Assessment of a Crisis Standards of Care Scoring System for Resource Prioritization and Estimated Excess Mortality by Race, Ethnicity, and Socially Vulnerable Area During a Regional Surge in COVID-19.

Importance: Crisis standards of care (CSOC) scores designed to allocate scarce resources during the COVID-19 pandemic could exacerbate racial disparities in health care. Objective: To analyze the association of a CSOC scoring system with resource prioritization and estimated excess mortality by race, ethnicity, and residence in a socially vulnerable area. Design, Setting, and Participants: This retrospective cohort analysis included adult patients in the intensive care unit during a regional COVID-19 surge from April 13 to May 22, 2020, at 6 hospitals in a health care network in greater Boston, Massachusetts. Participants were scored by acute severity of illness using the Sequential Organ Failure Assessment score and chronic severity of illness using comorbidity and life expectancy scores, and only participants with complete scores were included. The score was ordinal, with cutoff points suggested by the Massachusetts guidelines. Exposures: Race, ethnicity, Social Vulnerability Index. Main Outcomes and Measures: The primary outcome was proportion of patients in the lowest priority score category stratified by self-reported race. Secondary outcomes were discrimination and calibration of the score overall and by race, ethnicity, and neighborhood Social Vulnerability Index. Projected excess deaths were modeled by race, using the priority scoring system and a random lottery. Results: Of 608 patients in the intensive care unit during the study period, 498 had complete data and were included in the analysis; this population had a median (IQR) age of 67 (56-75) years, 191 (38.4%) female participants, 79 (15.9%) Black participants, and 225 patients (45.7%) with COVID-19. The area under the receiver operating characteristic curve for the priority score was 0.79 and was similar across racial groups. Black patients were more likely than others to be in the lowest priority group (12 [15.2%] vs 34 [8.1%]; P = .046). In an exploratory simulation model using the score for ventilator allocation, with only those in the highest priority group receiving ventilators, there were 43.9% excess deaths among Black patients (18 of 41 patients) and 28.6% (58 of 203 patients among all others (P = .05); when the highest and intermediate priority groups received ventilators, there were 4.9% (2 of 41 patients) excess deaths among Black patients and 3.0% (6 of 203) among all others (P = .53). A random lottery resulted in more excess deaths than the score. Conclusions and Relevance: In this study, a CSOC priority score resulted in lower prioritization of Black patients to receive scarce resources. A model using a random lottery resulted in more estimated excess deaths overall without improving equity by race. CSOC policies must be evaluated for their potential association with racial disparities in health care.

Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media.

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research.

Veno-venous extracorporeal membrane oxygenation allocation in the COVID-19 pandemic.

Rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resultant clinical illness, coronavirus disease 2019 (COVID-19), drove the World Health Organization to declare COVID-19 a pandemic. Veno-venous Extra-Corporeal Membrane Oxygenation (VV-ECMO) is an established therapy for management of patients demonstrating the most severe forms of hypoxemic respiratory failure from COVID-19. However, features of COVID-19 pathophysiology and necessary length of treatment present distinct challenges for utilization of VV-ECMO within the current healthcare emergency. In addition, growing allocation concerns due to capacity and cost present significant challenges. Ethical and legal aspects pertinent to triage of this resource-intensive, but potentially life-saving, therapy in the setting of the COVID-19 pandemic are reviewed here. Given considerations relevant to VV-ECMO use, additional emphasis has been placed on emerging hospital resource scarcity and disproportionate representation of healthcare workers among the ill. Considerations are also discussed surrounding withdrawal of VV-ECMO and the role for early communication as well as consultation from palliative care teams and local ethics committees. In discussing how to best manage these issues in the COVID-19 pandemic at present, we identify gaps in the literature and policy important to clinicians as this crisis continues.