The future of intensive care: the study of the microcirculation will help to guide our therapies.

The goal of hemodynamic resuscitation is to optimize the microcirculation of organs to meet their oxygen and metabolic needs. Clinicians are currently blind to what is happening in the microcirculation of organs, which prevents them from achieving an additional degree of individualization of the hemodynamic resuscitation at tissue level. Indeed, clinicians never know whether optimization of the microcirculation and tissue oxygenation is actually achieved after macrovascular hemodynamic optimization. The challenge for the future is to have noninvasive, easy-to-use equipment that allows reliable assessment and immediate quantitative analysis of the microcirculation at the bedside. There are different methods for assessing the microcirculation at the bedside; all have strengths and challenges. The use of automated analysis and the future possibility of introducing artificial intelligence into analysis software could eliminate observer bias and provide guidance on microvascular-targeted treatment options. In addition, to gain caregiver confidence and support for the need to monitor the microcirculation, it is necessary to demonstrate that incorporating microcirculation analysis into the reasoning guiding hemodynamic resuscitation prevents organ dysfunction and improves the outcome of critically ill patients.

Utilization of Palliative Care for Patients with COVID-19 and Acute Kidney Injury during a COVID-19 Surge.

BACKGROUND AND OBJECTIVES: AKI is a common complication of coronavirus disease 2019 (COVID-19) and is associated with high mortality. Palliative care, a specialty that supports patients with serious illness, is valuable for these patients but is historically underutilized in AKI. The objectives of this paper are to describe the use of palliative care in patients with AKI and COVID-19 and their subsequent health care utilization. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted a retrospective analysis of New York University Langone Health electronic health data of COVID-19 hospitalizations between March 2, 2020 and August 25, 2020. Regression models were used to examine characteristics associated with receiving a palliative care consult. RESULTS: Among patients with COVID-19 (n=4276; 40%), those with AKI (n=1310; 31%) were more likely than those without AKI (n=2966; 69%) to receive palliative care (AKI without KRT: adjusted odds ratio, 1.81; 95% confidence interval, 1.40 to 2.33; P<0.001; AKI with KRT: adjusted odds ratio, 2.45; 95% confidence interval, 1.52 to 3.97; P<0.001), even after controlling for markers of critical illness (admission to intensive care units, mechanical ventilation, or modified sequential organ failure assessment score); however, consults came significantly later (10 days from admission versus 5 days; P<0.001). Similarly, 66% of patients initiated on KRT received palliative care versus 37% (P<0.001) of those with AKI not receiving KRT, and timing was also later (12 days from admission versus 9 days; P=0.002). Despite greater use of palliative care, patients with AKI had a significantly longer length of stay, more intensive care unit admissions, and more use of mechanical ventilation. Those with AKI did have a higher frequency of discharges to inpatient hospice (6% versus 3%) and change in code status (34% versus 7%) than those without AKI. CONCLUSIONS: Palliative care was utilized more frequently for patients with AKI and COVID-19 than historically reported in AKI. Despite high mortality, consultation occurred late in the hospital course and was not associated with reduced initiation of life-sustaining interventions. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_02_24_CJN11030821.mp3.

Clinical Trends Among U.S. Adults Hospitalized With COVID-19, March to December 2020 : A Cross-Sectional Study.

BACKGROUND: The COVID-19 pandemic has caused substantial morbidity and mortality. OBJECTIVE: To describe monthly clinical trends among adults hospitalized with COVID-19. DESIGN: Pooled cross-sectional study. SETTING: 99 counties in 14 states participating in the Coronavirus Disease 2019-Associated Hospitalization Surveillance Network (COVID-NET). PATIENTS: U.S. adults (aged ≥18 years) hospitalized with laboratory-confirmed COVID-19 during 1 March to 31 December 2020. MEASUREMENTS: Monthly hospitalizations, intensive care unit (ICU) admissions, and in-hospital death rates per 100 000 persons in the population; monthly trends in weighted percentages of interventions, including ICU admission, mechanical ventilation, and vasopressor use, among an age- and site-stratified random sample of hospitalized case patients. RESULTS: Among 116 743 hospitalized adults with COVID-19, the median age was 62 years, 50.7% were male, and 40.8% were non-Hispanic White. Monthly rates of hospitalization (105.3 per 100 000 persons), ICU admission (20.2 per 100 000 persons), and death (11.7 per 100 000 persons) peaked during December 2020. Rates of all 3 outcomes were highest among adults aged 65 years or older, males, and Hispanic or non-Hispanic Black persons. Among 18 508 sampled hospitalized adults, use of remdesivir and systemic corticosteroids increased from 1.7% and 18.9%, respectively, in March to 53.8% and 74.2%, respectively, in December. Frequency of ICU admission, mechanical ventilation, and vasopressor use decreased from March (37.8%, 27.8%, and 22.7%, respectively) to December (20.5%, 12.3%, and 12.8%, respectively); use of noninvasive respiratory support increased from March to December. LIMITATION: COVID-NET covers approximately 10% of the U.S. population; findings may not be generalizable to the entire country. CONCLUSION: Rates of COVID-19-associated hospitalization, ICU admission, and death were highest in December 2020, corresponding with the third peak of the U.S. pandemic. The frequency of intensive interventions for management of hospitalized patients decreased over time. These data provide a longitudinal assessment of clinical trends among adults hospitalized with COVID-19 before widespread implementation of COVID-19 vaccines. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark.

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients.

BACKGROUND: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. METHODS: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. RESULTS: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). CONCLUSIONS: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation.

Social Media and Research Publication Activity During Early Stages of the COVID-19 Pandemic: Longitudinal Trend Analysis.

BACKGROUND: The COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discoveries. Current platforms available for the distribution of scientific and clinical research data and information include preprint repositories and traditional peer-reviewed journals. In recent times, social media has emerged as a helpful platform to share scientific and medical discoveries. OBJECTIVE: This study aimed to comparatively analyze activity on social media (specifically, Twitter) and that related to publications in the form of preprint and peer-reviewed journal articles in the context of COVID-19 and gastroenterology during the early stages of the COVID-19 pandemic. METHODS: COVID-19-related data from Twitter (tweets and user data) and articles published in preprint servers (bioRxiv and medRxiv) as well as in the PubMed database were collected and analyzed during the first 6 months of the pandemic, from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ-specific social media trends were compared to preprint and publication activity. Any relationship between Twitter activity and preprint articles published and that between Twitter activity and PubMed articles published overall, by organ system, and by geographic location were identified using Spearman's rank-order correlation. RESULTS: Over the 6-month period, 73,079 tweets from 44,609 users, 7164 journal publications, and 4702 preprint publications were retrieved. Twitter activity (ie, number of tweets) peaked in March 2020, whereas preprint and publication activity (ie, number of articles published) peaked in April 2020. Overall, strong correlations were identified between trends in Twitter activity and preprint and publication activity (P<.001 for both). COVID-19 data across the three platforms mainly concentrated on pulmonology or critical care, but when analyzing the field of gastroenterology specifically, most tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal gastroenterology. Furthermore, there were significant positive associations between trends in Twitter and publication activity for all gastroenterology topics (luminal gastroenterology: P=.009; hepatology and inflammatory bowel disease: P=.006; gastrointestinal endoscopy: P=.007), except pancreatology (P=.20), suggesting that Twitter activity did not correlate with publication activity for this topic. Finally, Twitter activity was the highest in the United States (7331 tweets), whereas PubMed activity was the highest in China (1768 publications). CONCLUSIONS: The COVID-19 pandemic has highlighted the potential of social media as a vehicle for disseminating scientific information during a public health crisis. Sharing and spreading information on COVID-19 in a timely manner during the pandemic has been paramount; this was achieved at a much faster pace on social media, particularly on Twitter. Future investigation could demonstrate how social media can be used to augment and promote scholarly activity, especially as the world begins to increasingly rely on digital or virtual platforms. Scientists and clinicians should consider the use of social media in augmenting public awareness regarding their scholarly pursuits.

Comparing Critical Care Admissions Among Urban Populations Before and During the COVID-19 Pandemic.

In the context of the COVID-19 pandemic, reassessing intensive care unit (ICU) use by population should be a priority for hospitals planning for critical care resource allocation. In our study, we reviewed the impact of COVID-19 on a community hospital serving an urban region, comparing the sociodemographic distribution of ICU admissions before and during the pandemic. We executed a time-sensitive analysis to see if COVID-19 ICU admissions reflect the regional sociodemographic populations and ICU admission trends before the pandemic. Sociodemographic variables included sex, race, ethnicity, and age of adult patients (ages 18 years and older) admitted to the hospital's medical and cardiac ICUs, which were converted to COVID-19 ICUs. The time period selected was 18 months, which was then dichotomized into pre-COVID-19 admissions (December 1, 2018 to March 13, 2020) and COVID-19 ICU admissions (March 14 to May 31, 2020). Variables were compared using Fisher's exact tests and Wilcoxon tests when appropriate. During the 18-month period, 1,861 patients were admitted to the aforementioned ICUs. The mean age of the patients was 62.75 (SD 15.57), with the majority of these patients being male (52.23%), White (64.43%), and non-Hispanic/Latinx (95.75%). Differences were found in racial and ethnic distribution comparing pre-COVID-19 admissions to COVID-19 admissions. Compared with pre-COVID-19 ICU admissions, we found an increase in African American versus White admissions (P = .01) and an increase in Hispanic/Latinx versus non-Hispanic/Latinx admissions (P < .01), during the COVID-19 pandemic. During the first 3 months of admissions to COVID-19 ICUs, the number of admissions among Hispanic/Latinx and African American patients increased while the number of admissions among non-Hispanic/Latinx and White patient decreased, compared with the pre-COVID-19 period. These findings support development of strategies to enhance allocation of resources to bolster novel, equitable strategies to mitigate the incidence of COVID-19 in urban populations.

COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease.

One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0-15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.

Relation of severe COVID-19 to polypharmacy and prescribing of psychotropic drugs: the REACT-SCOT case-control study.

BACKGROUND: The objective of this study was to investigate the relation of severe COVID-19 to prior drug prescribing. METHODS: Severe cases were defined by entry to critical care or fatal outcome. For this matched case-control study (REACT-SCOT), all 4251 cases of severe COVID-19 in Scotland since the start of the epidemic were matched for age, sex and primary care practice to 36,738 controls from the population register. Records were linked to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days. RESULTS: Severe COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in a care home, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.8, 13.3), and in those without any of the conditions designated as conferring increased risk of COVID-19. Of 17 drug classes postulated at the start of the epidemic to be "medications compromising COVID", all were associated with increased risk of severe COVID-19 and these associations were present in those without any of the designated risk conditions. The fraction of cases in the population attributable to exposure to these drug classes was 38%. The largest effect was for antipsychotic agents: rate ratio 4.18 (3.42, 5.11). Other drug classes with large effects included proton pump inhibitors (rate ratio 2.20 (1.72, 2.83) for = 2 defined daily doses/day), opioids (3.66 (2.68, 5.01) for = 50 mg morphine equivalent/day) and gabapentinoids. These associations persisted after adjusting for covariates and were stronger with recent than with non-recent exposure. CONCLUSIONS: Severe COVID-19 is associated with polypharmacy and with drugs that cause sedation, respiratory depression, or dyskinesia; have anticholinergic effects; or affect the gastrointestinal system. These associations are not easily explained by co-morbidity. Measures to reduce the burden of mortality and morbidity from COVID-19 should include reinforcing existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy. REGISTRATION: ENCEPP number EUPAS35558.

How the COVID-19 pandemic will change the future of critical care.

Coronavirus disease 19 (COVID-19) has posed unprecedented healthcare system challenges, some of which will lead to transformative change. It is obvious to healthcare workers and policymakers alike that an effective critical care surge response must be nested within the overall care delivery model. The COVID-19 pandemic has highlighted key elements of emergency preparedness. These include having national or regional strategic reserves of personal protective equipment, intensive care unit (ICU) devices, consumables and pharmaceuticals, as well as effective supply chains and efficient utilization protocols. ICUs must also be prepared to accommodate surges of patients and ICU staffing models should allow for fluctuations in demand. Pre-existing ICU triage and end-of-life care principles should be established, implemented and updated. Daily workflow processes should be restructured to include remote connection with multidisciplinary healthcare workers and frequent communication with relatives. The pandemic has also demonstrated the benefits of digital transformation and the value of remote monitoring technologies, such as wireless monitoring. Finally, the pandemic has highlighted the value of pre-existing epidemiological registries and agile randomized controlled platform trials in generating fast, reliable data. The COVID-19 pandemic is a reminder that besides our duty to care, we are committed to improve. By meeting these challenges today, we will be able to provide better care to future patients.

Impact of vaccination by priority group on UK deaths, hospital admissions and intensive care admissions from COVID-19.

National (and global) vaccination provides an opportunity to control the COVID-19 pandemic, which disease suppression by societal lockdown and individual behavioural changes will not. We modelled how vaccination through the UK's vaccine priority groups impacts deaths, hospital and ICU admissions from COVID-19. We used the UK COVID-19 vaccines delivery plan and publicly available data to estimate UK population by age group and vaccination priority group, including frontline health and social care workers and individuals deemed 'extreme clinical vulnerable' or 'high risk'. Using published data on numbers and distributions of COVID-19-related hospital and ICU admissions and deaths, we modelled the impact of vaccination by age group. We then modified the model to account for hospital and ICU admission, and death among health and social care workers and the population with extreme clinical vulnerability and high risk. Our model closely matches the government's estimates for mortality after vaccination of priority groups 1-4 and groups 1-9. The model shows vaccination will have a much slower impact on hospital and ICU admissions than on deaths. The early prioritisation of healthcare staff and clinically vulnerable patients increases the impact of vaccination on admissions and also protects the healthcare service. An inflection point, when 50% of the adult population has been vaccinated - with deaths reduced by 95% and hospital admissions by 80% - may be a useful point for re-evaluating vaccine prioritisation. Our model suggests substantial reductions in hospital and ICU admissions will not occur until late March and into April 2021.

Facilitating Virtual Visitation in Critical Care Units During a Pandemic.

Presence of support persons enhances patient and family satisfaction. The introduction of the coronavirus disease-2019 (COVID-19) pandemic has impacted hospital operations and has reduced visitation. A virtual visitation program was implemented in critical care units to replicate visitation by video chat to ease stress on patients and family members to improve communication.

Surge-in-Place: Conversion of a Cardiac Catheterization Laboratory Into a COVID-19 Intensive Care Unit and Back Again.

In Spring 2020, the United States epicenter of COVID-19 was New York City, in which the borough of the Bronx was particularly affected. This Fall, there has been a resurgence of COVID-19 in Europe and the Midwestern United States. We describe our experience transforming our cardiac catheterization laboratories to accommodate an influx of COVID-19 patients so as to provide other hospitals with a potential blueprint. We transformed our pre/postprocedural patient care areas into COVID-19 intensive care and step-down units and maintained emergent invasive care for ST-segment elevation myocardial infarction using existing space and personnel.