ICU Beds Research Articles

Prehospital COVID19-related Encounters Predict Future Hospital Utilization

Objective: Identify if prehospital patient encounters can predict SARS-CoV-2 (COVID19) related hospital utilization. Methods: EMS data from COVID19-related prehospital encounters was pulled from NEMSIS systems in Minnesota. This data was plotted against hospital general medical-surgical bed and ICU bed usage during the initial COVID19 surge and again during a second surge. A validation dataset from 2019 was also utilized. Results: There was a total of 6,460 influenza-like-illness calls, and 2,161 COVID19-specific calls during the studied timeframe. A total of 24,806 medical-surgical bed-days and 20,208 ICU bed-days were analyzed. During initial COVID surge (April-July 2020), EMS encounters best correlated with medical-surgical bed utilization 10 days in the future (r2 = 0.85, N = 113, p = <0.001), with each encounter correlating with a utilization of 7.1 beds. ICU bed utilization was best predicted 16 days in the future (r2 = 0.86, N = 107, p = <0.001) with each encounter correlating with the use of 4.5 ICU beds. Similarly strong and clinically significant correlations were found during the second surged during July and August. There was no significant correlation when comparing to a similar dataset using 2019 ILI calls. Conclusion: Minnesota prehospital COVID19-related prehospital encounters are shown to accurately predict hospital bed utilization 1-2 weeks in advance. This was reproducible across two COVID19 surges. Trends in EMS patient encounters could serve as a valuable data point in predicting COVID19 surges and their effects on hospital utilization.

Stress of conscience of COVID-19 among perianaesthesia nurses having worked in a COVID-ICU during the coronavirus pandemic: an international perspective

BackgroundSeveral studies have reported that working in a COVID-ICU impacted nurses’ mental well-being. Yet little is known about how perianaesthesia nurses who have been working in a COVID-ICU perceived their stress of conscience. The aim of this study was to: (1) describe and compare stress related to troubled conscience among perianaesthesia nurses in three countries who have been working in a COVID-ICU during the pandemic, (2) compare their levels of troubled conscience between working in a COVID-ICU and their usual workplace, and (3) compare nurses that usually work in an ICU department with nurses who usually work outside of the ICU.MethodsA descriptive, international cross‐sectional online survey including the Stress of Conscience Questionnaire (SCQ) was distributed between organizational member countries of the International Collaboration of PeriAnaesthesia Nurses.ResultsA total of 246 nurses from three countries participated. Significant differences were found in stress of conscience when working in the Covid-ICU between Sweden 31.8 (8.6), Denmark 23.1 (8.6), and Netherlands 16.4 (6.5) p < 0.001. Significant differences were also found between nurses working in a COVID-ICU in contrast with their usual workplace: 23.1(5.6) versus 17.7(5.3), p < 0.001. The most stressful aspect of conscience reported was that work in the COVID-ICU was so demanding, nurses did not have sufficient energy to be involved with their family as much as they desired. No statistical differences were found between nurses that usually work in an ICU department with nurses who usually work outside of the ICU.ConclusionThe COVID-19 pandemic has negatively impacted stress of conscience among nurses working in the COVID-ICU. Swedish nurses were found to be more significantly impacted. This could be related to low numbers of existing ICU beds and ICU nurses prior to the pandemic necessitating a longer time required for working in a COVID-ICU. Stress of conscience also increased when working in the Covid-ICU compared to working in the usual workplace, and the most stressing aspect reported was that COVID-ICU work was so demanding that nurses did not have the energy to devote themselves to their family as they would have liked.

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.

The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11524-021-00601-7.

Intensive care unit strain and mortality risk in patients admitted from the ward in Australia and New Zealand.

ICU strain (low number of available beds) may be associated with a delay and altered threshold for ICU admission and adverse patient outcomes. We aimed to investigate the impact of ICU strain on hospital mortality in critically ill patients admitted from wards across Australia and New Zealand. Ward patient admitted to ICU and ICU bed data at 137 hospitals were accessed between January 2013 and December 2016. ICU strain was classified as low (≤0.5 patients admitted per available ICU bed in a 6-h block), medium (0.5 to ≤1) or high (>1). Logistic regression models were used to examine the relationship between ICU strain and hospital mortality. 57,844 ICU admissions were analysed, with the majority (64.4%) admitted to medium-strain ICUs. Those admitted to high-strain ICUs spent longer in hospital prior to ICU than medium-strain or low-strain ICUs. After adjusting for confounders those admitted to high-strain ICUs [OR 1.24 (95%CI 1.14-1.35)] or medium-strain ICUs [OR 1.18 (95%CI 1.09-1.27)], (p < 0.001) had a higher risk of death compared low-strain ICUs. ICU strain is associated with longer times in hospital prior to ICU admission and was associated with increased risk of death in patients admitted from ward.

Patient survey superior to EMR extraction for eliciting positive symptoms at COVID-19 illness onset.

Context: COVID-19 has ravaged nations around the world, with New York City (NYC) and the NYC suburbs being particular epicenters of COVID-19 infection. Suffolk County, NY, has reported over 200,000 cases and 3,000 deaths to date. Most initial COVID-19 research concerned hospitalized patients. Presenting symptomology in the outpatient setting was poorly characterized, as were the implications of specific presenting symptoms, beyond respiratory distress or hypoxia, for eventual disease severity. This made it difficult for primary care physicians to predict which patients would require hospitalization for COVID-19 disease or decompensate while being managed at home during a time when hospital and ICU beds were limited. Objective: To characterize presenting symptoms of COVID-19 infection in the outpatient setting and evaluate for correlation with severity, duration, and chronicity of disease. Study Design and Analysis: We collected survey data from both patient telephone interviews and electronic medical record (EMR) extraction. Patient characteristics were described using means and percentages when appropriate. Percentage of symptoms by severity level, symptom duration, COVID-19 testing and escalating medical care were calculated. To evaluate association of risk factors with positive testing, severity, duration and chronicity of symptoms, logistic regression was used. Patient characteristics, medications and repeat measures were evaluated as risk factors in logistic regression. Setting or Dataset: 107 patients with suspected and confirmed COVID-19 cases at the 3 primary care practices of Stony Brook University Hospital between March and December, 2020. Population Studied: adult, English speaking primary care patients with suspected or confirmed COVID-19 Intervention/Instrument: patient self report telephone survey, EMR data extraction survey Outcome Measures: symptom duration, symptom severity, persistence of symptoms at 3 month time point Significant Results: Patient self-report survey elicited nearly twice as many symptoms described at illness onset vs. those recorded in the EMR. Conclusions: Early in the setting of newly emerging infectious diseases, particularly those such as COVID-19 which involve multiple organ systems, patient self report of symptoms of illness rather than EMR extraction alone may be crucial both for identifying cases and in order to characterize pathophysiology of disease in real time.

540 Burn-Specific Triage Guidelines in State-Based Crisis Standards of Care

IntroductionIn times of crisis, medical institutions must utilize contingency plans to ensure the highest quality of patient care. When these plans are overwhelmed, crisis standards of care may be adopted, resulting in modifications in resource allocation. The current coronavirus pandemic has created tremendous strains on hospitals throughout the world, with periodic shortages in equipment, PPE, ICU beds, and personnel. These pressures have been great enough at times to result in several states implementing crisis standards of care to allow hospitals to triage patients and "do the most good possible for the largest number of people with limited resources". However, these guidelines may not account for the unique needs of burn patients, whose care is often resource intensive. We examined state-based crisis standards of care guidelines in the United States to ascertain the degree to which triage of burn patients was addressed.MethodsInternet search engines were used to locate state-specific actionable “crisis standards of care” or “scarce resource allocation” policies available before October 1, 2021. Once identified, these guidelines were further examined to determine whether explicit information was provided to direct the triage of burn patients.ResultsOf the 50 states and the District of Columbia, only 35 states (70%) were confirmed to have official crisis standards of care policies that could be implemented by healthcare institutions during the current pandemic. Additionally, guidelines from non-government entities were identified for 4 states (Florida Bioethics Network, Ohio Hospital Association, Missouri Hospital Association, and West Virginia Hospital Association). Of the 39 plans available, only 13 (26%) provided specific information regarding triage of burn patients during implementation of crisis standards of care.ConclusionsCrisis standards of care are heterogenous throughout the United States and have varying levels of specificity. The majority of states and the District of Columbia do not provide clear, actionable guidance on the triage of burn patients.

COVID-19 response in Colombia: Hits and misses

IntroductionColombia has been hit particularly hard by the COVID-19 pandemic, being ranked 22nd among 187 countries in deaths per 100,000 people by February 2022. The country has also experienced the worst economic recession in its history, with real GDP contracting 7% in 2020. This paper describes Colombia's pre-pandemic context and the overall effect of the pandemic on health and economic indicators and examines the government's response to COVID-19. MethodsDescriptive chronologic policy analysis on the COVID-19 pandemic in Colombia and the measures and interventions implemented, using publicly available secondary data from Colombia's official agencies and international sources published from March 2020 to February 2022. ResultsThe Colombian healthcare system increased its capacity in terms of infrastructure and human resources (from 8.6 ICU beds per 100,000 pre-pandemic to 21.9 in 2020, from 1.7 to 1.3 per 1000 doctors and nurses, respectively in 2019 to 1.8 and 1.5 per 1000 in 2020). The government commenced the National Vaccination Strategy on February 17, 2021, despite a slow start it managed to fully vaccinate 65.1% of its population, including Venezuelan immigrants, by February 2022. ConclusionsColombia's response to the pandemic combines hits and misses. The country organized an early and robust response; increased its healthcare system capacity; designed a risk-based, equitable, and inclusive national vaccination strategy; and introduced a flexible and responsive vaccine rollout. However, the government chose the wrong timing for reform, implemented a long and unpredictable lockdown, was late to procure COVID-19 vaccines and has not appropriately tackled vaccine hesitancy. Public interest summaryColombia has been hit particularly hard by the COVID-19 pandemic, with over 138,000 COVID-19 deaths and over 6 million cases by the end of February 2022. According to number of deaths per 100,000 people, Colombia ranks 22nd in the world, among 187 countries. The country has also experienced the worst economic recession in its history. The Colombian healthcare system has been able to cope with a high number of COVID-19 cases without collapsing, but some public health indicators deteriorated. Colombia's response to the pandemic combines hits and misses. The country organized an early and robust response; increased its healthcare system capacity; designed a risk-based, equitable, and inclusive national vaccination strategy; and introduced a flexible and responsive vaccine rollout. As of February 2022, Colombia has fully vaccinated 65.1% of its population. However, the government chose the wrong timing for a tax reform, implemented a long and unpredictable lockdown, was late to procure COVID-19 vaccines and has not appropriately tackled vaccine hesitancy, which certainly contributed to its undesirable position in the mortality world raking.

A Study for Plausible Third Wave of COVID-19 in India through Fuzzy Time Series Modelling Based on Particle Swarm Optimization and Fuzzy c-Means

The outbreak of COVID-19 has become a global pandemic as announced by World Health Organisation. As India has already met the two waves, named first and second wave, it is assumed that COVID-19 will again strike in India in the form of third wave. The peak during the upcoming third wave and determination of the approximated maximum number of COVID-19 infected cases and deaths at a particular day becomes crucial for India. To determine the peak of infectious curve, this article proposed a hybrid fuzzy time series forecasting model based on particle swarm optimization and fuzzy c-mean technique, named as fuzzy time series particle swarm optimization extended fuzzy c-mean technique. The proposed model works in two phases. In phase-I, particle swarm optimization extended fuzzy c-mean method is used to form initial intervals with the help of centroids, while in phase-II, these intervals are updated to form subintervals. In the present article, a fitness function is developed for particle swarm optimization to increase its convergence speed and basic fuzzy c-mean is extended by using an exponential function to tolerate the effect of outliers, named as extended fuzzy c-mean technique. The effectiveness of the proposed model has been tested based on mean square error and root mean square error on first and second wave COVID-19 data, and the obtained results are very close to the existing data of COVID-19 with less error rate. Thus, the proposed model is suitable to forecast a better approximation value of COVID-19 infected cases and deaths in India during the upcoming third wave. This study demonstrates that third wave of COVID-19 could occur in India, while also illustrating that it is unlikely for any such resurgence to be as large as the second wave. The proposed model predicts that the peak of third wave will occur approximately after 40–70 days from the mid of December. Furthermore, the impact of vaccination on infected cases and deaths during the upcoming third wave in India is also studied. With the implementation of the vaccine on the Indian people, the peak of COVID-19 infected during third wave will be shifted in forward direction. On the basis of the proposed model, government authorities will be enabling to know expected required resources such as hospital patient beds, ICU beds, and oxygen concentrators during the upcoming outspread of COVID-19 like disease in future.

SARS-CoV-2 Vaccination Coverage and Key Public Health Indicators May Explain Disparities in COVID-19 Country-Specific Case Fatality Rate Within European Economic Area.

AimTo investigate the reasons for disparity regarding the country-specific COVID-19-related case fatality rate (CFR) within the 30 countries of the European Economic Area (EEA).Materials and methodsData regarding population, area, COVID-19-associated infections/deaths, vaccination, life expectancy, elderly population, infant mortality, gender disparity, urbanization, gross domestic product (GDP), income per capita, health spending per capita, physicians, nursing personnel, hospital beds, ICU beds, hypertension, diabetes, obesity, and smoking from all EEA countries were collected from official sources on January 16, 2022. Correlation coefficients were computed, and optimal scaling using ridge regression was used to reach the most parsimonious multivariate model assessing any potential independent correlation of public health parameters with COVID-19 CFR.ResultsCOVID-19 CFR ranges from 0.1% (Iceland) to 4.0% (Bulgaria). All parameters but population density, GDP, total health spending (% of GDP), ICU beds, diabetes, and obesity were correlated with COVID-19 CFR. In the most parsimonious multivariate model, elderly population rate (P = 0.018), males/total ratio (P = 0.013), nurses/hospital beds (P = 0.001), physicians/hospital beds (P = 0.026), public health spending (P = 0.013), smoking rate (P = 0.013), and unvaccinated population rate (P = 0.00005) were demonstrated to present independent correlation with COVID-19 CFR. In detail, the COVID-19 CFR is estimated to increase by 1.24 times in countries with vaccination rate of <0.34, 1.11 times in countries with an elderly population rate of ≥0.20, 1.14 times in countries with male ratio values ≥0.493, 1.12 times in countries spending <2,000$ annually per capita for public health, 1.14 and 1.10 times in countries with <2.30 nurses and <0.88 physicians per hospital bed, respectively, and 1.12 in countries with smoking ratio ≥0.22, while holding all other independent variables of the model constant.ConclusionCOVID-19 CFR varies substantially among EEA countries and is independently linked with low vaccination rates, increased elderly population rate, diminished public health spending per capita, insufficient physicians and nursing personnel per hospital bed, and prevalent smoking habits. Therefore, public health authorities are awaited to consider these parameters in prioritizing actions to manage the SARS-CoV-2 pandemic.

Development and application of a dynamic transmission model of health systems' preparedness and response to COVID-19 in twenty-six Latin American and Caribbean countries.

The global impact of COVID-19 has challenged health systems across the world. This situation highlighted the need to develop policies based on scientific evidence to prepare the health systems and mitigate the pandemic. In this scenario, governments were urged to predict the impact of the measures they were implementing, how they related to the population's behavior, and the capacity of health systems to respond to the pandemic. The overarching aim of this research was to develop a customizable and open-source tool to predict the impact of the expansion of COVID-19 on the level of preparedness of the health systems of different Latin American and the Caribbean countries, with two main objectives. Firstly, to estimate the transmission dynamics of COVID-19 and the preparedness and response capacity of health systems in those countries, based on different scenarios and public policies implemented to control, mitigate, or suppress the spread of the epidemic. Secondly, to facilitate policy makers' decisions by allowing the model to adjust its parameters according to the specific pandemic trajectory and policy context. How many infections and deaths are estimated per day?; When are the peaks of cases and deaths expected, according to the different scenarios?; Which occupancy rate will ICU services have along the epidemiological curve?; When is the optimal time increase restrictions in order to prevent saturation of ICU beds?, are some of the key questions that the model can respond, and is publicly accessible through the following link: http://shinyapps.iecs.org.ar/modelo-covid19/. This open-access and open code tool is based on a SEIR model (Susceptible, Exposed, Infected and Recovered). Using a deterministic epidemiological model, it allows to frame potential scenarios for long periods, providing valuable information on the dynamics of transmission and how it could impact on health systems through multiple customized configurations adapted to specific characteristics of each country.

Brazil's health system functionality amidst of the COVID-19 pandemic: An analysis of resilience

BackgroundAs of December 31, 2020, Brazil had the second-highest burden of COVID-19 worldwide. Given the absence of federal government coordination, it was up to the local governments to maintain healthcare provision for non-COVID health issues. In this descriptive study, we aimed to discuss the SUS functionality and resilience, describing the impact of the pandemic on non-COVID health services delivery while considering the regional inequalities of the allocation of financing health system, health infrastructure and health workforce.MethodsWe used input-output framework based on the World Health Organization (WHO) Health System Building Blocks to estimate health system functionality and resilience. An ecological assessment was designed to calculated mean relative changes to compare the first year of the pandemic in Brazil with the previous one. All data used in this study were anonymized and made available by the Brazilian Ministry of Health. Input indicators were categorized in health system financing (federal funding received as well as expenditure of both state and city governments), health system's infrastructure (hospital beds) and health workforce (healthcare workers positions). Output indicators were categorized into nine different groups of service delivery procedures. To explore the relationship between the variation in procedures with socioeconomic conditions, we used the Socioeconomic Vulnerability Index (SVI).FindingsState governments had a 38·6% increase in federal transfers, while municipal governments had a 33·9% increase. The increase of ICU beds reached its peak in the third quarter of 2020, averaging 72·1% by the end of the year. The country also saw an increase in jobs for registered nurses (13·6%), nurse assistants (8·5%), physiotherapists (7·9%), and medical doctors (4·9%). All procedures underwent expressive reduction: Screenings (−42·6%); Diagnostic procedures (−28·9%); Physician appointments (−42·5%); Low and medium complexity surgeries (−59·7%); High complexity surgeries (−27·9%); Transplants (−44·7%); Treatments and clinical procedures due to injuries of external causes (−19·1%); Irrepressible procedures (−8·5%); and Childbirths (−12·6%). The most significant drop in procedures happened in the first quarter of the pandemic, followed by progressive increase; most regions had not yet recovered by the end of 2020. State-level changes in numbers of procedures point towards a negative trend with SVI.InterpretationThe Brazilian Government did not consider that socioeconomically vulnerable states were at a higher risk of being impacted by the overburden of the health system caused by the COVID-19, which resulted in poorer health system functionality for those vulnerable states. The lack of proper planning to improve health system resilience resulted in the decrease of a quarter of the amount of healthcare procedures increasing the already existing health disparities in the country.FundingMCTIC/CNPQ/FNDCT/MS/SCTIE/DECIT No 07/2020.

Intensive care unit capacity and mortality in older adults: a three nations retrospective observational cohort study

BackgroundIntensive care unit (ICU) admissions among older adults are expected to increase, while the benefit remains uncertain. The availability of ICU beds varies between hospitals and between countries and is an important factor in the decision to admit older adults in the ICU. We aimed to assess if a non-restrictive approach to ICU older adults admission is associated with a corresponding change in survival.MethodsRetrospective cohort study that included patients ≥ 80 years who were admitted to each of the three participating hospitals in Australia, Israel, and the United States (USA), between the years 2006–2015, each with distinct ICU capacities and admission criteria. The primary outcomes were in-hospital mortality and all-cause mortality at 6, 12, 18, and 24 months following index hospitalization.ResultsThe cohort included 62,866 patients with a mean age of 85.9 ± 4.6 years and 58.8% were women. The ICU admission rates were 22.5%, 2.6% and 2.3% in USA, Australia, and Israel, respectively. We constructed a model for ICU admissions based on the USA cohort (highest availability of ICU beds) and then calculated the expected probabilities for the Israeli and Australian cohorts. For the patients in the highest quintile of the admission model, actual ICU admission rates were 67.6% in USA, 22.1% in Australia and 6.0% in Israel. Of these, in-hospital death rates were 52.3% in Israel, 29.8% in Australia, and 22.1% in USA. Two years after hospital discharge, the survival rates in the USA and Australia were 53%, while in Israel 48%.ConclusionICU admission of adults ≥ 80 years is associated with increased in-hospital survival compared to ward admission, but survival rates 2 years later are similar.

Free Tissue Transfer during the COVID-19 Pandemic: A Proposed Evidence-Based Protocol for Early Discharge.

BACKGROUNDAs free tissue transfer outcomes improve, institutions are examining early discharge protocols. “Early” is generally defined as between one and five days postoperatively, which correlates with the timing of most major complications and most opportunities for flap salvage. Given the trend towards early discharge, the need for healthcare cost reductions and shortage of ICU beds during a viral pandemic, we aimed to propose an evidence-based protocol to select patients for discharge within 72 h of free tissue transfer. METHODSA retrospective review of all patients who underwent free tissue transfer at Vanderbilt University Medical Center, Tennessee, USA since the onset of the COVID-19 (2020-2021) pandemic was performed. Patients were included for review if they were discharged within 72 h of surgery. Literature relating to expedited discharge after free tissue transfer was also reviewed. RESULTSSix patients met inclusion criteria for retrospective review. None suffered intraoperative or postoperative inpatient complications and all were discharged within 72 h postoperatively. There were no flap failures within 30 d of reconstruction. CONCLUSIONThis study reviews a patient cohort undergoing free tissue transfer during the COVID-19 pandemic. These cases were reviewed for factors that may have contributed to their postoperative success after discharge within 72 hours. These data points were combined with published evidence on risks for failure after free flap reconstruction to design a protocol to select patients for early discharge. The benefits of early discharge include reducing healthcare costs, risks of inpatient hospitalization, and ICU utilization, which is of paramount importance in the midst of a global pandemic..

Perceptions of Critical Care Shortages, Resource Use, and Provider Well-being During the COVID-19 Pandemic: A Survey of 1,985 Health Care Providers in Brazil

BackgroundBrazil has been disproportionately affected by COVID-19, placing a high burden on ICUs.Research QuestionAre perceptions of ICU resource availability associated with end-of-life decisions and burnout among health care providers (HCPs) during COVID-19 surges in Brazil?Study Design and MethodsWe electronically administered a survey to multidisciplinary ICU HCPs during two 2-week periods (in June 2020 and March 2021) coinciding with COVID-19 surges. We examined responses across geographical regions and performed multivariate regressions to explore factors associated with reports of: (1) families being allowed less input in decisions about maintaining life-sustaining treatments for patients with COVID-19 and (2) emotional distress and burnout.ResultsWe included 1,985 respondents (57% physicians, 14% nurses, 12% respiratory therapists, 16% other HCPs). More respondents reported shortages during the second surge compared with the first (P < .05 for all comparisons), including lower availability of intensivists (66% vs 42%), ICU nurses (53% vs 36%), ICU beds (68% vs 22%), and ventilators for patients with COVID-19 (80% vs 70%); shortages were highest in the North. One-quarter of HCPs reported that families were allowed less input in decisions about maintaining life-sustaining treatments for patients with COVID-19, which was associated with lack of intensivists (adjusted relative risk [aRR], 1.37; 95% CI, 1.05-1.80) and ICU beds (aRR, 1.71; 95% CI, 1.16-2.62) during the first surge and lack of N95 masks (aRR, 1.43; 95% CI, 1.10-1.85), noninvasive positive pressure ventilation (aRR, 1.56; 95% CI, 1.18-2.07), and oxygen concentrators (aRR, 1.50; 95% CI, 1.13-2.00) during the second surge. Burnout was higher during the second surge (60% vs 71%; P < .001), associated with witnessing colleagues at one’s hospital contract COVID-19 during both surges (aRR, 1.55 [95% CI, 1.25-1.93] and 1.31 [95% CI, 1.11-1.55], respectively), as well as worries about finances (aRR, 1.28; 95% CI, 1.02-1.61) and lack of ICU nurses (aRR, 1.25; 95% CI, 1.02-1.53) during the first surge.InterpretationDuring the COVID-19 pandemic, ICU HCPs in Brazil experienced substantial resource shortages, health care disparities between regions, changes in end-of-life care associated with resource shortages, and high proportions of burnout.

Spread of multidrug-resistant Acinetobacter baumannii isolates belonging to IC1 and IC5 major clones in Rondônia state.

In Brazil, carbapenem-resistant A. baumannii (CRAB) is a critical pathogen showing high carbapenem resistance rates. Currently, there is little epidemiological data on A. baumannii isolated in the Northern Brazilian region. Herein, this study aimed to characterize the resistance mechanisms of CRAB isolates recovered from hospitalized patients in the state of Rondônia in 2019. Most of CRAB were considered as extensively drug-resistant, and some of them showed high MICs for minocycline. Only polymyxins showed a satisfactory activity. All isolates carried blaOXA-23 and were included in 14 distinct clusters, with the predominance of clonal group A (29%). The IC1 was the most frequent clonal group, followed by IC5 and IC4. Here, we firstly reported the epidemiological scenario of CRAB in the state of Rondônia, located in the Brazilian Amazon region. The high frequency of CRAB presenting XDR phenotype is of great concern, due to limited therapeutical options, especially in the actual pandemic scenario, in which we observed an overcrowding of ICU beds. Such results are essential to better characterize the epidemiology of CRAB in the entire Brazilian territory.

Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic.

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents’ COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May–December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0–1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.

Characteristics and potential hemodynamic benefits of proning on the right ventricle in intensive care patients with SARS-CoV-2 ARDS

Abstract Funding Acknowledgements Type of funding sources: None. Introduction One of the key challenges in treating COVID-19 ARDS patients is hemodynamic monitoring. Therapies proven to be effective in ARDS, such as protective ventilation, fluid restriction or high PEEP tend to alter right ventricular (RV) function and indicate a closer in-bed management, which is particularly difficult in prone position (PP) patients. Transthoracic echocardiography(TTE) enables a direct window for hemodynamic monitoring of RV performance. Objective To assess characteristics and potential hemodynamic benefits of proning on the RV in ICU patients with SARS-CoV-2 ARDS, using echocardiography. Methods This is an observational, cross-sectional study of SARS-CoV-2 ARDS in 11 patients hospitalized in a 12 bed ICU in Farhat Hached University hospital in December 2020. All patients were deeply sedated and curarized during the study. After a stabilization period (MAP ≥ 65mmHg), TTE was performed first in the supine position before putting the patient on PP. Same measures were repeated during a second scan 4 to 8 hours after PP setting. Norepinephrine infusion levels were not changed between the two scans. The average duration of a scan was 20 ± 10 minutes depending mostly on echogenicity. Several frequencies and harmonics were tested and we selected the ones that provided the best image quality. PP ventilation (PPV) was performed continuously for 12 hours using the "swimmer position" that enabled affordable TTE views. A quantitative study of the RV was performed using RV focused 4C view. A special view dedicated to the IVC was studied. Parameters measured were RV basal end diastolic diameter, S’ peak wave in TDI, tricuspid annular plane systolic excursion (TAPSE), fractional area change (FAC) and maximal trans tricuspid velocity (MTTV). Post hoc analysis was performed by two readers (one intensivist and one cardiologist). All parameters are expressed as a mean of two measurements. Results Twenty-four TTEs were performed in 11 patients respectively 40 ± 15mn before and 5.8 ± 1.1hrs after the PP setting. Patients were 71.0 ± 9.4yrs aged, 5(45%) obese, 7(63%) had diabetes mellitus, 4(36%) hypertension, 1(9%) coronary artery disease. All (100%) were on sinus rhythm. 7(63.6%) presented moderate ARDS and 3(27.3%) severe ARDS. None of the patients developed RV failure prior to the examination. The PPV achieved a non-statistically significant improvement in RV function as assessed by the increase of S′, TAPSE, a decrease in RV basal diameter and an increase in FAC. A statistically significant decrease in MTTV (mean 3.20 ± 0.49 m/s before PP vs mean 2.47 ± 0.77m/s (p = 0.01)) was objectified. Conclusions PPV improves hemodynamic parameters of the RV. TEE 4C view is sufficient in the assessment of RV function in PP. SPAP monitoring could be the key parameter to quickly and reliably assess RV response to PPV.

Abstract WP32: Thinking Outside The Box In Delivering Advanced Stroke Care In A Pandemic

Background: The challenges presented by the COVID-19 Pandemic have caused healthcare providers, hospitals, and systems of care to re-think how care is provided to other acute time-sensitive diseases, such as stroke. Stroke admissions to hospitals decreased during the initial surge of COVID-19 cases. Purpose: To ensure Stroke patients who are eligible for endovascular therapy (EVT) receive timely access to evaluation and treatment when healthcare systems may be too overwhelmed with lack of post-procedural ICU beds and resources. Method: As a Primary Stroke Center (PSC), patients with stroke symptoms are quickly evaluated for potential treatment and advanced therapy. With Rapid software imaging, CTA and CT-P are readily available and reliable tools to diagnosis Large Vessel Occlusions (LVO). When eligible for EVT, patients are transferred emergently to a Comprehensive Stroke Center. With limited resources in the region, delays in care and transfers, ED and ICU beds were not readily available. Neuro-interventionalists were available to perform EVT but limited for lack of ICU bed availability. The CSC and PSC collaborated to develop a plan to transfer eligible patients for the EVT and transfer patient back to PSC ICU for post-procedure care. This allows the patient to receive appropriate and timely stroke care despite limited access to ICU beds at the CSC. Results: The CSC collaborated with the PSC to put this contingency plan into place. When an EVT eligible patient is identified at the PSC, the CSC is contacted for evaluation. If the patient is a candidate, the PCS admits patient to ICU and emergently transfers to CSC for outpatient procedure. This was approved by the billing department at both facilities. Conclusions: Modifications to stroke algorithms and care may be necessary during a Pandemic to maintain safe, efficient, and effective care. Communication and collaboration are key between stroke centers and stroke systems of care to facilitate positive outcomes without adding more challenges to an overwhelmed healthcare system

Abstract TMP21: Impact Of COVID-19 State-level Hospital Capacity On Overall Stroke Mortality In 2020 In The United States

Background: Although hospital admissions for stroke declined in 2020 during the COVID-19 pandemic, patients with comorbid COVID-19 and stroke had increased mortality. We explored stroke mortality in 2020 and its association with COVID-19 prevalence and state-level hospital capacities. Methods: We analyzed CDC National Vital Statistics System and COVID Data Tracker data from 2017-2020. The primary outcome was age-adjusted stroke (ischemic and hemorrhagic) mortality rate per 100,000. The secondary outcome was % change in state-level stroke mortality rates in 2020 (vs. 2017-19); we report its correlation with state-level 1) prevalence of confirmed COVID-19 infections by 12/31/2021, 2) total COVID mortality by 12/31/20, and the 2020 average state-level % of 3) hospital and 4) ICU beds occupied by COVID-19 patients. Results: Figure 1A shows the typical seasonal decline in stroke mortality in quarters 2/3 was attenuated in 2020. The % change in state-level stroke mortality in 2020 (Figure 1B) was not correlated with prevalence of COVID-19 infection (rho=0.05, p=0.74), mortality (rho=0.10, p=0.49), or the % of ICU beds occupied by COVID-19 patients (rho=0.24, p=0.09). There was a correlation with % of hospital beds occupied by COVID-19 patients (rho=0.35, p=0.01) (Figure 2). Conclusion: Overall stroke mortality increased in 2020, particularly in Q2/3, the early-to-mid phase of the COVID-19 pandemic. At the state level, the average % of all hospital beds occupied by COVID-19 patients in 2020 was the only COVID-19 metric associated with change in stroke mortality. Future work should determine if this association was due to decreased hospital capacity to deliver standard stroke care.

Regional responsibility and coordination of appropriate inpatient care capacities for patients with COVID-19 - the German DISPENSE model.

As of late 2019, the COVID-19 pandemic has been a challenge to health care systems worldwide. Rapidly rising local COVID-19 incidence rates, result in demand for high hospital and intensive care bed capacities on short notice. A detailed up-to-date regional surveillance of the dynamics of the pandemic, precise prediction of required inpatient capacities of care as well as a centralized coordination of the distribution of regional patient fluxes is needed to ensure optimal patient care. In March 2020, the German federal state of Saxony established three COVID-19 coordination centers located at each of its maximum care hospitals, namely the University Hospitals Dresden and Leipzig and the hospital Chemnitz. Each center has coordinated inpatient care facilities for the three regions East, Northwest and Southwest Saxony with 36, 18 and 29 hospital sites, respectively. Fed by daily data flows from local public health authorities capturing the dynamics of the pandemic as well as daily reports on regional inpatient care capacities, we established the information and prognosis tool DISPENSE. It provides a regional overview of the current pandemic situation combined with daily prognoses for up to seven days as well as outlooks for up to 14 days of bed requirements. The prognosis precision varies from 21% and 38% to 12% and 15% relative errors in normal ward and ICU bed demand, respectively, depending on the considered time period. The deployment of DISPENSE has had a major positive impact to stay alert for the second wave of the COVID-19 pandemic and to allocate resources as needed. The application of a mathematical model to forecast required bed capacities enabled concerted actions for patient allocation and strategic planning. The ad-hoc implementation of these tools substantiates the need of a detailed data basis that enables appropriate responses, both on regional scales in terms of clinic resource planning and on larger scales concerning political reactions to pandemic situations.