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Introduction: The United States lacks a national interfacility patient transfer coordination system. During the coronavirus 2019 (COVID-19) pandemic, many hospitals were overwhelmed and faced difficulties transferring sick patients, leading some states and cities to form transfer centers intended to assist sending facilities. In this study we aimed to explore clinician experiences with newly implemented transfer coordination centers. Methods: This mixed-methods study used a brief national survey along with in-depth interviews. The American College of Emergency Physicians Emergency Medicine Practice Research Network (EMPRN) administered the national survey in March 2021. From September–December 2021, semi-structured qualitative interviews were conducted with administrators and rural emergency clinicians in Arizona and New Mexico, two states that started transfer centers during COVID-19. Results: Among 141 respondents (of 765, 18.4% response rate) to the national EMPRN survey, only 30% reported implementation or expansion of a transfer coordination center during COVID-19. Those with new transfer centers reported no change in difficulty of patient transfers during COVID-19 while those without had increased difficulty. The 17 qualitative interviews expanded upon this, revealing four major themes: 1) limited resources for facilitating transfers even before COVID-19; 2) increased number of and distance to transfer partners during the COVID-19 pandemic; 3) generally positive impacts of transfer centers on workflow, and 4) the potential for continued use of centers to facilitate transfers. Conclusion: Transfer centers may have offset pandemic-related transfer challenges brought on by the COVID-19 pandemic. Clinicians who frequently need to transfer patients may particularly benefit from ongoing access to such transfer coordination services.

Introduction Acute kidney injury requiring renal replacement therapy (AKI-RRT) is common in the intensive care unit (ICU) and is associated with significant morbidity and mortality. Continuous RRT (CRRT) non-selectively removes large amounts of amino acids from plasma, lowering serum amino acid concentrations and potentially depleting total-body amino acid stores. Therefore, the morbidity and mortality associated with AKI-RRT may be partly mediated through accelerated skeletal muscle atrophy and resulting muscle weakness. However, the impact of AKI-RRT on skeletal muscle mass and function during and following critical illness remains unknown. We hypothesise that patients with AKI-RRT have higher degrees of acute muscle loss than patients without AKI-RRT and that AKI-RRT survivors are less likely to recover muscle mass and function when compared with other ICU survivors.Methods and analysis This protocol describes a prospective, multicentre, observational trial assessing skeletal muscle size, quality and function in ICU patients with AKI-RRT. We will perform musculoskeletal ultrasound to longitudinally evaluate rectus femoris size and quality at baseline (within 48 hours of CRRT initiation), day 3, day 7 or at ICU discharge, at hospital discharge, and 1–3 months postdischarge. Additional skeletal muscle and physical function tests will be performed at hospital discharge and postdischarge follow-up. We will analyse the effect of AKI-RRT by comparing the findings in enrolled subjects to historical controls of critically ill patients without AKI-RRT using multivariable modelling.Ethics and dissemination We anticipate our study will reveal that AKI-RRT is associated with greater degrees of muscle loss and dysfunction along with impaired postdischarge recovery of physical function. These findings could impact the in-hospital and postdischarge treatment plan for these patients to include focused attention on muscle strength and function. We intend to disseminate findings to participants, healthcare professionals, the public and other relevant groups via conference presentation and publication without any publication restrictions.Trial registration number NCT05287204.

Abstract Palliative care is an interdisciplinary care to optimize physical, psychosocial, and spiritual symptoms of patients and their families whose quality of life is impaired by serious, life-limiting illness. In 2021, the importance of providing palliative care in the intensive care unit (ICU) is well recognized by various studies to alleviate physical symptoms due to invasive treatments, to set patient-centered goals of care, and to provide end-of-life care. This paper summarizes the evidence known to date on primary palliative care delivered in the ICU settings. We will then discuss the potential benefits and harms of primary palliative care so that critical care clinicians are better equipped to decide what services might best improve the palliative care needs in their ICUs.

This White Paper has been formally accepted for support by the International Federation for Emergency Medicine (IFEM) and by the World Federation of Intensive and Critical Care (WFICC), put forth by a multi-specialty group of intensivists and emergency medicine providers from low- and low-middle-income countries (LMICs) and high-income countries (HiCs) with the aim of 1) defining the current state of caring for the critically ill in low-resource settings (LRS) within LMICs and 2) highlighting policy options and recommendations for improving the system-level delivery of early critical care services in LRS. LMICs have a high burden of critical illness and worse patient outcomes than HICs, hence, the focus of this White Paper is on the care of critically ill patients in the early stages of presentation in LMIC settings. In such settings, the provision of early critical care is challenged by a fragmented health system, costs, a health care workforce with limited training, and competing healthcare priorities. Early critical care services are defined as the early interventions that support vital organ function during the initial care provided to the critically ill patient—these interventions can be performed at any point of patient contact and can be delivered across diverse settings in the healthcare system and do not necessitate specialty personnel. Currently, a single “best” care delivery model likely does not exist in LMICs given the heterogeneity in local context; therefore, objective comparisons of quality, efficiency, and cost-effectiveness between varying models are difficult to establish. While limited, there is data to suggest that caring for the critically ill may be cost effective in LMICs, contrary to a widely held belief. Drawing from locally available resources and context, strengthening early critical care services in LRS will require a multi-faceted approach, including three core pillars: education, research, and policy. Education initiatives for physicians, nurses, and allied health staff that focus on protocolized emergency response training can bridge the workforce gap in the short-term; however, each country’s current human resources must be evaluated to decide on the duration of training, who should be trained, and using what curriculum. Understanding the burden of critical Illness, best practices for resuscitation, and appropriate quality metrics for different early critical care services implementation models in LMICs are reliant upon strengthening the regional research capacity, therefore, standard documentation systems should be implemented to allow for registry use and quality improvement. Policy efforts at a local, national and international level to strengthen early critical care services should focus on funding the building blocks of early critical care services systems and promoting the right to access early critical care regardless of the patient’s geographic or financial barriers. Additionally, national and local policies describing ethical dilemmas involving the withdrawal of life-sustaining care should be developed with broad stakeholder representation based on local cultural beliefs as well as the optimization of limited resources.

Latent knowledge can be extracted from the electronic notes that are recorded during patient encounters with the health system. Using these clinical notes to decipher a patient's underlying comorbidites, symptom burdens, and treatment courses is an ongoing challenge. Latent topic model as an efficient Bayesian method can be used to model each patient's clinical notes as "documents" and the words in the notes as "tokens". However, standard latent topic models assume that all of the notes follow the same topic distribution, regardless of the type of note or the domain expertise of the author (such as doctors or nurses). We propose a novel application of latent topic modeling, using multi-note topic model (MNTM) to jointly infer distinct topic distributions of notes of different types. We applied our model to clinical notes from the MIMIC-III dataset to infer distinct topic distributions over the physician and nursing note types. Based on manual assessments made by clinicians, we observed a significant improvement in topic interpretability using MNTM modeling over the baseline single-note topic models that ignore the note types. Moreover, our MNTM model led to a significantly higher prediction accuracy for prolonged mechanical ventilation and mortality using only the first 48 hours of patient data. By correlating the patients' topic mixture with hospital mortality and prolonged mechanical ventilation, we identified several diagnostic topics that are associated with poor outcomes. Because of its elegant and intuitive formation, we envision a broad application of our approach in mining multi-modality text-based healthcare information that goes beyond clinical notes. Code available at https://github.com/li-lab-mcgill/heterogeneous_ehr.

BackgroundAmong patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.MethodsRetrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.ResultsThere were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.DiscussionWe developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Introduction: Injuries account for 10% of the global burden of disease, resulting in approximately 5.8 million deaths annually. Trauma registries are an important tool in the development of a trauma system; however, limited resources in low- and middle-income countries (LMIC) make the development of high-quality trauma registries challenging. We describe the development of a LMIC trauma registry based on a robust retrospective chart review, which included data derived from prehospital, emergency centre and inpatient records. Methods: This paper outlines our methods for identifying and locating patients and their medical records using pragmatic and locally appropriate record linkage techniques. A prehospital database was queried to identify patients transported to University Teaching Hospital – Kigali, Rwanda from December 2012 through February 2015. Demographic information was recorded and used to create a five-factor identification index, which was then used to search OpenClinic GA, an online open source hospital information system. The medical record number and archive number obtained from OpenClinic GA were then used to locate the physical medical record for data extraction. Results: A total of 1668 trauma patients were transported during the study period. 66.7% were successfully linked to their medical record numbers and archive codes. 94% of these patients were successfully linked to their medical record numbers and archive codes were linked by four or five of the five pre-set identifiers. 945 charts were successfully located and extracted for inclusion in the trauma registry. Record linkage and chart extraction took approximately 1256 h. Conclusion: The process of record linkage and chart extraction was a resource-intensive process; however, our unique methodology resulted in a high linkage rate. This study suggests that it is feasible to create a retrospective trauma registry in LMICs using pragmatic and locally appropriate record linkage techniques.

Introduction: Injury accounts for 9.6% of the global mortality burden, disproportionately affecting those living in low- and middle-income countries. In an effort to improve trauma care in Rwanda, the Ministry of Health developed a prehospital service, Service d’Aide Médicale Urgente (SAMU), and established an emergency medicine training program. However, little is known about patients receiving prehospital and emergency trauma care or their outcomes. The objective was to develop a linked prehospital–hospital database to evaluate patient characteristics, mechanisms of injury, prehospital and hospital resource use, and outcomes among injured patients receiving acute care in Kigali, Rwanda. Methods: A retrospective cohort study was conducted at University Teaching Hospital – Kigali, the primary trauma centre in Rwanda. Data was included on all injured patients transported by SAMU from December 2012 to February 2015. SAMU’s prehospital database was linked to hospital records and data were collected using standardised protocols by trained abstractors. Demographic information, injury characteristics, acute care, hospital course and outcomes were included. Results: 1668 patients were transported for traumatic injury during the study period. The majority (77.7%) of patients were male. The median age was 30 years. Motor vehicle collisions accounted for 75.0% of encounters of which 61.4% involved motorcycles. 48.8% of patients sustained injuries in two or more anatomical regions. 40.1% of patients were admitted to the hospital and 78.1% required surgery. The overall mortality rate was 5.5% with nearly half of hospital deaths occurring in the emergency centre. Conclusion: A linked prehospital and hospital database provided critical epidemiological information describing trauma patients in a low-resource setting. Blunt trauma from motor vehicle collisions involving young males constituted the majority of traumatic injury. Among this cohort, hospital resource utilisation was high as was mortality. This data can help guide the implementation of interventions to improve trauma care in the Rwandan setting.

Rwanda, known as the “Land of a Thousand Hills,” is a small, East African country that was the site of the devastating 1994 genocide. In the past 18 years, this post-conflict country has made tremendous progress in rebuilding itself and its health infrastructure. The country has recovered or surpassed many of its pre-1994 health levels, including reduction in HIV/AIDS prevalence, under-five mortality and road traffic accidents. Nevertheless, Rwanda continues to face a high burden of disease. The leading causes of mortality in Rwanda include complications of HIV/AIDS and related opportunistic infections, severe malaria, pulmonary infections, and trauma, and are best managed with emergency and acute care services. However, health care personal resources remain significantly lacking, and there is currently no emergency medicine-trained workforce. The Rwandan government, partnering with international organizations, has launched a campaign to improve human resources for health, and as a part of that effort the creation of training programs in emergency medicine is now underway. The Rwandan Human Resources for Health program can serve as a guide to the development of similar programs within other African countries. The emergency medicine component of this program includes two tracks: a 2-year postgraduate diploma course, followed by a 3-year Masters of Medicine in Emergency Medicine. The program is slated to graduate its first cohort of trained Emergency Physicians in 2017.