Calcium channel blockade to prevent stroke in hypertension - A meta-analysis of 13 studies with 103,793 subjects

BACKGROUND: The possibility that specific antihypertensive treatments may prevent the occurrence of stroke more effectively than other treatments remains unproved. We undertook a meta-analysis to assess whether calcium channel blockers (CCBs) are associated with a lesser risk of stroke as compared with other antihypertensive drugs. METHODS: Through Medline we identified 13 major studies conducted in hypertensive subjects for a total of 103,793 subjects. Overall, there were 4040 incident cases of stroke, 1789 among 43,053 subjects randomized to CCBs and 2251 among 60,740 subjects randomized to different antihypertensive drugs. RESULTS: Considering all 13 trials, a pooled reduction in the risk of stroke was observed among subjects allocated to CCBs (odds ratio 0.90, 95% confidence interval [95% CI] 0.84-0.96; P =.002). The risk of stroke was significantly lower among subjects allocated to dihydropyridine CCBs than among those randomized to alternative drugs (odds ratio 0.90, 95% CI 0.84-0.97; P =.006), whereas the effect of non-dihydropyridine CCBs did not achieve significance (odds ratio 0.92, 95% CI 0.81-1.04). In a meta-regression analysis of these trials, the protection from stroke conferred by CCBs appeared unrelated to the degree of systolic blood pressure reduction. CONCLUSIONS: These findings suggest that CCBs decrease the risk of stroke more effectively than other treatments in patients with essential hypertension and that such an effect might not be completely explained by a better antihypertensive response. Calcium channel blockers should be considered in hypertensive subjects at increased risk of stroke.status: publishe

Meta-analysis of effectiveness or lack thereof of angiotensin-converting enzyme inhibitors for prevention of heart failure in patients with systemic hypertension

We undertook a meta-analysis of large, randomized controlled trials in hypertensive subjects that compared angiotensin-converting enzyme (ACE) inhibitors with different classes of antihypertensive drugs. Compared with subjects randomized to drugs different from ACE inhibitors, those treated with ACE inhibitors did not show a different risk of congestive heart failure (CHF) (odds ratio 1.03, 95% confidence interval 0.96 to 1.12, p = 0.407). The degree of protection from CHF associated with the use of ACE inhibitors showed a nonsignificant trend to increase with age and the degree of blood pressure control. Thus, the hypothesis that ACE inhibitors are superior to other antihypertensive drugs for prevention of CHF in hypertension remains unproven.status: publishe

Validation of the A & D UA-774 (UA-767Plus) device for self-measurement of blood pressure

Typescript (photocopy).A methodology was developed using neural network theory to predict the occurrence of out of control process parameter conditions in a composite board manufacturing facility. Three weeks of process parameter data were collected from the manufacturing operation. Multi-variable linear regression and time series analysis techniques were utilized initially to analyze the data set. A valid regression model could not be developed due to the presence of serial correlation in the data set. Time series analysis did not result in the development of a valid model because nonstationarity was present in the data set. The nonstationarity could not be removed using differencing techniques or power and logarithmic transformations. Neural network theory was chosen as an alternative approach. Feed forward back-propagation neural networks, with one hidden layer, were successfully trained to predict the classification of bonding treatment process parameters. The bonding treatment classification was based on the operating condition of the process with respect to the statistical process control limits. The bonding treatment values were classified as one of three possible conditions: above the upper process control limit, within the process control limits, or below the lower process control limit. The inputs to the network included data representing the current process condition along with historical data on relevant parameters, including moisture contents, bulk densities, and temperatures. Two training data sets were constructed, consisting of 30 and 60 data examples, respectively. Each training data set contained equal numbers of examples of the three process operating conditions. The best networks trained using the smaller training data set correctly predicted 60 percent of the bonding treatment test values. The best networks trained using the larger training data set correctly predicted the process state of control for 73 percent of the test values. These results indicate that the neural network back-propagation learning algorithm was able to identify and extract patterns from the training data sets to allow the prediction of future values of bonding treatment