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Background: Continued breathing following ventricular fibrillation has here-to-fore not been described.Methods: We analyzed the spontaneous ventilatory activity during the first several minutes of ventricular fibrillation (VF) in our isoflurane anesthesized swine model of out-of-hospital cardiac arrest. The frequency and type of ventilatory activity was monitored by pneumotachometer and main stream infrared capnometer and analyzed in 61 swine during the first 3 to 6 minutes of untreated VF.Results: During the first minute of VF, the air flow pattern in all 61 swine was similar to those recorded during regular spontaneous breathing during anesthesia and was clearly different from the patterns of gasping. The average rate of continued breathing during the first minute of untreated VF was 10 breaths per minute. During the second minute of untreated VF, spontaneous breathing activity either stopped or became typical of gasping. During minutes 2 to 5 of untreated VF, most animals exhibited very slow spontaneous ventilatory activity with a pattern typical of gasping; and the pattern of gasping was crescendo-decrescendo, as has been previously reported. In the absence of therapy, all ventilatory activity stopped 6 minutes after VF cardiac arrest.Conclusion: In our swine model of VF cardiac arrest, we documented that normal breathing continued for the first minute following cardiac arrest. [ABSTRACT FROM AUTHOR]

Background: Retaining participants over time is a frequent challenge in research studies evaluating long-term health outcomes. This study's objective was to compare the impact of prepaid and postpaid incentives on response to a six-month follow-up survey., Methods: We conducted an experiment to compare response between participants randomized to receive either prepaid or postpaid cash card incentives within a multisite study of children under 15 years in age who were hospitalized for a serious, severe, or critical injury. Participants were parents or guardians of enrolled children. The primary outcome was survey response. We also examined whether demographic characteristics were associated with response and if incentive timing influenced the relationship between demographic characteristics and response. We evaluated whether incentive timing was associated with the number of calls needed for contact., Results: The study enrolled 427 children, and parents of 420 children were included in this analysis. Follow-up survey response did not differ according to the assigned treatment arm, with the percentage of parents responding to the survey being 68.1% for the prepaid incentive and 66.7% with the postpaid incentive. Likelihood of response varied by demographics. Spanish-speaking parents and parents with lower income and lower educational attainment were less likely to respond. Parents of Hispanic/Latino children and children with Medicaid insurance were also less likely to respond. We found no relationship between the assigned incentive treatment and the demographics of respondents compared to non-respondents., Conclusions: Prepaid and postpaid incentives can obtain similar participation in longitudinal pediatric critical care outcomes research. Incentives alone do not ensure retention of all demographic subgroups. Strategies for improving representation of hard-to-reach populations are needed to address health disparities and ensure the generalizability of studies using these results., (© 2021. The Author(s).)

Background: Vaccine clinical studies typically provide time-resolved data on adaptive response read-outs in response to the administration of that particular vaccine to a cohort of individuals. However, modeling such data is challenged by the properties of these time-resolved profiles such as non-linearity, scarcity of measurement points, scheduling of the vaccine at multiple time points. Linear Mixed Models (LMM) are often used for the analysis of longitudinal data but their use in these time-resolved immunological data is not common yet. Apart from the modeling challenges mentioned earlier, selection of the optimal model by using information-criterion-based measures is far from being straight-forward. The aim of this study is to provide guidelines for the application and selection of LMMs that deal with the challenging characteristics of the typical data sets in the field of vaccine clinical studies., Methods: We used antibody measurements in response to Hepatitis-B vaccine with five different adjuvant formulations for demonstration purposes. We built piecewise-linear, piecewise-quadratic and cubic models with transformations of the axes with pre-selected or optimized knot locations where time is a numerical variable. We also investigated models where time is categorical and random effects are shared intercepts between different measurement points. We compared all models by using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Deviance Information Criterion (DIC), variations of conditional AIC and by visual inspection of the model fit in the light of prior biological information., Results: There are various ways of dealing with the challenges of the data which have their own advantages and disadvantages. We explain these in detail here. Traditional information-criteria-based measures work well for the coarse selection of the model structure and complexity, however are not efficient at fine tuning of the complexity level of the random effects., Conclusions: We show that common statistical measures for optimal model complexity are not sufficient. Rather, explicitly accounting for model purpose and biological interpretation is needed to arrive at relevant models., Trial Registration: Clinical trial registration number for this study: NCT00805389, date of registration: December 9, 2008 (pro-active registration).

Background: The "Age of Blood in Children in Pediatric Intensive Care Unit" (ABC PICU) study is a randomized controlled trial (RCT) that aims to determine if red blood cell (RBC) unit storage age affects outcomes in critically ill children. While RBCs can be stored for up to 42 days in additive solutions, their efficacy and safety after long-term storage have been challenged. Preclinical and clinical observational evidence suggests loss of efficacy and lack of safety of older RBC units, especially in more vulnerable populations such as critically ill children. Because there is a belief that shorter storage will improve outcomes, some physicians and institutions systematically transfuse fresh RBCs to children. Conversely, the standard practice of blood banks is to deliver the oldest available RBC unit (first-in, first-out policy) in order to decrease wastage., Methods/design: The ABC PICU study, is a double-blind superiority trial comparing the development of "New or Progressive Multiple Organ Dysfunction Syndrome" (NPMODS) in 1538 critically ill children randomized to either transfusion with RBCs stored for ≤ 7 days or to standard-issue RBCs (oldest in inventory). Patients are being recruited from 52 centers in the US, Canada, France, Italy, and Israel., Discussion: The ABC PICU study should have significant implications for blood procurement services. A relative risk reduction of 33% is postulated in the short-storage arm. If a difference is found, this will indicate that fresher RBCs do improve outcomes in the pediatric intensive care unit population and would justify that use in critically ill children. If no difference is found, this will reassure clinicians and transfusion medicine specialists regarding the safety of the current system of allocating the oldest RBC unit in inventory and will discourage clinicians from preferentially requesting fresher blood for critically ill children., Trial Registration: ClinicalTrials.gov, ID: NCT01977547 . Registered on 6 November 2013.

Background: Quality of cardiopulmonary resuscitation (CPR) is associated with survival, but recommended guidelines are often not met, and less than half the children with an in-hospital arrest will survive to discharge. A single-center before-and-after study demonstrated that outcomes may be improved with a novel training program in which all pediatric intensive care unit staff are encouraged to participate in frequent CPR refresher training and regular, structured resuscitation debriefings focused on patient-centric physiology., Methods/design: This ongoing trial will assess whether a program of structured debriefings and point-of-care bedside practice that emphasizes physiologic resuscitation targets improves the rate of survival to hospital discharge with favorable neurologic outcome in children receiving CPR in the intensive care unit. This study is designed as a hybrid stepped-wedge trial in which two of ten participating hospitals are randomly assigned to enroll in the intervention group and two are assigned to enroll in the control group for the duration of the trial. The remaining six hospitals enroll initially in the control group but will transition to enrolling in the intervention group at randomly assigned staggered times during the enrollment period., Discussion: To our knowledge, this is the first implementation of a hybrid stepped-wedge design. It was chosen over a traditional stepped-wedge design because the resulting improvement in statistical power reduces the required enrollment by 9 months (14%). However, this design comes with additional challenges, including logistics of implementing an intervention prior to the start of enrollment. Nevertheless, if results from the single-center pilot are confirmed in this trial, it will have a profound effect on CPR training and quality improvement initiatives., Trial Registration: ClinicalTrials.gov, NCT02837497 . Registered on July 19, 2016.

Background: Effective cardiopulmonary resuscitation is a critical component of the pre-hospital treatment of cardiac arrest victims. Mechanical chest compression (MCC) devices enable the delivery of MCC waveforms that could not be delivered effectively by hand. While chest compression generated blood flow has been studied for more than 50 years, the relation between sternum kinematics (depth over time) and the resulting blood flow have not been well described. Using a five parameter MCC model, we studied the effect of MCC depth, MCC release time, and their interaction on MCC generated blood flow in a highly instrumented swine model of cardiac arrest., Methods: MCC hemodynamics were studied in 17 domestic swine (~30 kg) using multiple extra-vascular flow probes and standard physiological monitoring. After 10 min of untreated ventricular fibrillation, mechanical MCC were started. MCC varied such that sternal release occurred over 100, 200, or 300 ms. MCC were delivered at a rate of 100 per min and at a depth of 1.25″ (n = 9) or at a depth of 1.9″ (n = 8) for a total of 18 min. Transitions between release times occurred every 2 min and were randomized. Linear Mixed Models were used to estimate the effect of MCC depth, MCC release time, and the interaction between MCC depth and release time on physiological outcomes., Results: Blood pressures were optimized by a 200 ms release. End tidal carbon dioxide (EtCO2) was optimized by a 100 ms release. Blood flows were significantly lower at a 300 ms release than at either a 100 or 200 ms release (p < 0.05). 1.9″ deep MCC improved EtCO2, right atrial pressure, coronary perfusion pressure, inferior vena cava blood flow, carotid blood flow, and renal vein blood flow relative to 1.25″ MCC., Conclusions: Deeper MCC improved several hemodynamic parameters. Chest compressions with a 300 ms release time generated less blood flow than chest compressions with faster release times. MCC release time is an important quantitative metric of MCC quality and, if optimized, could improve MCC generated blood flows and pressures.

Background: High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account., Results: We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks., Conclusion: Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach)., Availability: The additional file contains a MATLAB implementation of the sparse simultaneous component method.

Background: In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set., Results: In the simulation study, MxLSCA-P outperforms SCA-P in terms of recovery of the true underlying scores of the common mode and of the true values underlying the data entries. MxLSCA-P further performed especially better when the simulated data blocks were subject to different noise levels. In the analysis of an E. coli metabolomics data set, MxLSCA-P provided a slightly better and more consistent interpretation., Conclusion: MxLSCA-P is a promising addition to the SCA family. The analysis of coupled functional genomics data blocks could benefit from its ability to take different noise levels per data block into consideration and improve the recovery of the true patterns underlying the data. Moreover, the maximum likelihood based approach underlying MxLSCA-P could be extended to custom-made solutions to specific problems encountered.

Background: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability., Results: Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis., Conclusion: Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.