Prior Medications and the Cardiovascular Benefits From Combination Angiotensin‐Converting Enzyme Inhibition Plus Calcium Channel Blockade Among High‐Risk Hypertensive Patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142520/1/jah32856_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142520/2/jah32856.pd

Amlodipine+Benazepril is Superior to Hydrochlorothiazide+Benazepril Irrespective of Baseline Pulse Pressure: Subanalysis of the ACCOMPLISH Trial

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110751/1/jch12460.pd

Hypertension in African Americans Aged 60 to 79 Years: Statement From the International Society of Hypertension in Blacks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111078/1/jch12511.pd

Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study.

Background: Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study. Methods: We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according to an 80%:20% distribution with various seeds. Prediction models were optimized within the CARET package of R. Results: The best performance of the different machine learning techniques was that of the random forest method which yielded a receiver operator curve (ROC) area of 68.1%±4.2% (mean ± SD) on the testing subset with ten different seed values used to separate training and testing subsets. The four most important variables in the random forest method were SBP, serum creatinine, glycosylated hemoglobin, and DBP. Each of these variables was also important in at least some of the other methods. The treatment assignment group was not consistently an important determinant in any of the models. Conclusion: Prediction of a composite cardiovascular outcome was difficult in the CORAL population, even when employing machine learning methods. Assignment to either the stenting or best medical therapy group did not serve as an important predictor of composite outcome. Clinical Trial Registration: ClinicalTrials.gov, NCT00081731

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

The First Hypertension Trial Comparing the Effects of Two Fixed-Dose Combination Therapy Regimens on Cardiovascular Events: Avoiding Cardiovascular Events Through Combination Therapy in Patients Living With Systolic Hypertension (ACCOMPLISH)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73430/1/j.1524-6175.2003.02676.x.pd

Prompt, Aggressive BP Lowering in High-Risk Patients

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74213/1/j.1524-6175.2007.08145.x.pd