Apparent treatment-resistant hypertension and chronic kidney disease: another cardiovascular-renal syndrome?

To identify patients at increased risk of cardiovascular (CV) outcomes, apparent treatment-resistant hypertension (aTRH) is defined as having a blood pressure above goal despite the use of 3 or more antihypertensive therapies of different classes at maximally tolerated doses, ideally including a diuretic. Recent epidemiologic studies in selected populations estimated the prevalence of aTRH as 10% to 15% among patients with hypertension and that aTRH is associated with elevated risk of CV and renal outcomes. Additionally, aTRH and CKD are associated. Although the pathogenesis of aTRH is multifactorial, the kidney is believed to play a significant role. Increased volume expansion, aldosterone concentration, mineralocorticoid receptor activity, arterial stiffness, and sympathetic nervous system activity are central to the pathogenesis of aTRH and are targets of therapies. Although diuretics form the basis of therapy in aTRH, pathophysiologic and clinical data suggest an important role for aldosterone antagonism. Interventional techniques, such as renal denervation and carotid baroreceptor activation, modulate the sympathetic nervous system and are currently in phase III trials for the treatment of aTRH. These technologies are as yet unproven and have not been investigated in relationship to CV outcomes or in patients with CKD.

Evaluating marker-guided treatment selection strategies

© 2014, The International Biometric Society.A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.