Race-Specific Spirometry Equations Do Not Improve Models of Dyspnea and Quantitative Chest CT Phenotypes.

BACKGROUND: Race-specific spirometry reference equations are used globally to interpret lung function for clinical, research, and occupational purposes, but inclusion of race is under scrutiny. RESEARCH QUESTION: Does including self-identified race in spirometry reference equation formation improve the ability of predicted FEV1 values to explain quantitative chest CT abnormalities, dyspnea, or Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification? STUDY DESIGN AND METHODS: Using data from healthy adults who have never smoked in both the National Health and Nutrition Survey (2007-2012) and COPDGene study cohorts, race-neutral, race-free, and race-specific prediction equations were generated for FEV1. Using sensitivity/specificity, multivariable logistic regression, and random forest models, these equations were applied in a cross-sectional analysis to populations of individuals who currently smoke and individuals who formerly smoked to determine how they affected GOLD classification and the fit of models predicting quantitative chest CT phenotypes or dyspnea. RESULTS: Race-specific equations showed no advantage relative to race-neutral or race-free equations in models of quantitative chest CT phenotypes or dyspnea. Race-neutral reference equations reclassified up to 19% of Black participants into more severe GOLD classes, while race-neutral/race-free equations may improve model fit for dyspnea symptoms relative to race-specific equations. INTERPRETATION: Race-specific equations offered no advantage over race-neutral/race-free equations in three distinct explanatory models of dyspnea and chest CT scan abnormalities. Race-neutral/race-free reference equations may improve pulmonary disease diagnoses and treatment in populations highly vulnerable to lung disease.

Simulation for Clinical Preparedness in Pediatric Emergencies: A Pilot Study.

Positive outcomes for pediatric in-hospital cardiopulmonary arrest remain low with little change in mortality rates. Infrequently used clinical knowledge and skills decline quickly, contributing to poor quality of resuscitation. The aim of this pilot study exploring the outcomes of repeated pediatric mock code simulations with structured debriefing demonstrated statistically significant differences in participants' knowledge of pediatric emergencies, with no significant decline in resuscitation skills. This provides an evidence-based approach for professional development educators working with nurses on pediatric emergencies.

From Chaos to Competency: Implementing a New Competency Model in a Multihospital System.

The demonstration of nurses' competency has been a challenge for nurse educators for a multitude of reasons. Standardizing competencies across a health care system is especially challenging. One multihospital health care system embarked on a journey to standardize and implement competencies across the system. Using a collaborative approach, the Wright model of competency assessment was selected and implemented across the system. Lessons learned include (a) the full engagement of Wright's model by all nursing stakeholders is essential, (b) indicators and outcomes must align with quality improvement data, and (c) the needs of each unit must be individualized, and the methods of verification must be closely aligned with the measurable competency. After implementation of this model, the health system now has a standard definition, identification, methodology, and evaluation for competencies. In addition, there are now standard time frames for assessment and evaluation and increased accountability for staff involvement.

Assessing coral reefs on a Pacific-wide scale using the microbialization score.

The majority of the world's coral reefs are in various stages of decline. While a suite of disturbances (overfishing, eutrophication, and global climate change) have been identified, the mechanism(s) of reef system decline remain elusive. Increased microbial and viral loading with higher percentages of opportunistic and specific microbial pathogens have been identified as potentially unifying features of coral reefs in decline. Due to their relative size and high per cell activity, a small change in microbial biomass may signal a large reallocation of available energy in an ecosystem; that is the microbialization of the coral reef. Our hypothesis was that human activities alter the energy budget of the reef system, specifically by altering the allocation of metabolic energy between microbes and macrobes. To determine if this is occurring on a regional scale, we calculated the basal metabolic rates for the fish and microbial communities at 99 sites on twenty-nine coral islands throughout the Pacific Ocean using previously established scaling relationships. From these metabolic rate predictions, we derived a new metric for assessing and comparing reef health called the microbialization score. The microbialization score represents the percentage of the combined fish and microbial predicted metabolic rate that is microbial. Our results demonstrate a strong positive correlation between reef microbialization scores and human impact. In contrast, microbialization scores did not significantly correlate with ocean net primary production, local chla concentrations, or the combined metabolic rate of the fish and microbial communities. These findings support the hypothesis that human activities are shifting energy to the microbes, at the expense of the macrobes. Regardless of oceanographic context, the microbialization score is a powerful metric for assessing the level of human impact a reef system is experiencing.

A diffusion approach to labeling rows and columns in an irregular array.

A robust algorithm is presented for labeling rows and columns in an irregular array. The algorithm is based on hierarchical pattern matching to a local lattice, which is used as a template. Starting from the best local match, the pattern is expanded hierarchically to encompass the entire array. An application to labeling digitized images of an array of tissue sections mounted on a microscope slide is discussed.