Biomedical subjects

Michael A Proschan

Publications and source records attributed to Michael A Proschan.

4 recordsLinked to original sources

PubMed2003

Individual blood pressure responses to changes in salt intake: results from the DASH-Sodium trial.

Although group characteristics are known to influence average blood pressure response to changes in salt intake, predictability of individual responses is less clear. We examined variability and consistency of individual systolic blood pressure responses to changes in salt intake in 188 participants who ate the same diet at higher, medium, and lower (140, 104, 62 mmol/d) sodium levels for 30 days each, in random order, after 2 weeks of run-in at the higher sodium level. Regarding variability in systolic blood pressure changes over time, changes from run-in to higher sodium (no sodium level change) ranged from -24 to +25 mm Hg; 8.0% of participants decreased > or =10 mm Hg. Regarding variability in systolic blood pressure response to change in sodium intake, with higher versus lower sodium levels (78-mmol sodium difference), the range of systolic blood pressure change was -32 to +17 mm Hg; 33.5% decreased > or =10 mm Hg. Regarding consistency of response, systolic blood pressure change with run-in versus lower sodium was modestly correlated with systolic blood pressure change with higher versus medium sodium; systolic blood pressure change with higher versus lower sodium was similarly correlated with run-in versus medium sodium (combined Spearman r=0.27, P=0.002). These results show low-order consistency of response and confirm that identifying individuals as sodium responders is difficult. They support current recommendations for lower salt intake directed at the general public rather than "susceptible" individuals as one of several strategies to prevent and control adverse blood pressures widely prevalent in the adult population.

Adult↗

PubMed2003

Practical midcourse sample size modification in clinical trials.

Power calculations are very important in the planning of a well-designed clinical trial. Sometimes there is limited information available before the trial, making it highly desirable to adjust the sample size after seeing actual trial data. Indeed, there has been a recent proliferation of papers promising great flexibility in midcourse correction of sample size and other design features, such as choice of primary endpoint. We point out the difficulty in accurately estimating the treatment effect midway through a trial, and we encourage the use of a simple, conservative approach whereby sample size can be increased but not decreased from what was originally planned. We show how to compute the p value and confidence interval for this two-stage procedure. If the original sample size is maintained, analysis of the data is the same as for a fixed sample procedure.

Bayes Theorem↗

PubMed2003

Premier: a clinical trial of comprehensive lifestyle modification for blood pressure control: rationale, design and baseline characteristics.

PURPOSE: To describe PREMIER, a randomized trial to determine the effects of multi-component lifestyle interventions on blood pressure (BP). METHODS: Participants with above optimal BP through stage 1 hypertension were randomized to: 1) a behavioral lifestyle (BLS) intervention that implements established recommendations, 2) a BLS intervention that implements established recommendations plus the DASH diet, or 3) an advice only standard of care group. The two BLS interventions consist of group and individual counseling sessions for 18 months. The primary outcome is systolic BP at 6 months. Additional outcomes include diastolic BP and homocysteine at 6 months; systolic and diastolic BP at 18 months; fasting lipids, glucose and insulin at 6 and 18 months; and effects in subgroup. CONCLUSION: Results from the PREMIER trial will provide scientific rationale for implementing multi-component behavioral lifestyle intervention programs to control BP and prevent CVD.

Blood Pressure↗

PubMed2003

Estimation of energy requirements in a controlled feeding trial.

BACKGROUND: Estimating energy requirements is a frequent task in clinical studies. OBJECTIVE: We examined weight patterns of participants enrolled in a clinical trial and evaluated factors that may affect weight stabilization. The Harris-Benedict equation and the FAO/WHO equation, used in conjunction with physical activity levels estimated with the 7-d Physical Activity Recall, were compared for estimating energy expenditure. DESIGN: This was a multicenter, randomized controlled feeding trial with participants of the Dietary Approaches to Stop Hypertension Trial. For 11 wk, the amount of food participants received was adjusted to maintain their body weights as close to their initial weights as possible. Change-point regression techniques were used to identify weight-stable periods. Factors related to achieving weight stabilization were examined with logistic regression. RESULTS: A stable weight was achieved by 86% of the 448 participants during the run-in period and by 78% during the intervention period. Energy intake averaged 11 +/- 2.4 MJ/d (2628 +/- 578 kcal/d), with most participants (n = 270) requiring 9-13 MJ/d (2100-3100 kcal/d). The difference between predicted and observed intakes was highest at high estimated energy intakes, mainly because of high and probably incorrect estimates of the activity factor. Participants with lower energy intakes tended to need less adjustment of their energy intakes to maintain a stable weight than did participants with higher energy intakes. CONCLUSIONS: Weight stabilization is not affected by diet composition, sex, race, age, or baseline weight. Either the Harris-Benedict equation or the FAO/WHO equation can be used to estimate energy needs. Activity factors > 1.7 often lead to overestimation of energy needs.

Adolescent↗