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The treatment of active infective endocarditis (IE) presents a clinical dilemma with uncertain outcomes. This study sets out to determine the early and intermediate outcomes of patients treated surgically for active IE at an academic medical center.

One mechanism to improve outcomes of patients with endocarditis is early detection and treatment. The recommendations for management of these patients was recently published by Baddour et al. [5]. All patients with suspected endocarditis should have [1] 3 sets of blood cultures drawn from separate sites and an echocardiogram performed expeditiously. Once the organism is identified then antibiotic specific therapy to sterilize vegetations in IE are started. Furthermore, the duration of therapy must be sufficient to ensure complete eradication of microorganisms within vegetations [15].

Localized primary prostate cancer can be treated via external beam radiotherapy (EBRT) or permanent interstitial seed brachytherapy (BT). Both of these treatment modalities achieve excellent tumor control rates [1,2,3]. The recommendations and guidelines consider these modalities as equivalent especially for patients with low-risk prostate cancer [1, 2]. For intermediate-risk patients this strict recommendation to perform seeds BT is missing [1]. Nevertheless, numerous studies evaluating the tumor control rates after seeds BT included besides low-risk prostate cancer also intermediate-risk patients and reported excellent tumor control rates [4,5,6,7,8,9,10,11]. However, up to now no randomized trial successfully evaluating the effectiveness of seeds BT compared to EBRT has been published so far. The German PREFERE trial [12], coming closest, tried to compare the outcomes of active surveillance, EBRT, BT and prostatectomy in low- and favorable intermediate-risk prostate cancer, but recruited only 345 out of the targeted 7600 patients and was therefore closed, thus, leaving the question unanswered by a prospective study. Our objective with this study is to compare both methods with data acquired from clinical routine patients treated at our department over a period of 20 years. For the evaluation we use data from intermediate-risk prostate cancer patients. We report the results concerning biochemical no evidence of disease (bNED) as well as late gastrointestinal und genitourinary side effects, as increased bNED rates shift the focus on side effects.

Click on the links below to download a sample unit from the Student's Book, Workbook and Teacher's Book of Outcomes Intermediate, including the class and workbook audio. The video is available for download from a separate page, here.

Many trait measurements are size-dependent, and while we often divide these traits by size before fitting statistical models to control for the effect of size, this approach does not account for allometry and the intermediate outcome problem. We describe these problems and outline potential solutions.

An experimental treatment is intended to change a focal variable, but it often affects other unintended variables, referred to as mediators or intermediate outcomes [14]. For example, maternal dietary conditions, such as dietary restriction or over-nutrition, may influence offspring size (x), as well as offspring food intake (the focal trait; y) [2, 11]. If we know that offspring body mass and offspring food intake are correlated, we may want to account for the effect of offspring size when assessing experimental effects on offspring food intake. However, as both offspring body mass and food intake are measured after the treatment has been applied (offspring body mass cannot be measured before maternal diet is manipulated), we do not know the chain of causation. Imagine two scenarios. In scenario A, offspring body mass and food intake are mechanistically linked, and the maternal diet treatment subsequently affects both traits (direct effects; Fig. 2a). Alternatively, in scenario B, the treatment affects offspring body size (direct effect), which then influences offspring food intake (indirect effect), as well as a potential direct effect of the treatment on offspring food intake (Fig. 2b).

Two scenarios of the relationship among an experimental treatment, a trait of interest (focal variable, y) and an intermediate outcome (x). a The treatment affects both x and y, and therefore x and y are correlated (dotted line with a double-headed arrow) but x does not affect y. b The treatment affects both x and y, and then x also affects y

Measures of the distribution of health in and among populations are as relevant as measures of the level of health in and among populations (15). Understanding the distribution of health can focus attention and action on specific health determinants and population groups to reduce inequalities in health and improve the overall level of health. Although the distribution of health outcomes could be assessed on any measurable geographic, demographic, social, or economic characteristic, someresearchers argue that health inequalities should be assessed by using specific social and economic characteristics that have historically determined social status (for example, wealth, ethnicity, sex, educational attainment) (19). Others suggest that this viewpoint excludes potentially relevant determinants of health (20). Metrics to assess the distribution of outcomes include measures of inequality (Gini index), measures of association (rate ratio), measures of impact (population-attributableproportion), and measures based on ranking (concentration index) (21,22).

In 2008, Wold reviewed 35 sets of health indicators in use (26). Although not an exhaustive list, these 35 sets provide a representative view of health indicators and their intended uses, which include presenting a picture of the health of a place, stimulating action to improve health, and tracking progress toward meeting objectives (Table 2). No set of indicators is explicitly used as a guide to financially reward improvement in health outcomes.

The principal sources of data available for US population health outcomes are mortality data derived from death certificates and data on subjective health status, functional status, and experiential state derived from population health surveys. The National Vital Statistics System (NVSS) collects and compiles data on births and deaths from all registration districts (most commonly states) in the United States. The most commonly used surveys are NHIS, BRFSS, NHANES, and the National Survey onDrug Use and Health (NSDUH). Several states conduct city- or county-level risk factor surveys by using BRFSS methods and questions, and an increasing number of cities and counties now conduct their own surveys based on or derived from BRFSS. A few states and local areas (Wisconsin and New York City, for example) conduct surveys based on NHIS or NHANES methods to provide state or local estimates of health outcomes and determinants.

This metric mirrors a relevant outcome, data are readily available to assess temporal trends and geographic and demographic variation, and mortality is amenable to population health interventions, although changes in the mortality metric may take years to appear. Life expectancy has the advantage of being more easily communicated to, and understood by, the public than mortality rates.

This metric has the advantages of the overall mortality metric, as above, and allows public health programs to monitor the effect of specific interventions on more specific outcomes. An example might be monitoring increases in life expectancy or reductions in motor vehicle injury-related mortality resulting from efforts to modify driver behavior and to make roads and vehicles safer.

Summary measures of population health, which combine information on death and nonfatal health outcomes, have the advantage of simplicity and parsimony and may be easier to communicate to the public and track over time than the series of basic measures previously recommended. If a summary measure is desirable, the health-adjusted life expectancy and healthy life years are good choices because they are based on life expectancy and use a population-based measure of HRQL, rather than anexpert judgment-based measure.

MethodsThrough the Healthy Aging Regional Collaborative, 8 area agencies delivered 82 workshops in 62 locations throughout South Florida. Spanish-speaking participants who attended workshops from October 1, 2008, through December 31, 2010, were aged 55 years or older, had at least 1 chronic condition, and completed baseline and post-test surveys were included in analysis (N = 682). Workshops consisted of six, 2.5-hour sessions offered once per week for 6 weeks. A self-report survey was administered at baseline and again at the end of program instruction. To assess differences in outcomes, a repeated measures general linear model was used, controlling for agency and baseline general health.

ResultsAll outcomes showed improvement at 6 weeks. Outcomes that improved significantly were self-efficacy to manage disease, perceived social and role activities limitations, time spent walking, and time spent performing other aerobic activities.

ConclusionImplementation of TCDS significantly improved 4 of 8 health promotion skills and behaviors of Spanish-speaking older adults in South Florida. A community-based implementation of TCDS has the potential to improve health outcomes for a diverse, Spanish-speaking, older adult population.

The purpose of this study was to examine whether TCDS improved symptom management self-efficacy, perceived social and role activities limitations, and time spent exercising, when implemented by community-based agencies through a large-scale collaborative effort in South Florida. The effort by HARC represented the first large-scale, community-based implementation of TCDS and was an example of Phase 4 translational research, the evaluation of health outcomes in a real-world setting. Since limited information is available on the translation of TCDS to practice settings, we focused on short-term program outcomes to evaluate effectiveness outside controlled trials. We hypothesized that program participants would show significant improvements over baseline scores for measures of self-efficacy, perceived social and role activities limitations, and time spent exercising when measured on the last day of workshop participation. 2b1af7f3a8