Pulseless electrical activity in in-hospital cardiac arrest - A crossroad for decisions

Background PEA is often seen during resuscitation, either as the presenting clinical state in cardiac arrest or as a secondary rhythm following transient return of spontaneous circulation (ROSC), ventricular fibrillation/tachycardia (VF/VT), or asystole (ASY). The aim of this study was to explore and quantify the evolution from primary/secondary PEA to ROSC in adults during in-hospital cardiac arrest (IHCA). Methods We analyzed 700 IHCA episodes at one Norwegian hospital and three U.S. hospitals at different time periods between 2002 and 2021. During resuscitation ECG, chest compressions, and ventilations were recorded by defibrillators. Each event was manually annotated using a graphical application. We quantified the transition intensities, i.e., the propensity to change from PEA to another clinical state using time-to-event statistical methods. Results Most patients experienced PEA at least once before achieving ROSC or being declared dead. Time average transition intensities to ROSC from primary PEA (n = 230) and secondary PEA after ASY (n = 72) were 0.1 per min, peaking at 4 and 7 minutes, respectively; thus, a patient in these types of PEA showed a 10% chance of achieving ROSC in one minute. Much higher transition intensities to ROSC, average of 0.15 per min, were observed for secondary PEA after VF/VT (n = 83) or after ROSC (n = 134). Discussion PEA is a crossroad in which the subsequent course is determined. The four distinct presentations of PEA behave differently on important characteristics. A transition to PEA during resuscitation should encourage the resuscitation team to continue resuscitative efforts.This work was partially supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades through grant RTI2018-101475-BI00, jointly with the Fondo Europeo de Desarrollo Regional (FEDER), and by the Basque Government through grant IT1229-19. This study has been made possible by DAM foundation and the Norwegian Health Association

GeneTools – application for functional annotation and statistical hypothesis testing

BACKGROUND: Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. RESULTS: GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis. CONCLUSION: GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.n

Real-time compression feedback for patients with in-hospital cardiac arrest: a multi-center randomized controlled clinical trial

Objective: To determine if real-time compression feedback using a non-automated hand-held device improves patient outcomes from in-hospital cardiac arrest (IHCA). Methods: We conducted a prospective, randomized, controlled, parallel study (no crossover) of patients with IHCA in the mixed medical–surgical intensive care units (ICUs) of eight academic hospitals. Patients received either standard manual chest compressions or compressions performed with real-time feedback using the Cardio First Angel™ (CFA) device. The primary outcome was sustained return of spontaneous circulation (ROSC), and secondary outcomes were survival to ICU and hospital discharge. Results: One thousand four hundred fifty-four subjects were randomized; 900 were included. Sustained ROSC was significantly improved in the CFA group (66.7% vs. 42.4%, P < 0.001), as was survival to ICU discharge (59.8% vs. 33.6%) and survival to hospital discharge (54% vs. 28.4%, P < 0.001). Outcomes were not affected by intra-group comparisons based on intubation status. ROSC, survival to ICU, and hospital discharge were noted to be improved in inter-group comparisons of non-intubated patients, but not intubated ones. Conclusion: Use of the CFA compression feedback device improved event survival and survival to ICU and hospital discharge

Causes of rail staff fatigue: results of qualitative analysis and a diary study

The purpose of this study was to investigate the causes of fatigue among rail staff by analysing qualitative data and conducting an online diary study. It had a closer look at the experience of fatigue among rail staff and brought a more detailed blueprint picture of fatigue and its causes in the rail staff’s real-life. Study 1 analysed 133 responses of qualitative data from rail staff, and Study 2 was a diary study examining fatigue and its related risk factors before and after work, on the first and the last day of a working week in 19 rail staff. The findings from the two studies, using different methodologies, showed similar results that fatigue among rail staff was a result of heavy workload and a high workload would further increase fatigue. Fatigue before work mainly resulted from sleep quality, length of sleep, and the time spent on commute, while fatigue after work resulted from the perceived workload and shift type. Evidence has demonstrated that overtime work, specific shift patterns, insufficient rest days between opposed shifts, and poor timing of breaks during work were also associated with fatigue