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Aims: The PARAMEDIC-3 trial evaluates the clinical and cost-effectiveness of an intraosseous first strategy, compared with an intravenous first strategy, for drug administration in adults who have sustained an out-of-hospital cardiac arrest. Methods: PARAMEDIC-3 is a pragmatic, allocation concealed, open-label, multi-centre, superiority randomised controlled trial. It will recruit 15,000 patients across English and Welsh ambulance services. Adults who have sustained an out-of-hospital cardiac arrest are individually randomised to an intraosseous access first strategy or intravenous access first strategy in a 1:1 ratio through an opaque, sealed envelope system. The randomised allocation determines the route used for the first two attempts at vascular access. Participants are initially enrolled under a deferred consent model.The primary clinical-effectiveness outcome is survival at 30-days. Secondary outcomes include return of spontaneous circulation, neurological functional outcome, and health-related quality of life. Participants are followed-up to six-months following cardiac arrest. The primary health economic outcome is incremental cost per quality-adjusted life year gained. Conclusion: The PARAMEDIC-3 trial will provide key information on the clinical and cost-effectiveness of drug route in out-of-hospital cardiac arrest.Trial registration: ISRCTN14223494, registered 16/08/2021, prospectively registered.

BackgroundThe clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. ObjectiveWe aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. MethodsThe pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. ResultsThe pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. ConclusionsThe components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence–driven CDS. Trial RegistrationClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480

Background: Current measures of health-related quality of life are neither sufficiently sensitive or specific to capture the complex and heterogenous nature of the recovery and survivorship associated with cardiac arrest. To address this critical practice gap, we plan a mixed-methods study to co-produce and evaluate a new cardiac arrest-specific patient/survivor-reported outcome measure (PROM). Methods: International guidelines have informed a two-stage, iterative, and interactive process.Stage one will establish what is important to measure following cardiac arrest. A meta-ethnography of published qualitative research and a qualitative exploration of the experiences of survivors and their key supporters will inform the development of a measurement framework. This will be supplemented by existing, extensive reviews describing concepts that have previously been measured in this population. Focus groups with survivors, key supporters, and healthcare professionals, followed by further interviews with survivors and key supporters, will inform the iterative refinement of the framework, candidate items, and PROM structure.Stage two will involve a psychometric evaluation following completion by a large cohort of survivors. Measurement theory will inform: the identification of items that best measure important outcomes; item reduction; and provide robust evidence of measurement and practical properties. Discussion: An international, collaborative approach to PROM development will engage survivors, key supporters, researchers, and health professionals from study commencement. Successful co-production of the cardiac arrest survivorship and health-related quality of life (CASHQoL) measure will provide a robust, relevant, and internationally applicable measure, suitable for completion by adult survivors, and integration into research, registries, and routine care settings.Ethical approval: University of Warwick Biomedical & Scientific Research Ethics Committee (BSREC 22/20-21 granted 10/11/20).

Background The megalichthyids are one of several clades of extinct tetrapodomorph fish that lived throughout the Devonian–Permian periods. They are advanced “osteolepidid-grade” fishes that lived in freshwater swamp and lake environments, with some taxa growing to very large sizes. They bear cosmine-covered bones and a large premaxillary tusk that lies lingually to a row of small teeth. Diagnosis of the family remains controversial with various authors revising it several times in recent works. There are fewer than 10 genera known globally, and only one member definitively identified from Gondwana. Cladarosymblema narrienense Fox et al. 1995 was described from the Lower Carboniferous Raymond Formation in Queensland, Australia, on the basis of several well-preserved specimens. Despite this detailed work, several aspects of its anatomy remain undescribed. Methods Two especially well-preserved 3D fossils of Cladarosymblema narrienense, including the holotype specimen, are scanned using synchrotron or micro-computed tomography (µCT), and 3D modelled using specialist segmentation and visualisation software. New anatomical detail, in particular internal anatomy, is revealed for the first time in this taxon. A novel phylogenetic matrix, adapted from other recent work on tetrapodomorphs, is used to clarify the interrelationships of the megalichthyids and confirm the phylogenetic position of C. narrienense. Results Never before seen morphological details of the palate, hyoid arch, basibranchial skeleton, pectoral girdle and axial skeleton are revealed and described. Several additional features are confirmed or updated from the original description. Moreover, the first full, virtual cranial endocast of any tetrapodomorph fish is presented and described, giving insight into the early neural adaptations in this group. Phylogenetic analysis confirms the monophyly of the Megalichthyidae with seven genera included (Askerichthys, Cladarosymblema, Ectosteorhachis, Mahalalepis, Megalichthys, Palatinichthys, and Sengoerichthys). The position of the megalichthyids as sister group to canowindrids, crownward of “osteolepidids” (e.g.,Osteolepis and Gogonasus), but below “tristichopterids” such as Eusthenopteron is confirmed, but our findings suggest further work is required to resolve megalichthyid interrelationships.

Abstract Background The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. Results Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l 2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. Conclusions Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.

Background: Adrenaline has been used as a treatment for cardiac arrest for many years, despite uncertainty about its effects on long-term outcomes and concerns that it may cause worse neurological outcomes. Objectives: The objectives were to evaluate the effects of adrenaline on survival and neurological outcomes, and to assess the cost-effectiveness of adrenaline use. Design: This was a pragmatic, randomised, allocation-concealed, placebo-controlled, parallel-group superiority trial and economic evaluation. Costs are expressed in Great British pounds and reported in 2016/17 prices. Setting: This trial was set in five NHS ambulance services in England and Wales. Participants: Adults treated for an out-of-hospital cardiac arrest were included. Patients were ineligible if they were pregnant, if they were aged

Background: Each year, approximately 30,000 people have an out-of-hospital cardiac arrest (OHCA) that is treated by UK ambulance services. Across all cases of OHCA, survival to hospital discharge is less than 10%. Acute coronary syndrome (ACS) is a common cause of OHCA. Objectives: To explore factors that influence survival in patients who initially survive an OHCA attributable to ACS. Data source: Data collected by the Myocardial Ischaemia National Audit Project (MINAP) between 2003 and 2015. Participants: Adult patients who had a first OHCA attributable to ACS and who were successfully resuscitated and admitted to hospital. Main outcome measures: Hospital mortality, neurological outcome at hospital discharge, and time to all-cause mortality. Methods: We undertook a cohort study using data from the MINAP registry. MINAP is a national audit that collects data on patients admitted to English, Welsh and Northern Irish hospitals with myocardial ischaemia. From the data set, we identified patients who had an OHCA. We used imputation to address data missingness across the data set. We analysed data using multilevel logistic regression to identify modifiable and non-modifiable factors that affect outcome. Results: Between 2003 and 2015, 1,127,140 patient cases were included in the MINAP data set. Of these, 17,604 OHCA cases met the study inclusion criteria. Overall hospital survival was 71.3%. Across hospitals with at least 60 cases, hospital survival ranged from 34% to 89% (median 71.4%, interquartile range 60.7–76.9%). Modelling, which adjusted for patient and treatment characteristics, could account for only 36.1% of this variability. For the primary outcome, the key modifiable factors associated with reduced mortality were reperfusion treatment [primary percutaneous coronary intervention (pPCI) or thrombolysis] and admission under a cardiologist. Admission to a high-volume cardiac arrest hospital did not influence survival. Sensitivity analyses showed that reperfusion was associated with reduced mortality among patients with a ST elevation myocardial infarction (STEMI), but there was no evidence of a reduction in mortality in patients who did not present with a STEMI. Limitations: This was an observational study, such that unmeasured confounders may have influenced study findings. Differences in case identification processes at hospitals may contribute to an ascertainment bias. Conclusions: In OHCA patients who have had a cardiac arrest attributable to ACS, there is evidence of variability in survival between hospitals, which cannot be fully explained by variables captured in the MINAP data set. Our findings provide some support for the current practice of transferring resuscitated patients with a STEMI to a hospital that can deliver pPCI. In contrast, it may be reasonable to transfer patients without a STEMI to the nearest appropriate hospital. Future work: There is a need for clinical trials to examine the clinical effectiveness and cost-effectiveness of invasive reperfusion strategies in resuscitated OHCA patients of cardiac cause who have not had a STEMI. Funding: The National Institute for Health Research Health Services and Delivery Research programme.

Toxin-antitoxin (TA) systems are ubiquitous bacterial systems that may function in genome maintenance and metabolic stress management, but are also thought to play a role in virulence by helping pathogens survive stress. We previously demonstrated that the Xanthomonas oryzae pv. oryzicola protein AvrRxo1 is a type III-secreted virulence factor that has structural similarities to the zeta family of TA toxins, and is toxic to plants and bacteria in the absence of its predicted chaperone Arc1. In this work, we confirm that AvrRxo1 and its binding partner Arc1 function as a TA system when expressed in Escherichia coli. Sequences of avrRxo1 homologs were culled from published and newly generated phytopathogen genomes, revealing that avrRxo1:arc1 modules are rare or frequently inactivated in some species and highly conserved in others. Cloning and functional analysis of avrRxo1 from Acidovorax avenae, A. citrulli, Burkholderia andropogonis, Xanthomonas translucens, and Xanthomonas euvesicatoria showed that some AvrRxo1 homologs share the bacteriostatic and Rxo1-mediated cell death triggering activities of AvrRxo1 from X. oryzae. Additional distant putative homologs of avrRxo1 and arc1 were identified in genomic or metagenomic sequence of environmental bacteria with no known pathogenic role. One of these distant homologs was cloned from the filamentous soil bacterium Cystobacter fuscus. avrRxo1 from C. fuscus caused watersoaking and triggered Rxo1-dependent cell collapse in Nicotiana benthamiana, but no growth suppression in E. coli was observed. This work confirms that a type III effector can function as a TA system toxin, and illustrates the potential of microbiome data to reveal new environmental origins or reservoirs of pathogen virulence factors.

Despite the acknowledged merits of precision oncology (PO) and its increasing global implementation, its full potential for advancing care and prevention remains unrealized. The benefits are currently accessible to only limited patient segments because of multifaceted barriers. Successful implementation hinges on various factors—scientific complexities not limited to technical, clinical, regulatory, economic, administrative, and health care policy–related challenges. From building infrastructure to the associated costs, including research and development, testing, processing, and trained personnel, a lack of alignment persists. Administrative alignment with regulatory and payor acceptance is crucial. Health care policy must adapt to the ongoing shift from a one-size-fits-all treatment to a personalized approach. Without official endorsement of long-term gains over short-term costs and the health establishment's readiness for innovation, PO prospects, even in prosperous economies, may stagnate. Lower-income countries face exacerbated challenges, intensifying barriers to adoption. Nevertheless, growing awareness and utilization, driven by recognized potential for patients and public health, along with successful examples and advocacy, are progressively influencing policy for a more inclusive and beneficial approach to PO adoption.