Patient Information Items Needed to Guide the Allocation of Scarce Life-Sustaining Resources: A Delphi Study of Multidisciplinary Experts.

OBJECTIVE: Plans for allocation of scarce life-sustaining resources during the coronavirus disease 2019 (COVID-19) pandemic often include triage teams, but operational details are lacking, including what patient information is needed to make triage decisions. METHODS: A Delphi study among Washington state disaster preparedness experts was performed to develop a list of patient information items needed for triage team decision-making during the COVID-19 pandemic. Experts proposed and rated their agreement with candidate information items during asynchronous Delphi rounds. Consensus was defined as ≥80% agreement. Qualitative analysis was used to describe considerations arising in this deliberation. A timed simulation was performed to evaluate feasibility of data collection from the electronic health record. RESULTS: Over 3 asynchronous Delphi rounds, 50 experts reached consensus on 24 patient information items, including patients' age, severe or end-stage comorbidities, the reason for and timing of admission, measures of acute respiratory failure, and clinical trajectory. Experts weighed complex considerations around how information items could support effective prognostication, consistency, accuracy, minimizing bias, and operationalizability of the triage process. Data collection took a median of 227 seconds (interquartile range = 205, 298) per patient. CONCLUSIONS: Experts achieved consensus on patient information items that were necessary and appropriate for informing triage teams during the COVID-19 pandemic.

Predicting Health Disparities in Regions at Risk of Severe Illness to Inform Health Care Resource Allocation During Pandemics: Observational Study.

BACKGROUND: Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations. OBJECTIVE: Our objective was to create a repeatable modeling process to identify regional population centers with pandemic vulnerability. METHODS: Using readily available COVID-19 and socioeconomic variable data sets, we used stepwise linear regression techniques to build predictive models during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in high population density states. The mean sample size was 43 and ranged from 8 (Connecticut) to 82 (Michigan). RESULTS: The New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan, and Pennsylvania models provided the strongest predictions of top counties in densely populated states with a high likelihood of disproportionate COVID-19 mortality rates. For all of these models, P values were less than .05. CONCLUSIONS: The models have been shared with the Department of Health Commissioners of each of these states with strong model predictions as input into a much needed "pandemic playbook" for local health care agencies in allocating medical testing and treatment resources. We have also confirmed the utility of our models with pharmaceutical companies for use in decisions pertaining to vaccine trial and distribution locations.