Decades-old, common ICU practices including deep sedation, immobilization, and limited family access are being challenged. We endeavoured to evaluate the relationship between ABCDEF bundle ...performance and patient-centered outcomes in critical care.
Prospective, multicenter, cohort study from a national quality improvement collaborative.
68 academic, community, and federal ICUs collected data during a 20-month period.
15,226 adults with at least one ICU day.
We defined ABCDEF bundle performance (our main exposure) in two ways: 1) complete performance (patient received every eligible bundle element on any given day) and 2) proportional performance (percentage of eligible bundle elements performed on any given day). We explored the association between complete and proportional ABCDEF bundle performance and three sets of outcomes: patient-related (mortality, ICU and hospital discharge), symptom-related (mechanical ventilation, coma, delirium, pain, restraint use), and system-related (ICU readmission, discharge destination). All models were adjusted for a minimum of 18 a priori determined potential confounders.
Complete ABCDEF bundle performance was associated with lower likelihood of seven outcomes: hospital death within 7 days (adjusted hazard ratio, 0.32; CI, 0.17-0.62), next-day mechanical ventilation (adjusted odds ratio AOR, 0.28; CI, 0.22-0.36), coma (AOR, 0.35; CI, 0.22-0.56), delirium (AOR, 0.60; CI, 0.49-0.72), physical restraint use (AOR, 0.37; CI, 0.30-0.46), ICU readmission (AOR, 0.54; CI, 0.37-0.79), and discharge to a facility other than home (AOR, 0.64; CI, 0.51-0.80). There was a consistent dose-response relationship between higher proportional bundle performance and improvements in each of the above-mentioned clinical outcomes (all p < 0.002). Significant pain was more frequently reported as bundle performance proportionally increased (p = 0.0001).
ABCDEF bundle performance showed significant and clinically meaningful improvements in outcomes including survival, mechanical ventilation use, coma, delirium, restraint-free care, ICU readmissions, and post-ICU discharge disposition.
Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel ...extension of the p-value-a second-generation p-value (pδ)-that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.
Abstract
In May 2020, the Journal published an opinion piece by a member of the Editorial Board, in which the author reviewed several papers and argued that using hydroxychloroquine ...(HCQ) + azithromycin (AZ) early to treat symptomatic coronavirus disease 2019 (COVID-19) cases in high-risk patients should be broadly applied. As members of the Journal’s Editorial Board, we are strongly supportive of open debate in science, which is essential even on highly contentious issues. However, we must also be thorough in our examination of the facts and open to changing our minds when new information arises. In this commentary, we document several important errors in the manuscript, review the literature presented, and demonstrate why it is not of sufficient quality to support scale up of HCQ + AZ, and then discuss the literature that has been generated since the publication, which also does not support use of this therapy. Unfortunately, the current scientific evidence does not support HCQ + AZ as an effective treatment for COVID-19, if it ever did, and even suggests many risks. Continuing to push the view that it is an essential treatment in the face of this evidence is irresponsible and harmful to the many people already suffering from infection.
Design Principles for Data Analysis McGowan, Lucy D'Agostino; Peng, Roger D.; Hicks, Stephanie C.
Journal of computational and graphical statistics,
04/2023, Letnik:
32, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The data revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the ...practice of data analysis is design thinking-the problem-solving process to understand the people for whom a solution is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. Here, we introduce design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide data showing variation of principles within and between producers of data analyses. Our work suggests a formal mechanism to describe data analyses based on design principles. These results provide guidance for future work in characterizing the data analytic process.
Supplementary materials
for this article are available online.
Abstract
The coronavirus disease 2019 (COVID-19) pandemic thrust the field of public health into the spotlight. For many epidemiologists, biostatisticians, and other public health professionals, this ...caused the professional aspects of our lives to collide with the personal, as friends and family reached out with concerns and questions. Learning how to navigate this space was new for many of us and required refining our communication style depending on context, setting, and audience. Some of us took to social media, utilizing our existing personal accounts to share information after sorting through and summarizing the rapidly emerging literature to keep loved ones safe. However, those in our lives sometimes asked unanswerable questions, or began distancing themselves when we suggested more stringent guidance than they had hoped for, causing additional stress during an already traumatic time. We often had to remind ourselves that we were also individuals experiencing this pandemic and that our time-intensive efforts were meaningful, relevant, and impactful. As this pandemic and other public health crises continue, we encourage members of our discipline to consider how we can best use shared lessons from this period and to recognize that our professional knowledge, when used in our personal lives, can promote, protect, and bolster confidence in public health.
BACKGROUND:Closed incision negative pressure therapy (ciNPT) is an emerging approach to managing closed incisions of patients at risk of postoperative complications. There are primarily 2 different ...commercially available ciNPT systems. Both systems consist of a single-use, battery-powered device and foam- or gauze-based peel-and-place dressing designed for closed incisions. These systems vary in design, and there are no data comparing outcomes between the 2 systems.
METHODS:We performed 2 separate meta-analyses to compare surgical site infection (SSI) rates postuse of (1) ciNPT with foam dressing (FOAM) versus conventional dressings and (2) ciNPT with multilayer absorbent dressing (MLA) versus conventional dressings.
RESULTS:Seven articles and 2 abstracts met inclusion criteria in the FOAM group (n = 489) versus the control group (n = 489) in meta-analysis 1; 7 articles and 1 abstract met inclusion criteria in the MLA group (n = 532) versus the control group (n = 540) in meta-analysis 2. Meta-analysis 1 showed that patients in the control group were 3.17 times more likely to develop an SSI compared with patients in the FOAM group weighted mean odds ratios of FOAM group versus control group was 3.17 (P < 0.0001) with the 95% confidence intervals of 2.17–4.65. Meta-analysis 2 showed no significant difference in SSI rates between patients in the MLA group and patients in the control group weighted mean odds ratios of MLA group versus control group was 1.70 (P = 0.08) with the 95% confidence intervals of 0.94–3.08.
CONCLUSIONS:Comparing outcomes of two different ciNPT systems with a common comparator (conventional dressings) may provide an interim basis for comparing ciNPT systems until further comparative evidence is available. More comparative research is required to determine outcomes in clinical practice.
The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral ...species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease.
COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).
In the 2nd paragraph of Acknowledgements, “S.M.B. is currently an employee at AstraZeneca, Gaithersburg, MD, USA, and may own stock or stock options; work was initially conducted at Georgetown ...University Medical Center, with writing, reviewing, and editing continued while working at AstraZeneca. Y.P. is now employed by Pfizer (subsequent to contributions to this project).” should read “S.M.B. is currently an employee at AstraZeneca, Gaithersburg, MD, USA, and may own stock or stock options. Y.P. is affiliated with Pfizer Worldwide Research; the author has no financial interests to declare and contributed as an author prior to joining Pfizer, and the work was not part of a Pfizer collaboration nor was it funded by Pfizer.” a Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA b Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, USA c Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, USA d Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, USA e Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA f Department of Biotechnology, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India g Biomedical Data Science and Machine Learning Group, German Center for Neurodegenerative Diseases, Tübingen, Germany h Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA i Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA j Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, University Park, Pennsylvania, USA k Mercer University, Macon, Georgia, USA l Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina, USA m Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA n Georgia State University, Atlanta, Georgia, USA o Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA p St. George’s University School of Medicine, St. George’s, Grenada q Department of Computer Science, University of Virginia grid.27755.32 , Charlottesville, Virginia, USA r Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA s Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA t Department of Clinical Sciences, Lund University, Lund, Sweden u University of Michigan School of Medicine, Ann Arbor, Michigan, USA v Department of Microbiology and Immunology, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA w Azimuth1, McLean, Virginia, USA x Allen Institute for Immunology, Seattle, Washington, USA y Department of Physics and Astronomy, University of California-Riverside, Riverside, California, USA z Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, California, USA aa Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil bb Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA cc Morgridge Institute for Research, Madison, Wisconsin, USA dd Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, Pennsylvania, USA
The community research fellows training (CRFT) program is a community-based participatory research (CBPR) initiative for the St. Louis area. This 15-week program, based on a Master in Public Health ...curriculum, was implemented by the Division of Public Health Sciences at Washington University School of Medicine and the Siteman Cancer Center.
We measure the knowledge gained by participants and evaluate participant and faculty satisfaction of the CRFT program both in terms of meeting learning objectives and actively engaging the community in the research process.
We conducted analyses on 44 community members who participated in the CRFT program and completed the baseline and follow-up knowledge assessments.
Knowledge gain is measured by a baseline and follow-up assessment given at the first and final session. Additionally, pre- and post-tests are given after the first 12 sessions. To measure satisfaction, program evaluations are completed by both the participants and faculty after each topic. Mid-way through the program, a mid-term evaluation was administered to assess the program's community engagement. We analyzed the results from the assessments, pre- and post-tests, and evaluations.
The CRFT participants' knowledge increased at follow-up as compared with baseline on average by a 16.5 point difference (p < 0.0001). Post-test scores were higher than pre-test scores for 11 of the 12 sessions. Both participants and faculty enjoyed the training and rated all session well.
The CRFT program was successful in increasing community knowledge, participant satisfaction, and faculty satisfaction. This success has enhanced the infrastructure for CBPR as well as led to CBPR pilot projects that address health disparities in the St. Louis Greater Metropolitan Area.
Objective: Few studies have examined how individuals respond to genomic risk information for common, chronic diseases. This randomized study examined differences in responses by type of genomic ...information (genetic test/family history) and disease condition (diabetes/heart disease), and by race/ethnicity in a medically underserved population. Methods: 1,057 English-speaking adults completed a survey containing 1 of 4 vignettes (2-by-2 randomized design). Differences in dependent variables (i.e., interest in receiving genomic assessment, discussing with doctor or family, changing health habits) by experimental condition and race/ethnicity were examined using chi-squared tests and multivariable regression analysis. Results: No significant differences were found in dependent variables by type of genomic information or disease condition. In multivariable models, Hispanics were more interested in receiving a genomic assessment than Whites (OR = 1.93; p < .0001); respondents with marginal (OR = 1.54; p = .005) or limited (OR = 1.85; p = .009) health literacy had greater interest than those with adequate health literacy. Blacks (OR = 1.78; p = .001) and Hispanics (OR = 1.85; p = .001) had greater interest in discussing information with family than Whites. Non-Hispanic Blacks (OR = 1.45; p = .04) had greater interest in discussing genomic information with a doctor than Whites. Blacks (β = −0.41; p < .001) and Hispanics (β = −0.25; p = .033) intended to change fewer health habits than Whites; health literacy was negatively associated with number of health habits participants intended to change. Conclusions: Findings suggest that race/ethnicity may affect responses to genomic risk information. Additional research could examine how cognitive representations of this information differ across racial/ethnic groups. Health literacy is also critical to consider in developing approaches to communicating genomic information.
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