Atomic Resonance and Scattering

Contains reports on four research projects.U.S. Air Force - Office of Scientific Research (Grant AFOSR-76-2972)National Science Foundation (Grant CHE79-02967)National Science Foundation (Grant PHY79-09743)Joint Services Electronics Program (Contract DAAG29-78-C-0020)Joint Services Electronics Program (Contract DAAG29-80-C-0104

Atomic Resonance and Scattering

Contains reports on eight research projects.National Science Foundation (Grant PHY77-09155)Joint Services Electronics Program (Contract DAAG29-78-C-0020)U. S. Department of Energy (Grant EG-77-S-02-4370)National Science Foundation (Grant DMR 77-10084)National Aeronautics and Space Administration (Grant NSG-1551)U. S. Air Force - Office of Scientific Research (Grant AFOSR-76-2972)National Science Foundation (Grant CHE76-81750

Lung function and microbiota diversity in cystic fibrosis

Abstract: Background: Chronic infection and concomitant airway inflammation is the leading cause of morbidity and mortality for people living with cystic fibrosis (CF). Although chronic infection in CF is undeniably polymicrobial, involving a lung microbiota, infection surveillance and control approaches remain underpinned by classical aerobic culture-based microbiology. How to use microbiomics to direct clinical management of CF airway infections remains a crucial challenge. A pivotal step towards leveraging microbiome approaches in CF clinical care is to understand the ecology of the CF lung microbiome and identify ecological patterns of CF microbiota across a wide spectrum of lung disease. Assessing sputum samples from 299 patients attending 13 CF centres in Europe and the USA, we determined whether the emerging relationship of decreasing microbiota diversity with worsening lung function could be considered a generalised pattern of CF lung microbiota and explored its potential as an informative indicator of lung disease state in CF. Results: We tested and found decreasing microbiota diversity with a reduction in lung function to be a significant ecological pattern. Moreover, the loss of diversity was accompanied by an increase in microbiota dominance. Subsequently, we stratified patients into lung disease categories of increasing disease severity to further investigate relationships between microbiota characteristics and lung function, and the factors contributing to microbiota variance. Core taxa group composition became highly conserved within the severe disease category, while the rarer satellite taxa underpinned the high variability observed in the microbiota diversity. Further, the lung microbiota of individual patient were increasingly dominated by recognised CF pathogens as lung function decreased. Conversely, other bacteria, especially obligate anaerobes, increasingly dominated in those with better lung function. Ordination analyses revealed lung function and antibiotics to be main explanators of compositional variance in the microbiota and the core and satellite taxa. Biogeography was found to influence acquisition of the rarer satellite taxa. Conclusions: Our findings demonstrate that microbiota diversity and dominance, as well as the identity of the dominant bacterial species, in combination with measures of lung function, can be used as informative indicators of disease state in CF. BBFJdPr3cu-jH3LTAhe361Video Abstrac

NEJM -- Capturing the Unexpected Benefits of Medical Research

Remarkable advances have been made recently in our understanding of the molecular and genetic bases of disease. The potential therapeutic opportunities offered by these scientific findings, combined with the expanding needs of an aging population, have led to broad-based congressional support for increases in the National Institutes of Health budget. These developments have put into sharp relief the question of how to allocate growing budgetary resources among the various categories of medical research. In addition to the need to support basic-science research, investigators and policy analysts alike have recently emphasized the need to invest in translational research and clinical evaluative research. The rationale for supporting translational research, which is typically physiologic in nature, is that it is needed to convert the insights provided by basic biomedical science into new methods of diagnosis and therapy. 1 A case in point is the research that was based on fundamental observations about how renal tubules handle sodium and that led, in turn, to the development of new diuretic agents and methods of managing sodium imbalance. The argument for supporting clinical evaluative research is that it is needed to assess the efficacy and safety of such new interventions. There is, however, a much stronger rationale for the support of both types of research. Innovation is a learning process that takes place over time, and a fundamental aspect of learning is the reduction of uncertainty. The end of the research-and-development process does not entail the elimination of all, or even most, of the uncertainties surrounding medical innovation. Among these uncertainties are benefits that were unanticipated when the research was performed. Unanticipated uses of diagnostic and therapeutic interventions are often identified many years after their introduction. Indeed, widespread use is often an essential precondition for the identification of new applications, and clinical practice itself is thus a particularly important source of medical innovation. The unexpected and anomalous results of clinical experience thus pose new questions for basic biomedical research and enrich its ultimate payoff. What measures might be taken to broaden the range of indications for new therapies and to accelerate their discovery and introduction? Should these measures be publicly or privately financed? It took half a century for the cardiovascular benefits of aspirin, the most widely used drug in the world, to be recognized and nearly 40 more years before it was widely used for cardiovascular indications. Could this process have been expedited? To address these questions, it is necessary to examine the pathways by which new indications for therapies are discovered. Why Uncertainty Endures Successful research and development puts an end to some uncertainties but opens the gate to others, not for want of methodologic acumen, but because the complexity of humans limits the ability to predict the effect of a new intervention. Alpha-adrenergic-receptor antagonists, for instance, were first tested for hypertension

Impact of Socioeconomic Status Measures on Hospital Profiling in New York City

BackgroundCurrent 30-day readmission models used by the Center for Medicare and Medicaid Services for the purpose of hospital-level comparisons lack measures of socioeconomic status (SES). We examined whether the inclusion of an SES measure in 30-day congestive heart failure readmission models changed hospital risk-standardized readmission rates in New York City (NYC) hospitals.Methods and resultsUsing a Centers for Medicare & Medicaid Services (CMS)-like model, we estimated 30-day hospital-level risk-standardized readmission rates by adjusting for age, sex, and comorbid conditions. Next, we examined how hospital risk-standardized readmission rates changed relative to the NYC mean with inclusion of the Agency for Healthcare Research and Quality (AHRQ)-validated SES index score. In a secondary analysis, we examined whether inclusion of the AHRQ SES index score in 30-day readmission models disproportionately impacted the risk-standardized readmission rates of minority-serving hospitals. Higher AHRQ SES scores, indicators of higher SES, were associated with lower odds (0.99) of 30-day readmission (P<0.019). The addition of the AHRQ SES index did not change the model's C statistic (0.63). After adjustment for the AHRQ SES index, 1 hospital changed status from worse than the NYC average to no different than the NYC average. After adjustment for the AHRQ SES index, 1 NYC minority-serving hospital was reclassified from worse to no different than average.ConclusionsAlthough patients with higher SES were less likely to be admitted, the impact of SES on readmission was small. In NYC, inclusion of the AHRQ SES score in a CMS-based model did not impact hospital-level profiling based on 30-day readmission

Impact of Socioeconomic Status Measures on Hospital Profiling in New York City

BackgroundCurrent 30-day readmission models used by the Center for Medicare and Medicaid Services for the purpose of hospital-level comparisons lack measures of socioeconomic status (SES). We examined whether the inclusion of an SES measure in 30-day congestive heart failure readmission models changed hospital risk-standardized readmission rates in New York City (NYC) hospitals.Methods and resultsUsing a Centers for Medicare & Medicaid Services (CMS)-like model, we estimated 30-day hospital-level risk-standardized readmission rates by adjusting for age, sex, and comorbid conditions. Next, we examined how hospital risk-standardized readmission rates changed relative to the NYC mean with inclusion of the Agency for Healthcare Research and Quality (AHRQ)-validated SES index score. In a secondary analysis, we examined whether inclusion of the AHRQ SES index score in 30-day readmission models disproportionately impacted the risk-standardized readmission rates of minority-serving hospitals. Higher AHRQ SES scores, indicators of higher SES, were associated with lower odds (0.99) of 30-day readmission (P<0.019). The addition of the AHRQ SES index did not change the model's C statistic (0.63). After adjustment for the AHRQ SES index, 1 hospital changed status from worse than the NYC average to no different than the NYC average. After adjustment for the AHRQ SES index, 1 NYC minority-serving hospital was reclassified from worse to no different than average.ConclusionsAlthough patients with higher SES were less likely to be admitted, the impact of SES on readmission was small. In NYC, inclusion of the AHRQ SES score in a CMS-based model did not impact hospital-level profiling based on 30-day readmission