Polynomial analysis of ambulatory blood pressure measurements

In normotensive subjects blood pressures follow a circadian rhythm. A circadian rhythm in hypertensive patients is less well established, and may be clinically important, particularly with rigorous treatments of daytime blood pressures. Polynomial analysis of ambulatory blood pressure monitoring (ABPM) data were used to identify and study circadian rhythms in ten mildly hypertensive patients both untreated and after single-dose treatment with different categories of antihypertensive agents. ABPM monitoring was performed using validated Space Lab Medical Inc portable equipment, polynomial regression analyses of the systolic blood pressures using Harvard Graphics 3 as well as SPSS Statistical Software. Polynomial curves were compared with the observed data as measured. Fourth order polynomial curves provided the best fit not only for individual data but also for group means. A clear circadian rhythm could be identified, with a day-by-day reproducibility as measured by intra-class correlation, as high as 0.64 versus 0.46 for the observed data, and a goodness of fit as measured by level of correlation between observed and modelled data as high as 0.912. The 4th order polynomial curves provided trough-peak ratios with a median value of 0.85 versus 0.65 for the observed data. Also, the approach enabled comparison of the patterns of reaction to single-dose treatment with different categories of antihypertensive drugs. Reproducibility of polynomial analysis of ABPM data is fundamentally better than that of observed data, and this is so not only with means of populations but also with individual data. We assume that the difference in reproducibility is due to the potential of polynomial analysis to remove exogenic components from the data and thus visualise the true endogenic circadian rhythm of blood pressures. The method enables us to study circadian rhythms both in treated and untreated patients with mild hypertension, and could be used to predict night-time blood pressure from observed daytime value

Determinants of perceived severity of hypertension and drug-compliance in mildly hypertensive patients.

Background:Severity of illness is not an important determinant of drug-compliance. In this paper we hypothesize that the perceived severity of illness rather than the true severity of illness is a determinant of drug-compliance. If this is true, then it will be worthwhile for physicians to look for factors determining this perceived severity of illness. Objectives: (I) To test in a prospective survey whether this hypothesis can be confirmed in mildly hypertensive patients, and (II) to identify factors determining their perceived severity of illness. Methods: 450 patients were invited to participate in a prospective survey if their systolic blood pressure had been between 140 and 170 mm Hg and their diastolic blood pressure between 90 and 100 mm Hg despite treatment, for at least three clinic visits. Based on previously published data three factors possibly contributing to the perceived severity of hypertension were identified: (1) objective medical information, (2) expected physical symptoms, and (3) a positive social identification with fellow-patients. These factors were used as independent determinants in a multiple linear regression model with perceived severity of hypertension as outcome variable. Subsequently, this outcome variable together with patient characteristics was used as an independent variable in a multiple logistic regression model with drug-compliance as outcome variable. Results: 176 patients, mean age 62 years, 52% females, completed the study. In the multiple linear regression analysis all of the three identified factors were statistically significant predictors of the perceived severity of hypertension with betavalues from 0.22 to 0.26, and p-values between 0.031 and 0.004. The multiple logistic regression analysis demonstrated that, after adjustment for gender, age, school, and general health status, the perceived severity of hypertension was a significant determinant of drug-compliance at p = 0.040. Discussion: The present study shows what information patients use to conclude on the level of their blood pressure being too high or not. This information can be used to better understand the patients ideas about health and possibly to influence these ideas. Patients conclusion about the level of their blood pressure predicted their drug-compliance. Our study increased insight into the psychology of the patient and the results may be helpful to physicians in order to further understand and influence patient behaviors, particularly, adherence to antihypertensive medication

Clinical Trials: Odds Ratios and Multiple Regression Models—Why and How to Assess Them

Odds ratios (ORs), unlike chi2 tests, provide direct insight into the strength of the relationship between treatment modalities and treatment effects. Multiple regression models can reduce the data spread due to certain patient characteristics and thus improve the precision of the treatment comparison. Despite these advantages, the use of these methods in clinical trials is relatively uncommon. Our objectives were (1) to emphasize the great potential of ORs and multiple regression models as a basis of modern methods; (2) to illustrate their ease of use; and (3) to familiarize nonmathematical readers with these important methods. Advantages of ORs are multiple. (1) They describe the probability that people with a certain treatment will have an event, versus those without the treatment, and are therefore a welcome alternative to the widely used chi2 tests for analyzing binary data in clinical trials. (2) statistical software of ORs is widely available. (3) Computations using risk ratios (RRs) are less sensitive than those using ORs. (4) ORs are the basis for modern methods such as meta-analyses, propensity scores, logistic regression, and Cox regression. For analysis, logarithms of the ORs have to be used; results are obtained by calculating antilogarithms. A limitation of the ORs is that they present relative benefits but not absolute benefits. ORs, despite a fairly complex mathematical background, are easy to use, even for nonmathematicians. Both linear and logistic regression models can be adequately applied for the purpose of improving precision of parameter estimates such as treatment effects. We caution that, although application of these models is very easy with computer programs widely available, the fit of the regression models should always be carefully checked, and the covariate selection should be carefully considered and sparse. We do hope that this article will stimulate clinical investigators to use ORs and multiple regression models more ofte

Cardiovascular drug trials: how to examine interaction, and why so

BACKGROUND: In practice the benefit of cardiovascular medicines is less consistent than it is in clinical trials. This is due to multiple uncontrolled factors that co-determine the efficacy of the new treatment. In statistical terms, they interact with the new treatment. Interaction effects are rarely assessed in cardiovascular trials. OBJECTIVE: To review (1) important factors that may interact with the treatment efficacy, (2) how to examine such factors, and (3) why so. RESULTS: Important factors include (a) possible risk factors such as specific patient characteristics, and concomitant medications, and (b) study-specific aspects such as heterogeneities of investigators, health centres, and individual patients including patient compliance. Such factors can be assessed by comparing subgroups. A common but incorrect approach is the comparison of the significances of difference between treatment modalities in either subgroup. Instead, a direct comparison of effect sizes relative to the standard errors is adequate. As an alternative, regression modelling is adequate and convenient. Results of interaction assessments are post-hoc and, therefore, of an exploratory and unconfirmed nature. So, why should they be performed? In cardiovascular research the effects of patient characteristics and drug-drug interactions on drug efficacies are numerous. It is valuable to account at least post-hoc for such mechanisms. Second, current cardiovascular trials involve heterogeneous health centres, investigators, and patient groups. Accounting for these heterogeneities can be helpful to better predict individual responses in future patients. CONCLUSION: Cardiovascular trials enrolling patient groups at risk for heterogeneity should include at least a post-hoc assessment for interaction. Correct and incorrect methods for that purpose are described. Interaction assessments are helpful to better predict the efficacy/safety of new cardiovascular medicines in the future treatment of subgroups of patients. (Neth Heart J 2007;15:61-6.17612662

Assessment of quality of life in patients with angina pectoris: Progress made by the Dutch Mononitrate Quality-Of-Life (DUMQOL) Study Group

Two major issues in quality of life (QOL) research are the patient's opinion as a contributing factor in QOL assessments, and the lack of sensitivity of QOL assessments. To review results from recent research by the Dutch Mononitrate Quality Of Life (DUMQOL) Study Group relevant to these two issues. Using a test battery including Stewart's Short Form (SF)-36 Questionnaire and the DUMQOL-50 questionnaire, the DUMQOL Study Group tested the hypothesis that the patient's opinion might be an independent determinant of QOL. To do this, a stepwise multiple regression analysis of data from 82 patients attending an outpatient clinic with stable angina pectoris was performed. Secondly, using an odds ratio approach to QOL scores in 1350 outpatients with stable angina pectoris who were attending a clinic, the DUMQOL Study Group assessed the question whether relative scores might provide increased precision in estimating the effects of patient characteristics on QOL data. Psychological distress was the most important contributor to QOL (beta 0.43, p <0.0001). Also, the patient's opinion on his/her QOL significantly contributed to QOL (beta 0.22, p=0.032). Physical health status according to the patient's judgement only made a borderline contribution (beta 0.19, p=0.71), while the physician's judgement was not associated with QOL at all (beta 0.11, p=0.87). Regarding the second issue, increased QOL difficulties were observed in New York Heart Association (NYHA) angina class III-IV patients, in patients with comorbidity, as well as in female and elderly patients. Odds ratios can be used in these categories to predict the benefit from treatments. Recent QOL research of the DUMQOL Study Group allows conclusions to be drawn that are relevant to clinical practice. QOL should be defined in a subjective rather than an objective method. The patient's opinion is an important independent contributor to QOL. The comparison of absolute QOL scores lacks the sensitivity to truly estimate QOL. The odds ratio approach of QOL scores provides increased precision in estimating QO