Activation of TLR by ssRNA after FcγR-mediated phagocytosis of immune complexes (IC) may be relevant in autoimmune-associated congenital heart block (CHB) where the obligate factor is a maternal anti-SSA/Ro Ab and the fetal factors, protein/RNA on an apoptotic cardiocyte and infiltrating macrophages. This study addressed the hypothesis that Ro60-associated ssRNAs link macrophage activation to fibrosis via TLR engagement. Both macrophage transfection with noncoding ssRNA that bind Ro60 and an IC generated by incubation of Ro60-ssRNA with an IgG fraction from a CHB mother or affinity purified anti-Ro60 significantly increased TNF-α secretion, an effect not observed using control RNAs or normal IgG. Dependence on TLR was supported by the significant inhibition of TNF-α release by IRS661 and chloroquine. The requirement for FcγRIIIa-mediated delivery was provided by inhibition with an anti-CD16a Ab. Fibrosis markers were noticeably increased in fetal cardiac fibroblasts after incubation with supernatants generated from macrophages transfected with ssRNA or incubated with the IC. Supernatants generated from macrophages with ssRNA in the presence of IRS661 or chloroquine did not cause fibrosis. In a CHB heart, but not a healthy heart, TLR7 immunostaining was localized to a region near the atrioventricular groove at a site enriched in mononuclear cells and fibrosis. These data support a novel injury model in CHB, whereby endogenous ligand, Ro60-associated ssRNA, forges a nexus between TLR ligation and fibrosis instigated by binding of anti-Ro Abs to the target protein likely accessible via apoptosis.

Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages "instance-level explanations", measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved.

IntroductionEmergency departments (ED) care for society’s most vulnerable older adults who present with exacerbations of chronic disease at the end of life, yet the clinical paradigm focuses on treatment of acute pathologies. Palliative care interventions in the ED capture high-risk patients at a time of crisis and can dramatically improve patient-centred outcomes. This study aims to implement and evaluate Primary Palliative Care for Emergency Medicine (PRIM-ER) on ED disposition, healthcare utilisation and survival in older adults with serious illness.Methods and analysisThis is the protocol for a pragmatic, cluster-randomised stepped wedge trial to test the effectiveness of PRIM-ER in 35 EDs across the USA. The intervention includes four core components: (1) evidence-based, multidisciplinary primary palliative care education; (2) simulation-based workshops; (3) clinical decision support; and (4) audit and feedback. The study is divided into two phases: a pilot phase, to ensure feasibility in two sites, and an implementation and evaluation phase, where we implement the intervention and test the effectiveness in 33 EDs over 2 years. Using Centers for Medicare and Medicaid Services (CMS) data, we will assess the primary outcomes in approximately 300 000 patients: ED disposition to an acute care setting, healthcare utilisation in the 6 months following the ED visit and survival following the index ED visit. Analysis will also determine the site, provider and patient-level characteristics that are associated with variation in impact of PRIM-ER.Ethics and disseminationInstitutional Review Board approval was obtained at New York University School of Medicine to evaluate the CMS data. Oversight will also be provided by the National Institutes of Health, an Independent Monitoring Committee and a Clinical Informatics Advisory Board. Trial results will be submitted for publication in a peer-reviewed journal.Trial registration numberNCT03424109; Pre-results

Background: The emergency department is a critical juncture in the trajectory of care of patients with serious, lifelimiting illness. Implementation of a clinical decision support (CDS) tool automates identification of older adults who may benefit from palliative care instead of relying upon providers to identify such patients, thus improving quality of care by assisting providers with adhering to guidelines. The Primary Palliative Care for Emergency Medicine (PRIM-ER) study aims to optimize the use of the electronic health record by creating a CDS tool to identify high risk patients most likely to benefit from primary palliative care and provide point-of-care clinical recommendations. Methods: A clinical decision support tool entitled Emergency Department Supportive Care Clinical Decision Support (Support-ED) was developed as part of an institutionally-sponsored value based medicine initiative at the Ronald O. Perelman Department of Emergency Medicine at NYU Langone Health. A multidisciplinary approach was used to develop SupportED including: a scoping review of ED palliative care screening tools; launch of a workgroup to identify patient screening criteria and appropriate referral services; initial design and usability testing via the standard System Usability Scale questionnaire, education of the ED workforce on the SupportED background, purpose and use, and; creation of a dashboard for monitoring and feedback. Results: The scoping review identified the Palliative Care and Rapid Emergency Screening (P-CaRES) survey as a validated instrument in which to adapt and apply for the creation of the CDS tool. The multidisciplinary workshops identified two primary objectives of the CDS: to identify patients with indicators of serious life limiting illness, and to assist with referrals to services such as palliative care or social work. Additionally, the iterative design process yielded three specific patient scenarios that trigger a clinical alert to fire, including: 1) when an advance care planning document was present, 2) when a patient had a previous disposition to hospice, and 3) when historical and/or current clinical data points identify a serious life-limiting illness without an advance care planning document present. Monitoring and feedback indicated a need for several modifications to improve CDS functionality. Conclusions: CDS can be an effective tool in the implementation of primary palliative care quality improvement best practices. Health systems should thoughtfully consider tailoring their CDSs in order to adapt to their unique workflows and environments. The findings of this research can assist health systems in effectively integrating a primary palliative care CDS system seamlessly into their processes of care.