key: cord-0948299-l8zqs655
authors: Zebrowski, A. M.; Doorley, R.; Sims, S.; Rundle, A. G.; Branas, C. C.; Carr, B. G.
title: 2 Rightsizing Response: The Optimization of Critical Care Resources during COVID-19
date: 2020-10-31
journal: Annals of Emergency Medicine
DOI: 10.1016/j.annemergmed.2020.09.012
sha: 2742f1e90858494d80c78dafd75bbee9e1241ef3
doc_id: 948299
cord_uid: l8zqs655

nan

Study Objectives: Podcasts have become increasingly popular platforms for knowledge synthesis and translation. Trainees now report spending more time with podcasts than any other educational resource, including textbooks and journals. Though almost two thirds of residents report podcast listening changes their clinical practice, there is uncertainty over the quality and influence of podcasts. Given the broad use of podcasts among emergency medicine (EM) trainees, there is a need to better understand the processes by which they sort, interpret, and judge information as they learn. What is not known is how EM residents make credibility judgements about podcast content, how their judgements compare to the judgements of attending physicians, and how those credibility decisions relate to other learning modalities.

The objective was to explore the processes by which podcasts are weighed, valued, and judged relative to one another, and relative to other learning modalities.

Methods: We performed a multi-center qualitative thematic analysis based on a constructivist grounded theory approach by conducting 11 semi-structured interviews with resident and attending physicians from three North American teaching institutions from January 2020 to June 2020. Narrative transcripts were coded line-byline using constant comparative analysis to organize transcripts into focused codes, key conceptual categories, and then major themes. Three authors met regularly during the analysis to develop the coding schema, resolve discrepancies, and discuss themes.

Results: We identified four broad themes related to credibility judgements and educational podcasts: trust in source, congruence of content, triangulation of references, and application context. Participants had a baseline level of trust in a podcast resource based on popularity, recommendations from colleagues, format, Web site, and speaker credentials. When listening to podcast content, participants' levels of scrutiny varied based on the type of material (core content vs. cutting-edge) and level of agreement of the content with their existing knowledge. When considering incongruent or cutting-edge information, participants triangulated the podcast content with their experience, understanding of physiology, content of other podcasts and online resources, reading the primary literature, and conversations with attending physicians. When applying information gleaned from podcasts, participants yielded to local practice contexts and, for residents, their attendings' judgments.

Conclusion: When listening to educational podcasts, resident and attending physicians made a series of complex credibility judgements that weighed trust in the source, congruence of the content, triangulation of references, and the context of application. Study Objectives: As the number of COVID-19 patients increased across the US, health care systems required a variety of approaches to meet the demand for critical care resources. We sought to determine the ability of the existing health care system to meet these demands and explored the intersection of critical care bed (CCB) capacity and staffing availability in U.S. counties using two-week-ahead projections for April 13th, 2020.

Methods: A linear optimization model was developed and solved using the revised simplex method. The model aimed to minimize unmet demand for COVID-19 critical care through an optimal combination of (i) redistribution of nurses and physicians within each state (within 250 miles) and (ii) provision of additional CCB capacity and staff. Staffing ratios of 2 CCBs/nurse and 10 CCBs/physician were applied. Advanced practice practitioners (APPs) were used to "extend" physician coverage with each APP equal to 0.5 physicians. Staffing counts were estimated using American Hospital Association and Health Resources and Services Administration Data. To account for critical care training, 15% of RNs, 12% of NPs, 1.4% of PAs, and 50% of CRNAs were considered as available critical care trained staff. Intensivists (100%) and Medical and Surgical specialists (30%) were included with 45% of these available for hospital staffing. Case count projections were taken from the Columbia University models (Shaman, 2020) and 70% of CCBs in each county were assumed to be occupied by non-COVID-19 patients. For each county, three potential constraints on increasing capacity were estimated: the number of nurses, the number of physicians (including APPs), and the number of CCBs. One or more constraints could be active at any time.

Results: Prior to optimization, 91% of counties were able to meet the demand for projected case counts. In contrast, 8.4% were limited by nursing resources, 0.09% by physicians, and 0.8% by the number of CCBs. After optimization, 16.9% of counties sent nurses to a different county(s) (median 6 nurses sent, IQR 13.75) compared with 5.5% counties receiving them (median 23, IQR 43.5). Fewer physicians were relocated (0.09% sent, median 1, IQR 1; 0.06% received, median 2.5, IQR 1.5) (Figure) . Using baseline staffing ratios and availability, these redistributions led to a reduction in total unmet demand from 24,155 to 19,976. In order to fully meet demand across the US under these conditions, an additional 1,225 physicians, 41,939 nurses and 13,905 CCBs would have been needed.

Conclusion: This work shows that with the redeployment of resources even within state boundaries may provide relief to areas of need without causing strain in other locations. While validation with actual redeployment during the pandemic can improve estimates, these models can provide decision support to stakeholders by suggesting optimal reallocation or the ability of existing resources to support additional capacity. Study Objectives: Although some ED risk stratification tools (eg, HEART score) consider non-specific electrocardiogram (ECG) findings as an aid in disposition decisions, their clinical value in patients with an initially normal high-sensitivity troponin I (hsTnI) is unclear.

Our purpose was to determine if non-specific ECG changes are associated with 30day major adverse cardiac events (MACE) in ED patients presenting with suspected acute coronary syndrome and who have a low initial hsTnI.

Methods: Using the prospective Siemens Atellica hsTnI FDA submission observational database, we evaluated the association between non-specific ECG changes (defined as left bundle branch block (LBBB), ST depression or T wave inversions) and 30-day MACE (death, myocardial infarction, heart failure, or percutaneous coronary intervention). Eligible patients presented to one of 28 US EDs with suspected acute coronary syndromes from April 2015 to April 2016, and had hsTnI obtained at 1, 3, and 6 hours after ED presentation. After excluding STelevation myocardial infarction and unstable ECG changes (VT, VF, tachyarrhythmias, or AV blocks), the association between non-specific changes on the initial ECG and the initial hsTnI (Siemen's Atellica, Siemens Healthineers, Inc, Malvern, PA) with 30 day MACE was determined by chi-square testing.

Results: Of 2667 enrolled, 1037 patients met the inclusion criteria and were included in the analysis. Mean age was 61 years (SD AE12), 55% were male, with 55% white and 40% African American. Median (IQR) time from symptom onset to presentation, and presentation to specimen collection was 87 (0, 216) and 147 (117, 178) minutes, respectively. The most common presenting symptom were chest pain (83%) and dyspnea (10%). ECG findings were T wave inversion or non-specific T changes, ST depression or non-specific ST changes, RBBB or early repolarization, or LBBB in 40, 15, 10, and 2%, respectively. MACE occurred in 118 (11.4%) patients, with ACS without MI (69 patients, 6.65%) and heart failure (24 patients, 2.3%) being most frequent. In patients with hs-cTnI <400 ng/L, there was no association between non-specific ECG changes and 30-day MACE (p¼0.71). If the hs-cTnI was !400 ng/L there was an association with increased rates of 30-day MACE and nonspecific ECG findings (p¼0.026).

Conclusion: In ED suspected ACS patients without STEMI or unstable ECG changes, and a hsTnI <400 ng/L, non-specific ECG findings have no association with 30-day adverse cardiac events. The use of non-specific ECG findings to affect disposition decisions should be reconsidered.

4 Impact of the SARS-CoV-2 Pandemic on Emergency

(n¼18,646) decreased 49% and 53.2% respectively. The total numbers of patients diagnosed with myocardial infarctions (STEMI and Non-STEMI), stroke, appendicitis and cholecystitis all decreased by a similar percentage. While there were fewer visits for mental health (n¼1104 in preceding weeks, n¼1032 for year-prior, n¼752 during pandemic), they made up a larger proportion of ED visits -2.9% for both baselines and 4% during period of interest

Out-of-hospital mortality for natural (non-COVID-related) and non-natural deaths increased from 73 pre-COVID to 128 during the COVID period (p<.001). The significant increase in outof-hospital mortality drives the overall mortality increase. There was an increase in deaths, driven by out-of-hospital mortality. Conclusion: Fewer patients presenting with acute and time-sensitive diagnoses suggests that patients are deferring care, this may be further supported by an increase in out-of-hospital mortality as well as a lower number of patients presenting with complaints and diagnoses that would be