Task force VI: Self-monitoring of the blood pressure.

BACKGROUND: Self-monitoring of the blood pressure by patients at home or in other nonclinical settings has become increasingly common in recent years. This phenomenon has been fueled in part by the increase in availability of automatic sphygmomanometers, which are now both affordable and easy for patients to use. BENEFITS OF SELF-MONITORING: Self-monitoring of the blood pressure can be an important adjunct to management of hypertension. The technique allows patients to participate more in their care. Self-measured values of blood pressure are more likely to be representative of the average daily blood pressure than is a clinic measurement and may be better related to hypertensive involvement of target organs and cardiovascular morbidity than is the clinic blood pressure. Finally, the self-monitoring of blood pressure has the potential to reduce the costs of hypertension-related care. LIMITATIONS OF SELF-MONITORING: There are several issues that prevent the more widespread use of self-monitoring of the blood pressure in clinical practice. First, devices marketed for use by patients have advanced technically during the 1990s, but many have not been subjected to rigorous clinical validation for precision and reliability (e.g. in terms of Association for the Advancement of Medical Instrumentation and British Hypertension Society guidelines). It is recommended that devices for measuring blood pressure used by patients at home be subjected to the same validation processes as those that are applied to ambulatory recordings. Second, although the upper limits of normal for self-monitored blood pressure of a general population can be defined statistically (it is approximately 135/85 mmHg), it is not yet possible to determine the normal self-monitored blood pressure because these values must be linked to classical clinical cardiovascular endpoints or outcomes. Third, the relationships among self-monitored, clinic, and ambulatory blood pressures are defined for some populations but their behaviors according to age, sex, ethnicity, and treatment status require further study. Fourth, several different schedules for self-monitoring of the blood pressure by patients have been used in clinical research and practice. It will be necessary to determine the optimal schedule and number of recordings required when patients perform self-monitoring of the blood pressure. Fifth, self-monitoring of the blood pressure in clinical trials of antihypertensive therapies is certainly feasible but has typically not been included in their design, either by investigators or by the pharmaceutical sponsors. Sixth, there have been data suggesting that self-monitoring of the blood pressure reduces the comprehensive costs associated with hypertension care on an annual basis. However, since most work on the economic impact of self-monitoring of the blood pressure has been performed in managed-care environments in the USA, it is not known whether this reduction in health-care costs would be applicable to other types of practice environments on a worldwide basis. CONCLUSIONS: Self-monitoring of the blood pressure is at present useful as an adjunct measurement for the management of hypertensive patients and might provide benefits in clinical trials of antihypertensive therapy. Nevertheless, the available data on self-monitoring of the blood pressure are inadequate as grounds for clinicians to make primary diagnostic or therapeutic decisions and should not override the blood pressure obtained by clinical measurement or via ambulatory monitoring

Evaluation of gain scheduled predictive control in a nonlinear MIMO model of a hydropower station

This work deals with the evaluation of the performance of a predictive control applied to a nonlinear model of Dinorwig a pumped storage hydropower plant. The controller uses a piecewise-linear plant model for prediction and is gain-scheduled according to the number of active hydro-generation Units (ranging from 1 to 6). Simulated results are presented to evaluate the performance of the predictive controller, which is compared with a gain-scheduled PI controller that has anti-windup features; this controller was tuned using the current practical values. The results show that the response, to various changes in the plant operating conditions, obtained with the predictive controller is faster and less sensitive than the one obtained from the PI controller. The results also show how reduced-order models can be used for prediction, allowing the reduction of the computing time (or the computing cost) without compromising the closed-loop performance control signal. (C) 2014 Elsevier Ltd. All rights reserved

Neural Network Analysis Identifies Scaffold Properties Necessary for In Vitro Chondrogenesis in Elastin-like Polypeptide Biopolymer Scaffolds

The successful design of biomaterial scaffolds for articular cartilage tissue engineering requires an understanding of the impact of combinations of material formulation parameters on diverse and competing functional outcomes of biomaterial performance. This study sought to explore the use of a type of unsupervised artificial network, a self-organizing map, to identify relationships between scaffold formulation parameters (crosslink density, molecular weight, and concentration) and 11 such outcomes (including mechanical properties, matrix accumulation, metabolite usage and production, and histological appearance) for scaffolds formed from crosslinked elastin-like polypeptide (ELP) hydrogels. The artificial neural network recognized patterns in functional outcomes and provided a set of relationships between ELP formulation parameters and measured outcomes. Mapping resulted in the best mean separation amongst neurons for mechanical properties and pointed to crosslink density as the strongest predictor of most outcomes, followed by ELP concentration. The map also grouped formulations together that simultaneously resulted in the highest values for matrix production, greatest changes in metabolite consumption or production, and highest histological scores, indicating that the network was able to recognize patterns amongst diverse measurement outcomes. These results demonstrated the utility of artificial neural network tools for recognizing relationships in systems with competing parameters, toward the goal of optimizing and accelerating the design of biomaterial scaffolds for articular cartilage tissue engineering

Renal function trajectories and clinical outcomes in acute heart failure

Background—Prior studies have demonstrated adverse risk associated with baseline and worsening renal function in acute heart failure, but none has modeled the trajectories of change in renal function and their impact on outcomes. Methods and Results—We used linear mixed models of serial measurements of blood urea nitrogen and creatinine to describe trajectories of renal function in 1962 patients with acute heart failure and renal dysfunction enrolled in the Placebo-Controlled Randomized Study of the Selective A1 Adenosine Receptor Antagonist Rolofylline for Patients Hospitalized with Acute Decompensated Heart Failure and Volume Overload to Assess Treatment Effect on Congestion and Renal Function study. We assessed risk of 180-day mortality and 60-day cardiovascular or renal readmission and used Cox regression to determine association between renal trajectories and outcomes. Compared with patients alive at 180 days, patients who died were older, had lower blood pressure and ejection fraction, and higher creatinine levels at baseline. On average for the entire cohort, creatinine rose from days 1 to 3 and increased further after discharge, with the trajectory dependent on the day of discharge. Blood urea nitrogen, creatinine, and the rate of change in creatinine from baseline were the strongest independent predictors of 180-day mortality and 60-day readmission, whereas the rate of change of blood urea nitrogen from baseline was not predictive of outcomes. Baseline blood urea nitrogen >35 mg/dL and increase in creatinine >0.1 mg/dL per day increased the risk of mortality, whereas stable or decreasing creatinine was associated with reduced risk. Conclusions—Patients with acute heart failure and renal dysfunction demonstrate variable rise and fall in renal indices during and immediately after hospitalization. Risk of morbidity and mortality can be predicted based on baseline renal function and creatinine trajectory during the first 7 days. Clinical Trial Registration—URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00328692 and NCT00354458