24-hour day, there was also an association between symptoms and a patient’s physical activity level (p < 0.05 for each period).

Conclusions

More than half of patients experienced COPD symptoms throughout the whole 24-hour day. There was a significant relationship between night-time, early morning and daytime symptoms. In each period, symptoms were associated with worse patient-reported outcomes, suggesting that improving 24-hour symptoms should be an important consideration in the management of COPD.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0122-1) contains supplementary material, which is available to authorized users.     

Optimal matching with minimal deviation from fine balance in a study of obesity and surgical outcomes

In multivariate matching, fine balance constrains the marginal distributions of a nominal variable in treated and matched control groups to be identical without constraining who is matched to whom. In this way, a fine balance constraint can balance a nominal variable with many levels while focusing efforts on other more important variables when pairing individuals to minimize the total covariate distance within pairs. Fine balance is not always possible; that is, it is a constraint on an optimization problem, but the constraint is not always feasible. We propose a new algorithm that returns a minimum distance finely balanced match when one is feasible, and otherwise minimizes the total distance among all matched samples that minimize the deviation from fine balance. Perhaps we can come very close to fine balance when fine balance is not attainable; moreover, in any event, because our algorithm is guaranteed to come as close as possible to fine balance, the investigator may perform one match, and on that basis judge whether the best attainable balance is adequate or not. We also show how to incorporate an additional constraint. The algorithm is implemented in two similar ways, first as an optimal assignment problem with an augmented distance matrix, second as a minimum cost flow problem in a network. The case of knee surgery in the Obesity and Surgical Outcomes Study motivated the development of this algorithm and is used as an illustration. In that example, 2 of 47 hospitals had too few nonobese patients to permit fine balance for the nominal variable with 47 levels representing the hospital, but our new algorithm came very close to fine balance. Moreover, in that example, there was a shortage of nonobese diabetic patients, and incorporation of an additional constraint forced the match to include all of these nonobese diabetic patients, thereby coming as close as possible to balance for this important but recalcitrant covariate.     

Testing hypotheses in order

In certain circumstances, one wishes to test one hypothesisonly if certain other hypotheses have been rejected. This orderingof hypotheses simplifies the task of controlling the probabilityof rejecting any true hypothesis. In an example from an observationalstudy, a treated group is shown to be further from both of twocontrol groups than the two control groups are from each other.