Emergency medical service provider decision-making in out of hospital cardiac arrest: An exploratory study

© 2017 The Author(s). Background: There are approximately 60,000 out-of-hospital cardiac arrests (OHCA) in the United Kingdom (UK) each year. Within the UK there are well-established clinical practice guidelines that define when resuscitation should be commenced in OHCA, and when resuscitation should cease. Background literature indicates that decision-making in the commencement and cessation of resuscitation efforts in OHCA is complex, and not comprehensively understood. No relevant research from the UK has been published to date and this research study seeks to explore the influences on UK Emergency Medical Service (EMS) provider decision-making when commencing and ceasing resuscitation attempts in OHCA. The aim of this research to explore the influences on UK Emergency Medical Services provider decision-making when commencing and ceasing resuscitation attempts in OHCA. Methods: Four focus groups were convened with 16 clinically active EMS providers. Four case vignettes were discussed to explore decision-making within the focus groups. Thematic analysis was used to analyse transcripts. Results: This research found that there are three stages in the decision-making process when EMS providers consider whether to commence or cease resuscitation attempts in OHCA. These stages are: the call; arrival on scene; the protocol. Influential factors present at each of the three stages can lead to different decisions and variability in practice. These influences are: factual information available to the EMS provider; structural factors such as protocol, guidance and research; cultural beliefs and values; interpersonal factors; risk factors; personal values and beliefs. Conclusions: An improved understanding of the circumstantial, individual and interpersonal factors that mediate the decision-making process in clinical practice could inform the development of more effective clinical guidelines, education and clinical decision support in OHCA. These changes have the potential to lead to greater consistency. and EMS provider confidence, with the potential for improved patient outcome from OHCA

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality

Expert consensus statements for the management of COVID-19-related acute respiratory failure using a Delphi method.

Coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented pressure on healthcare system globally. Lack of high-quality evidence on the respiratory management of COVID-19-related acute respiratory failure (C-ARF) has resulted in wide variation in clinical practice. Using a Delphi process, an international panel of 39 experts developed clinical practice statements on the respiratory management of C-ARF in areas where evidence is absent or limited. Agreement was defined as achieved when > 70% experts voted for a given option on the Likert scale statement or > 80% voted for a particular option in multiple-choice questions. Stability was assessed between the two concluding rounds for each statement, using the non-parametric Chi-square (χ <sup>2</sup> ) test (p < 0·05 was considered as unstable). Agreement was achieved for 27 (73%) management strategies which were then used to develop expert clinical practice statements. Experts agreed that COVID-19-related acute respiratory distress syndrome (ARDS) is clinically similar to other forms of ARDS. The Delphi process yielded strong suggestions for use of systemic corticosteroids for critical COVID-19; awake self-proning to improve oxygenation and high flow nasal oxygen to potentially reduce tracheal intubation; non-invasive ventilation for patients with mixed hypoxemic-hypercapnic respiratory failure; tracheal intubation for poor mentation, hemodynamic instability or severe hypoxemia; closed suction systems; lung protective ventilation; prone ventilation (for 16-24 h per day) to improve oxygenation; neuromuscular blocking agents for patient-ventilator dyssynchrony; avoiding delay in extubation for the risk of reintubation; and similar timing of tracheostomy as in non-COVID-19 patients. There was no agreement on positive end expiratory pressure titration or the choice of personal protective equipment. Using a Delphi method, an agreement among experts was reached for 27 statements from which 20 expert clinical practice statements were derived on the respiratory management of C-ARF, addressing important decisions for patient management in areas where evidence is either absent or limited. The study was registered with Clinical trials.gov Identifier: NCT04534569

Real-Time Monitoring of Tumorigenesis, Dissemination, & Drug Response in a Preclinical Model of Lymphangioleiomyomatosis/Tuberous Sclerosis Complex

Background: TSC2-deficient cells can proliferate in the lungs, kidneys, and other organs causing devastating progressive multisystem disorders such as lymphangioleiomyomatosis (LAM) and tuberous sclerosis complex (TSC). Preclinical models utilizing LAM patient-derived cells have been difficult to establish. We developed a novel animal model system to study the molecular mechanisms of TSC/LAM pathogenesis and tumorigenesis and provide a platform for drug testing. Methods and Findings: TSC2-deficient human cells, derived from the angiomyolipoma of a LAM patient, were engineered to co-express both sodium-iodide symporter (NIS) and green fluorescent protein (GFP). Cells were inoculated intraparenchymally, intravenously, or intratracheally into athymic NCr nu/nu mice and cells were tracked and quantified using single photon emission computed tomography (SPECT) and computed tomography (CT). Surprisingly, TSC2-deficient cells administered intratracheally resulted in rapid dissemination to lymph node basins throughout the body, and histopathological changes in the lung consistent with LAM. Estrogen was found to be permissive for tumor growth and dissemination. Rapamycin inhibited tumor growth, but tumors regrew after the drug treatment was withdrawn. Conclusions: We generated homogeneous NIS/GFP co-expressing TSC2-deficient, patient-derived cells that can proliferate and migrate in vivo after intratracheal instillation. Although the animal model we describe has some limitations, we demonstrate that systemic tumors formed from TSC2-deficient cells can be monitored and quantified noninvasively over time using SPECT/CT, thus providing a much needed model system for in vivo drug testing and mechanistic studies of TSC2-deficient cells and their related clinical syndromes

Achieving the "triple aim" for inborn errors of metabolism: a review of challenges to outcomes research and presentation of a new practice-based evidence framework

Across all areas of health care, decision makers are in pursuit of what Berwick and colleagues have called the “triple aim”: improving patient experiences with care, improving health outcomes, and managing health system impacts. This is challenging in a rare disease context, as exemplified by inborn errors of metabolism. There is a need for evaluative outcomes research to support effective and appropriate care for inborn errors of metabolism. We suggest that such research should consider interventions at both the level of the health system (e.g., early detection through newborn screening, programs to provide access to treatments) and the level of individual patient care (e.g., orphan drugs, medical foods). We have developed a practice- based evidence framework to guide outcomes research for inborn errors of metabolism. Focusing on outcomes across the triple aim, this framework integrates three priority themes: tailoring care in the context of clinical heterogeneity; a shift from “urgent care” to “opportunity for improvement”; and the need to evaluate the comparative effectiveness of emerging and established therapies. Guided by the framework, a new Canadian research network has been established to generate knowledge that will inform the design and delivery of health services for patients with inborn errors of metabolism and other rare diseases.This work was supported by a CIHR Emerging Team Grant (“Emerging team in rare diseases: acheiving the ‘triple aim’ for inborn errors of metabolism,” B.K. Potter, P. Chakraborty, and colleagues, 2012– 2017, grant no. TR3–119195). Current investigators and collaborators in the Canadian Inherited Metabolic Diseases Research Network are: B.K. Potter, P. Chakraborty, J. Kronick, D. Coyle, K. Wilson, M. Brownell, R. Casey, A. Chan, S. Dyack, L. Dodds, A. Feigenbaum, D. Fell, M. Geraghty, C. Greenberg, S. Grosse, A. Guttmann, A. Khan, J. Little, B. Maranda, J. MacKenzie, A. Mhanni, F. Miller, G. Mitchell, J. Mitchell, M. Nakhla, M. Potter, C. Prasad, K. Siriwardena, K.N. Speechley, S. Stocker, L. Turner, H. Vallance, and B.J. Wilson. Members of our external advisory board are D. Bidulka, T. Caulfield, J.T.R. Clarke, C. Doiron, K. El Emam, J. Evans, A. Kemper, W. McCormack, and A. Stephenson Julian. J. Little is supported by a Canada Research Chair in Human Genome Epidemiology. K. Wilson is supported by a Canada Research Chair in Public Health Policy