Cardiac arrest: An interdisciplinary scoping review of clinical literature from 2020.

Objectives: The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct an annual search of peer-reviewed literature relevant to cardiac arrest. Now in its third year, the goals of the review are to highlight annual updates in the interdisciplinary world of clinical cardiac arrest research with a focus on clinically relevant and impactful clinical and population-level studies from 2020. Methods: A search of PubMed using keywords related to clinical research in cardiac arrest was conducted. Titles and abstracts were screened for relevance and sorted into 7 categories: Epidemiology & Public Health Initiatives; Prehospital Resuscitation, Technology & Care; In-Hospital Resuscitation & Post-Arrest Care; Prognostication & Outcomes; Pediatrics; Interdisciplinary Guidelines & Reviews; and a new section dedicated to the coronavirus disease 2019 (COVID-19) pandemic. Screened manuscripts underwent standardized scoring of methodological quality and impact on the respective fields by reviewer teams lead by a subject matter expert editor. Articles scoring higher than 99 percentiles by category were selected for full critique. Systematic differences between editors' and reviewers' scores were assessed using Wilcoxon signed-rank test. Results: A total of 3594 articles were identified on initial search; of these, 1026 were scored after screening for relevance and deduplication, and 51 underwent full critique. The leading category was Prehospital Resuscitation, Technology & Care representing 35% (18/51) of fully reviewed articles. Four COVID-19 related articles were included for formal review that was attributed to a relative lack of high-quality data concerning cardiac arrest and COVID-19 specifically by the end of the 2020 calendar year. No significant differences between editor and reviewer scoring were found among review articles (P = 0.697). Among original research articles, section editors scored a median 1 point (interquartile range, 0-3; P < 0.01) less than reviewers. Conclusions: Several clinically relevant studies have added to the evidence base for the management of cardiac arrest patients including methods for prognostication of neurologic outcome following arrest, airway management strategy, timing of coronary intervention, and methods to improve expeditious performance of key components of resuscitation such as chest compressions in adults and children.

Unrecognized cardiac arrests: A one-year review of audio from emergency medical dispatch calls.

OBJECTIVES: Immediate recognition of out-of-hospital cardiac arrest (OHCA) by Emergency Medical Dispatch (EMD) operators is crucial to facilitate timely initiation of telephone cardiopulmonary resuscitation (T-CPR) and to enable the appropriate level of Emergency Medical Services (EMS) response. The goal of this study was to identify patterns that can increase EMD-level recognition of cardiac arrests prior to EMS arrival. METHODS: The Combined Communications Center in Alachua County, Florida provided audio recordings of all emergency calls from January 1, 2018 to November 16, 2018 dispatched as a chief complaint other than OHCA, but later identified as cardiac arrest. A multi-disciplinary medical team transcribed and analyzed the calls to determine common themes and trends. RESULTS: Out of an initial 81 calls meeting inclusion criteria, 69 were immediately recognized as OHCA by EMDs, leaving 12 calls of unrecognized OHCA. In 11 of 12 calls respiratory issues were described to EMD. In 10 of 12 calls the subject was described as unconscious, and in the other 2 calls, the subject lost consciousness during the call. CONCLUSIONS: Lack of recognition of OHCA by EMD occurred in most calls due to difficulty communicating the subject's respiratory status. Further emphasis should be placed on identifying non-viable respirations in unconscious patients in EMD training and algorithms to increase recognition of OHCA and initiation of T-CPR. A multi-year review of a comparable dataset from geographically and socioeconomically diverse regions in the United States can validate and expand these preliminary trends.

Innovation in resuscitation: A novel clinical decision display system for advanced cardiac life support.

INTRODUCTION: The Advanced Cardiac Life Support (ACLS) Clinical Decision Display System (CDDS) is a novel application designed to optimize team organization and facilitate decision-making during ACLS resuscitations. We hypothesized that resuscitation teams would more consistently adhere to ACLS guideline time intervals in simulated resuscitation scenarios with the CDDS compared to without. METHODS: We conducted a simulation-based, non-blinded, randomized, crossover-design study with resuscitation teams comprised of Emergency Medicine physicians, registered nurses, critical care technicians, and paramedics. Each team performed 4 ACLS scenarios in randomized sequences, half with the CDDS and half without. We analyzed the resuscitations and recorded the times of interventions that have defined intervals by ACLS: rhythm checks, epinephrine administration, and shock delivery. In addition, we surveyed each resuscitation team regarding their experience using the CDDS. RESULTS: On average, teams performed rhythm checks 4.9 s closer to ACLS guidelines with the CDDS (p = 0.0358). Teams were also more consistent; on average, teams reduced the variation of time between consecutive doses of epinephrine by 45% (p = 0.0001) and defibrillation by 47% (p < 0.0001). Ninety-eight percent of participants indicated they would use the CDDS if available in real cardiac arrests. CONCLUSIONS: This study demonstrates that the CDDS improves the accuracy and precision of timed ACLS interventions in a simulated setting. Resuscitation teams were strongly in favor of utilizing the CDDS in clinical practice. Further investigations of the introduction of the platform into real time clinical environments will be needed to assess true efficacy and patient outcomes.

Cardiac arrest: An interdisciplinary scoping review of the literature from 2019.

OBJECTIVES: The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct a systematic annual search of peer-reviewed literature relevant to cardiac arrest. Now in its second year, the goals of the review are to illustrate best practices in research and help reduce compartmentalization of knowledge by disseminating clinically relevant advances in the field of cardiac arrest across disciplines. METHODS: An electronic search of PubMed using keywords related to cardiac arrest was conducted. Title and abstracts retrieved by these searches were screened for relevance, classified by article type (original research or review), and sorted into 7 categories. Screened manuscripts underwent standardized scoring of overall methodological quality and impact on the categorized fields of study by reviewer teams lead by a subject-matter expert editor. Articles scoring higher than 99 percentiles by category-type were selected for full critique. Systematic differences between editors' and reviewers' scores were assessed using Wilcoxon signed-rank test. RESULTS: A total of 3348 articles were identified on initial search; of these, 1364 were scored after screening for relevance and deduplication, and forty-five underwent full critique. Epidemiology & Public Health represented 24% of fully reviewed articles with Prehospital Resuscitation, Technology & Care, and In-Hospital Resuscitation & Post-Arrest Care Categories both representing 20% of fully reviewed articles. There were no significant differences between editor and reviewer scoring. CONCLUSIONS: The sheer number of articles screened is a testament to the need for an accessible source calling attention to high-quality and impactful research and serving as a high-yield reference for clinicians and scientists seeking to follow the ever-growing body of cardiac arrest-related literature. This will promote further development of the unique and interdisciplinary field of cardiac arrest medicine.

Interactions between specific phytoplankton and bacteria affect lake bacterial community succession.

Time-series observations and a phytoplankton manipulation experiment were combined to test the hypothesis that phytoplankton succession effects changes in bacterial community composition. Three humic lakes were sampled weekly May-August and correlations between relative abundances of specific phytoplankton and bacterial operational taxonomic units (OTUs) in each time series were determined. To experimentally characterize the influence of phytoplankton, bacteria from each lake were incubated with phytoplankton from one of the three lakes or no phytoplankton. Following incubation, variation in bacterial community composition explained by phytoplankton treatment increased 65%, while the variation explained by bacterial source decreased 64%. Free-living bacteria explained, on average, over 60% of the difference between phytoplankton and corresponding no-phytoplankton control treatments. Fourteen out of the 101 bacterial OTUs that exhibited positively correlated patterns of abundance with specific algal populations in time-series observations were enriched in mesocosms following incubation with phytoplankton, and one out of 59 negatively correlated bacterial OTUs was depleted in phytoplankton treatments. Bacterial genera enriched in mesocosms containing specific phytoplankton assemblages included Limnohabitans (clade betI-A), Bdellovibrio and Mitsuaria. These results suggest that effects of phytoplankton on certain bacterial populations, including bacteria tracking seasonal changes in algal-derived organic matter, result in correlations between algal and bacterial community dynamics.

Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes.

Recent studies have suggested a bacterial role in the development of autoimmune disorders including type 1 diabetes (T1D). Over 30 billion nucleotide bases of Illumina shotgun metagenomic data were analyzed from stool samples collected from four pairs of matched T1D case-control subjects collected at the time of the development of T1D associated autoimmunity (i.e., autoantibodies). From these, approximately one million open reading frames were predicted and compared to the SEED protein database. Of the 3,849 functions identified in these samples, 144 and 797 were statistically more prevalent in cases and controls, respectively. Genes involved in carbohydrate metabolism, adhesions, motility, phages, prophages, sulfur metabolism, and stress responses were more abundant in cases while genes with roles in DNA and protein metabolism, aerobic respiration, and amino acid synthesis were more common in controls. These data suggest that increased adhesion and flagella synthesis in autoimmune subjects may be involved in triggering a T1D associated autoimmune response. Extensive differences in metabolic potential indicate that autoimmune subjects have a functionally aberrant microbiome. Mining 16S rRNA data from these datasets showed a higher proportion of butyrate-producing and mucin-degrading bacteria in controls compared to cases, while those bacteria that produce short chain fatty acids other than butyrate were higher in cases. Thus, a key rate-limiting step in butyrate synthesis is more abundant in controls. These data suggest that a consortium of lactate- and butyrate-producing bacteria in a healthy gut induce a sufficient amount of mucin synthesis to maintain gut integrity. In contrast, non-butyrate-producing lactate-utilizing bacteria prevent optimal mucin synthesis, as identified in autoimmune subjects.

Toward defining the autoimmune microbiome for type 1 diabetes.

Several studies have shown that gut bacteria have a role in diabetes in murine models. Specific bacteria have been correlated with the onset of diabetes in a rat model. However, it is unknown whether human intestinal microbes have a role in the development of autoimmunity that often leads to type 1 diabetes (T1D), an autoimmune disorder in which insulin-secreting pancreatic islet cells are destroyed. High-throughput, culture-independent approaches identified bacteria that correlate with the development of T1D-associated autoimmunity in young children who are at high genetic risk for this disorder. The level of bacterial diversity diminishes overtime in these autoimmune subjects relative to that of age-matched, genotype-matched, nonautoimmune individuals. A single species, Bacteroides ovatus, comprised nearly 24% of the total increase in the phylum Bacteroidetes in cases compared with controls. Conversely, another species in controls, represented by the human firmicute strain CO19, represented nearly 20% of the increase in Firmicutes compared with cases overtime. Three lines of evidence are presented that support the notion that, as healthy infants approach the toddler stage, their microbiomes become healthier and more stable, whereas, children who are destined for autoimmunity develop a microbiome that is less diverse and stable. Hence, the autoimmune microbiome for T1D may be distinctly different from that found in healthy children. These data also suggest bacterial markers for the early diagnosis of T1D. In addition, bacteria that negatively correlated with the autoimmune state may prove to be useful in the prevention of autoimmunity development in high-risk children.

PANGEA: pipeline for analysis of next generation amplicons.

High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including pre-processing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the chi(2) step, are joined into one program called the 'backbone'.