Intensive Care Unit Occupancy Research Articles

The association of strained ICU capacity with hospital patient racial and ethnic composition and federal relief during the COVID-19 pandemic.

ObjectiveTo identify the association between strained intensive care unit (ICU) capacity during the COVID‐19 pandemic and hospital racial and ethnic patient composition, federal pandemic relief, and other hospital characteristics.Data SourcesWe used government data on hospital capacity during the pandemic and Provider Relief Fund (PRF) allocations, Medicare claims and enrollment data, hospital cost reports, and Social Vulnerability Index data.Study DesignWe conducted cross‐sectional bivariate analyses relating strained capacity and PRF award per hospital bed with hospital patient composition and other characteristics, with and without adjustment for hospital referral region (HRR).Data CollectionWe linked PRF data to CMS Certification Numbers based on hospital name and location. We used measures of racial and ethnic composition generated from Medicare claims and enrollment data. Our sample period includes the weeks of September 18, 2020 through November 5, 2021, and we restricted our analysis to short‐term, general hospitals with at least one intensive care unit (ICU) bed. We defined “ICU strain share” as the proportion of ICU days occurring while a given hospital had an ICU occupancy rate ≥ 90%.Principal FindingsAfter adjusting for HRR, hospitals in the top tercile of Black patient shares had higher ICU strain shares than did hospitals in the bottom tercile (30% vs. 22%, p < 0.05) and received greater PRF amounts per bed ($118,864 vs. $92,407, p < 0.05). Having high versus low ICU occupancy relative to pre‐pandemic capacity was associated with a modest increase in PRF amounts per bed after adjusting for HRR ($107,319 vs. $96,627, p < 0.05), but there were no statistically significant differences when comparing hospitals with high versus low ICU occupancy relative to contemporaneous capacity.ConclusionsHospitals with large Black patient shares experienced greater strain during the pandemic. Although these hospitals received more federal relief, funding was not targeted overall toward hospitals with high ICU occupancy rates.

The effect of COVID certificates on vaccine uptake, health outcomes, and the economy

In the COVID-19 pandemic many countries required COVID certificates, proving vaccination, recovery, or a recent negative test, to access public and private venues. We estimate their effect on vaccine uptake for France, Germany, and Italy using counterfactuals constructed via innovation diffusion theory. The announcement of COVID certificates during summer 2021 were associated – although causality cannot be directly inferred – with increased vaccine uptake in France of 13.0 (95% CI 9.7–14.9) percentage points (p.p.) of the total population until the end of the year, in Germany 6.2 (2.6–6.9) p.p., and in Italy 9.7 (5.4–12.3) p.p. Based on these estimates, an additional 3979 (3453–4298) deaths in France, 1133 (−312–1358) in Germany, and 1331 (502–1794) in Italy were averted; and gross domestic product (GDP) losses of €6.0 (5.9–6.1) billion in France, €1.4 (1.3–1.5) billion in Germany, and €2.1 (2.0–2.2) billion in Italy were prevented. Notably, in France, the application of COVID certificates averted high intensive care unit occupancy levels where prior lockdowns were instated.

COVID‐19 pandemic in Southern Brazil: Hospitalizations, intensive care unit admissions, lethality rates, and length of stay between March 2020 and April 2022

Coronavirus disease 2019 (COVID‐19) pandemic spread rapidly with more than 515 million cases and 6.2 million deaths. Epidemiological factors are important for understanding the state of the pandemic. This study aims to evaluate the hospitalizations, intensive care unit (ICU) admissions, and lethality from March 2020 to April 2022. Data were collected from a hospital in Porto Alegre city, southern Brazil. The Mann–Whitney, analysis of variance, and Kruskal–Wallis tests were used to compare quantitative variables. Categorical variables were compared by Pearson's χ 2 test. p values <0.05 for all tests were considered significant. Were observed 3784 hospitalizations. Males were 51.4% and the age was 60.4± 20.3. Intensive care unit (ICU) patients were 31.2%, the median length of stay (LOS) was 9.0 and lethality was 13.3%. ICU lethality was 34.5% versus 4.6% in other inpatients (p < 0.01). The LOS of ICU patients was 22.0 versus 7.0 in other inpatients (p < 0.01). The first peak (July–Novemebr 2020) showed ICU occupancy of 79.1%. The second peak (December 2020–June 2021) with 91.6% occupancy. The third peak January–March 2022 with 81.0% occupancy (p < 0.01). Lethality rates were 10.3% in 2020, 14.9% in 2021 and 15.4% in 2022 (p < 0.01). In conclusion, the ICU occupancy rate was higher in 2021 and the lethality rates of ICU patients were high during pandemic years (10.3% in 2020, 14.9% in 2021, and 15.2% in 2022). The lethality of these patients ranged from 25.0% in March to 21.8% in December 2020, from 20.9% in January 22.2% in Decemebr 2021, and 35.7% in January 2022 to 21.4% in April 2022. These data demonstrate that COVID‐19 is a critical illness, even in a private hospital setting.

Clinical Severity of SARS-CoV-2 Omicron Variant Compared with Delta among Hospitalized COVID-19 Patients in Belgium during Autumn and Winter Season 2021-2022.

This retrospective multi-center matched cohort study assessed the risk for severe COVID-19 (combination of severity indicators), intensive care unit (ICU) admission, and in-hospital mortality in hospitalized patients when infected with the Omicron variant compared to when infected with the Delta variant. The study is based on a causal framework using individually-linked data from national COVID-19 registries. The study population consisted of 954 COVID-19 patients (of which, 445 were infected with Omicron) above 18 years old admitted to a Belgian hospital during the autumn and winter season 2021–2022, and with available viral genomic data. Patients were matched based on the hospital, whereas other possible confounders (demographics, comorbidities, vaccination status, socio-economic status, and ICU occupancy) were adjusted for by using a multivariable logistic regression analysis. The estimated standardized risk for severe COVID-19 and ICU admission in hospitalized patients was significantly lower (RR = 0.63; 95% CI (0.30; 0.97) and RR = 0.56; 95% CI (0.14; 0.99), respectively) when infected with the Omicron variant, whereas in-hospital mortality was not significantly different according to the SARS-CoV-2 variant (RR = 0.78, 95% CI (0.28–1.29)). This study demonstrates the added value of integrated genomic and clinical surveillance to recognize the multifactorial nature of COVID-19 pathogenesis.

Severity of infection with the SARS-CoV-2 B.1.1.7 lineage among hospitalized COVID-19 patients in Belgium.

The pathogenesis of COVID-19 depends on the interplay between host characteristics, viral characteristics and contextual factors. Here, we compare COVID-19 disease severity between hospitalized patients in Belgium infected with the SARS-CoV-2 variant B.1.1.7 and those infected with previously circulating strains. The study is conducted within a causal framework to study the severity of SARS-CoV-2 variants by merging surveillance registries in Belgium. Infection with SARS-CoV-2 B.1.1.7 ('exposed') was compared to infection with previously circulating strains ('unexposed') in terms of the manifestation of severe COVID-19, intensive care unit (ICU) admission, or in-hospital mortality. The exposed and unexposed group were matched based on the hospital and the mean ICU occupancy rate during the patient's hospital stay. Other variables identified as confounders in a Directed Acyclic Graph (DAG) were adjusted for using regression analysis. Sensitivity analyses were performed to assess the influence of selection bias, vaccination rollout, and unmeasured confounding. We observed no difference between the exposed and unexposed group in severe COVID-19 disease or in-hospital mortality (RR = 1.15, 95% CI [0.93-1.38] and RR = 0.92, 95% CI [0.62-1.23], respectively). The estimated standardized risk to be admitted in ICU was significantly higher (RR = 1.36, 95% CI [1.03-1.68]) when infected with the B.1.1.7 variant. An age-stratified analysis showed that among the younger age group (≤65 years), the SARS-CoV-2 variant B.1.1.7 was significantly associated with both severe COVID-19 progression and ICU admission. This matched observational cohort study did not find an overall increased risk of severe COVID-19 or death associated with B.1.1.7 infection among patients already hospitalized. There was a significant increased risk to be transferred to ICU when infected with the B.1.1.7 variant, especially among the younger age group. However, potential selection biases advocate for more systematic sequencing of samples from hospitalized COVID-19 patients.

SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021

In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated. To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021. COVID-19-positive test result reported to state department of health. The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated. At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive. These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.

Value of proteinuria in assessing the severity of pre-eclampsia and its maternal and neonatal outcomes

Objective: To investigate the value of proteinuria in evaluating the severity of pre-eclampsia (PE) and assessing the maternal and neonatal outcomes of PE. Methods: The clinical records of 265 pregnant women who were diagnosed with PE at Peking Union Medical College Hospital from January 2011 to June 2021 were retrospectively analyzed. According to 24-hour urine protein (24-hUPro) results, pregnant women were divided into two groups: the non-proteinuric group (24-hUPro<0.3 g, n=10) and proteinuric group (24-hUPro≥0.3 g, n=255). The proteinuric group was further divided into 3 subgroups based on proteinuria levels: mild group (0.3 g≤24-hUPro<2.0 g, n=119), moderate group (2.0 g≤24-hUPro<5.0 g, n=59), and severe group (24-hUPro≥5.0 g, n=77). The demographic and clinical data, laboratory indicators, pregnancy complications, maternal and neonatal outcomes were compared between different groups. Results: In proteinuric subgroups, increased proteinuria was associated with earlier onset gestations, higher incidence of headache, peripheral tissue edema, serosal effusion, intrauterine growth restriction, and abnormal umbilical cord blood flow (all P<0.05). There were no significant differences in the incidence of placental abruption, eclampsia and maternal mortality among the three subgroups, but there were significant differences in the incidence of neonatal birth weight and multiple neonatal complications (all P<0.05). Compared with the proteinuric group, the non-proteinuric group showed later onset gestation (median:34.7 vs 37.6 weeks) and gestational age of delivery (median:36.0 vs 38.4 weeks), lower proportion of ocular vascular lesions [56.7% (135/238) vs 2/9], higher birth weight (median: 2 325 vs 2 750 g), and lower rate of neonatal intensive care unit occupancy [54.3%(127/234) vs 1/10;all P<0.05]. Conclusions: The proteinuria plays an important role in assessing the severity of PE and maternal and neonatal outcomes, but it is not the only indicator. The non-proteinuric PE pregnant women might still lead to severe maternal and neonatal outcomes.

Clinical features and outcomes of leucine-rich glioma-inactivated protein 1 and contactin protein-like 2 antibody-associated autoimmune encephalitis in a Chinese cohort

The present study aimed to investigate the clinical manifestations, epidemiological characteristics, and outcomes of Chinese patients with voltage-gated potassium channel complex (VGKC) antibody–associated encephalitis. Patients diagnosed with VGKC antibody-associated encephalitis at our institution between January 2016 and December 2020 were included in this study. We retrospectively evaluated their clinical features, auxiliary examination results, treatments details, long-term outcomes, and risk factors for poor outcome. Of the 91 included patients, 61 (67.78%) were men and 30 (32.97%) were women. The most common clinical symptoms were seizures (n = 63, 69.23%), memory deficits (n = 62, 68.13%), mental behavioral disorders (n = 29, 31.87%), and hyponatremia (n = 57, 62.64%). Although patients with anti- leucine-rich glioma-inactivated 1 (LGI1) (n = 76) and anti- contactin-associated protein-like 2 (CASPR2) encephalitis (n = 15) had similar clinical manifestations, the former were more diverse. In total, 86 (94.51%) patients were treated with immunotherapy. Over a median follow-up period of 25 months, there were no mortalities and 14 (15.38%) patients experienced a relapse. Univariate analysis indicated differences in sex, modified Rankin Scale scores at onset, movement disorders, central hypoventilation, and intensive care unit occupancy between the good- and poor- outcome groups. Patients with anti-LGI1 and anti-CASPR2 encephalitis showed similar clinical manifestations while presenting delineating characteristics. Those with VGKC antibody-associated diseases generally responded well to immunotherapy and demonstrated favorable clinical outcomes. Several factors affected the prognosis, and a long-term follow-up examination is necessary.

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities.

In non-pharmaceutical management of COVID-19, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients and demonstrate this approach for the United States city of Chicago. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model using daily COVID-19 ICU and hospital census data between March and August 2020. We projected various possible ICU occupancy trajectories from September 2020 to May 2021 with two possible levels of transmission increase and uncertainty in core model parameters. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

Lessons from COVID-19 syndromic surveillance through emergency department activity: a prospective time series study from western Switzerland

ObjectiveWe aimed to assess if emergency department (ED) syndromic surveillance during the first and second waves of the COVID-19 outbreak could have improved our surveillance system.Design and settingsWe did an...

Blood Urea Nitrogen as a Prognostic Marker in Severe Acute Pancreatitis.

Objectives To explore independent risk factors with good and early predictive power for SAP severity and prognosis. Methods Patients with SAP were enrolled at Central South University Xiangya Hospital between April 2017 and May 2021 and used as the training cohort. From June 2021 to February 2022, all patients with SAP were defined as external patients for validation. Patients were grouped by survival status at a 30-day posthospital admission and then compared in terms of basic information and laboratory tests to screen the independent risk factors. Results A total of 249 patients with SAP were enrolled in the training cohort. The all-cause mortality rate at a 30-day postadmission was 25.8% (51/198). Blood urea nitrogen (BUN) levels were significantly higher in the mortality group (20.45 [interquartile range (IQR), 19.7] mmol/L) than in the survival group (6.685 [IQR, 6.3] mmol/L; P < 0.001). After propensity score matching (PSM), the BUN level was still higher in the mortality group than in the survival group (18.415 [IQR, 19.555] mmol/L vs. 10.63 [IQR, 6.03] mmol/L; P = 0.005). The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of BUN was 0.820 (95% confidence interval, 0.721–0.870; P < 0.001). The optimal BUN level cut-off for predicting a 30-day all-cause mortality was 10.745 mmol/L. Moreover, patients with SAP were grouped according to BUN levels and stratified according to optimal cut-off value. Patients with high BNU levels were associated with significantly higher rates of invasive mechanical ventilation (before PSM: 61.8% vs. 20.6%, P < 0.001; after PSM: 71.1% vs. 32%, P = 0.048) and a 30-day all-cause mortality (before PSM: 44.9% vs. 6.9%, P < 0.001; after PSM: 60% vs. 34.5%, P = 0.032) than those with low BNU levels before or after PSM. The effectiveness of BUN as a prognostic marker was further validated using ROC curves for the external validation set (n = 49). The AUC of BUN was 0.803 (95% CI, 0.655–0.950; P = 0.011). It showed a good ability to predict a 30-day all-cause mortality in patients with SAP. We also observed similar results regarding disease severity, including the Acute Physiology and Chronic Health Evaluation II score (before PSM: 16 [IQR, 8] vs. 8 [IQR, 6], P < 0.001; after PSM: 18 [IQR, 10] vs. 12 [IQR, 7], P < 0.001), SOFA score (before PSM: 7 [IQR, 5] vs. 3 [IQR, 3], P < 0.001; after PSM: 8 [IQR, 5] vs. 5 [IQR, 3.5], P < 0.001), and mMarshall score (before PSM: 4 [IQR, 3] vs. 3 [IQR, 1], P < 0.001; after PSM: 5 [IQR, 2.5] vs. 3 [IQR, 1], P < 0.001). There was significant increase in intensive care unit occupancy in the high BUN level group before PSM (93.3% vs. 73.1%, P < 0.001), but not after PSM (97.8% vs. 86.2%, P = 0.074). Conclusions Our results showed that BUN levels within 24 h after hospital admission were independent risk factors for a 30-day all-cause death in patients with SAP.

Forecasting models to guide intensive care COVID-19 capacities in Germany

HintergrundZeitdynamische Prognosemodelle spielen eine zentrale Rolle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten im Pandemiegeschehen. Ein wichtiger Vorhersagewert (Prädiktor) für die zukünftige intensivmedizinische (ITS-)COVID-19-Bettenbelegungen ist die Anzahl der SARS-CoV-2-Neuinfektionen in der Bevölkerung, die wiederum stark von Schwankungen im Wochenverlauf, Meldeverzug, regionalen Unterschieden, Dunkelziffer, zeitabhängiger Ansteckungsrate, Impfungen, SARS-CoV-2-Virusvarianten sowie von nichtpharmazeutischen Eindämmungsmaßnahmen abhängt. Darüber hinaus wird die aktuelle und auch zukünftige COVID-ITS-Belegung maßgeblich von den intensivmedizinischen Entlassungs- und Sterberaten beeinflusst.MethodeSowohl die Anzahl der SARS-CoV-2-Neuinfektionen in der Bevölkerung als auch die intensivmedizinischen COVID-19-Bettenbelegungen werden bundesweit flächendeckend erfasst. Diese Daten werden tagesaktuell mit epidemischen SEIR-Modellen aus gewöhnlichen Differenzialgleichungen und multiplen Regressionsmodellen statistisch analysiert.ErgebnisseDie Prognoseergebnisse der unmittelbaren Entwicklung (20-Tage-Vorhersage) der ITS-Belegung durch COVID-19-Patienten*innen werden Entscheidungsträgern auf verschiedenen überregionalen Ebenen zur Verfügung gestellt.SchlussfolgerungDie Prognosen werden der Entwicklung von betreibbaren intensivmedizinischen Bettenkapazitäten gegenübergestellt, um frühzeitig Kapazitätsengpässe zu erkennen und kurzfristig reaktive Handlungssteuerungen, wie etwa überregionale Verlegungen, zu ermöglichen.

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics.

Anticipating intensive care unit (ICU) occupancy is critical in supporting decision makers to impose (or relax) measures that mitigate COVID-19 transmission. Mechanistic approaches such as Susceptible-Infected-Recovered (SIR) models have traditionally been used to achieve this objective. However, formulating such models is challenged by the necessity to formulate equations for plausible causal mechanisms between the intensity of COVID-19 transmission and external epidemic drivers such as temperature, and the stringency of non-pharmaceutical interventions. Here, we combined a neural network model (NN) with a Susceptible-Exposed-Infected-Recovered model (SEIR) in a hybrid model and attempted to increase the prediction accuracy of existing models used to forecast ICU occupancy. Between 1st of October, 2020 - 1st of July, 2021, the hybrid model improved performances of the SEIR model at different geographical levels. At a national level, the hybrid model improved, prediction accuracy (i.e., mean absolute error) by 74%. At the cantonal and hospital levels, the reduction on the forecast’s mean absolute error were 46% and 50%, respectively. Our findings illustrate those predictions from hybrid model can be used to anticipate occupancy in ICU, and support the decision-making for lifesaving actions such as the transfer of patients and dispatching of medical personnel and ventilators.

Intensive care unit occupancy predictions in the COVID-19 pandemic based on age-structured modelling and differential flatness

The COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. In many countries, hospitalization and in particular ICU occupancy is the primary measure for policy makers to decide on possible non-pharmaceutical interventions. In this paper a combined methodology for the prediction of COVID-19 case numbers, case-specific hospitalization and ICU admission rates as well as hospital and ICU occupancies is proposed. To this end, we employ differential flatness to provide estimates of the states of an epidemiological compartmental model and estimates of the unknown exogenous inputs driving its nonlinear dynamics. A main advantage of this method is that it requires the reported infection cases as the only data source. As vaccination rates and case-specific ICU rates are both strongly age-dependent, specifically an age-structured compartmental model is proposed to estimate and predict the spread of the epidemic across different age groups. By utilizing these predictions, case-specific hospitalization and case-specific ICU rates are subsequently estimated using deconvolution techniques. In an analysis of various countries we demonstrate how the methodology is able to produce real-time state estimates and hospital/ICU occupancy predictions for several weeks thus providing a sound basis for policy makers.

The effect of the COVID-19 pandemic on the epidemiology of positive blood cultures in Swiss intensive care units: a nationwide surveillance study

BackgroundEvidence about the impact of the pandemic of COVID-19 on the incidence rates of blood cultures contaminations and bloodstream infections in intensive care units (ICUs) remains scant. The objective of this study was to investigate the nationwide epidemiology of positive blood cultures drawn in ICUs during the first two pandemic waves of COVID-19 in Switzerland.MethodsWe analyzed data on positive blood cultures among ICU patients, prospectively collected through a nationwide surveillance system (ANRESIS), from March 30, 2020, to May 31, 2021, a 14-month timeframe that included a first wave of COVID-19, which affected the French and Italian-speaking regions, an interim period (summer 2020) and a second wave that affected the entire country. We used the number of ICU patient-days provided by the Swiss Federal Office of Public Health as denominator to calculate incidence rates of blood culture contaminations and bloodstream infections (ICU-BSI). Incidence rate ratios comparing the interim period with the second wave were determined by segmented Poisson regression models.ResultsA total of 1099 blood culture contaminations and 1616 ICU-BSIs were identified in 52 ICUs during the study. Overall, more episodes of blood culture contaminations and ICU-BSI were observed during the pandemic waves, compared to the interim period. The proportions of blood culture contaminations and ICU-BSI were positively associated with the ICU occupancy rate, which was higher during the COVID-19 waves. During the more representative second wave (versus interim period), we observed an increased incidence of blood culture contaminations (IRR 1.57, 95% CI 1.16–2.12) and ICU-BSI (IRR 1.20, 95% CI 1.03–1.39).ConclusionsAn increase in blood culture contaminations and ICU-BSIs was observed during the second COVID-19 pandemic wave, especially in months when the ICU burden of COVID-19 patients was high.

Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation.

Background: The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly. Objective: To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases. Methods: The analysis included 334 cases from four adjacent counties south-west of Munich. From length of stay and type of ward [general ward (NOR), intensive care unit (ICU)] probabilities of case numbers within a hospital at a certain time point were derived. The epidemiological situation was simulated by the effective reproduction number R, the infection rates in mid-August 2020 in the counties, and the German hospitalization rate. Simulation results are compared with real data from 2nd and 3rd wave (September 2020-May 2021). Results: With R = 2, a hospitalization rate of 17%, mitigation measures implemented on day 9 (i.e., 7-day incidence surpassing 50/100,000), the peak occupancy was reached on day 22 (155.1 beds) for the normal ward and on day 25 (44.9 beds) for the intensive care unit. A higher R led to higher occupancy rates. Simulated number of infections and intensive care unit occupancy was concordant in validation with real data obtained from the 2nd and 3rd waves in Germany. Conclusion: Hospitals could expect a peak occupancy of normal ward and intensive care unit within ~5-11 days after infections reached their peak and critical resources could be allocated accordingly. This delay (in particular for the peak of intensive care unit occupancy) might give options for timely preparation of additional intensive care unit resources.

Assessment of a novel marker of ICU strain, the ICU Activity Index, during the COVID-19 pandemic in Victoria, Australia

Objectives: To validate a real-time Intensive Care Unit (ICU) Activity Index as a marker of ICU strain from daily data available from the Critical Health Resource Information System (CHRIS), and to investigate the association between this Index and the need to transfer critically ill patients during the coronavirus disease 2019 (COVID-19) pandemic in Victoria, Australia.Design: Retrospective observational cohort study.Setting: All 45 hospitals with an ICU in Victoria, Australia.Participants: Patients in all Victorian ICUs and all critically ill patients transferred between Victorian hospitals from 27 June to 6 September 2020.Main outcome measure: Acute interhospital transfer of one or more critically ill patients per day from one site to an ICU in another hospital.Results: 150 patients were transported over 61 days from 29 hospitals (64%). ICU Activity Index scores were higher on days when critical care transfers occurred (median, 1.0 [IQR, 0.4–1.7] v 0.6 [IQR, 0.3–1.2]; P < 0.001). Transfers were more common on days of higher ICU occupancy, higher numbers of ventilated or COVID-19 patients, and when more critical care staff were unavailable. The highest ICU Activity Index scores were observed at hospitals in north-western Melbourne, where the COVID-19 disease burden was greatest. After adjusting for confounding factors, including occupancy and lack of available ICU staff, a rising ICU Activity Index score was associated with an increased risk of a critical care transfer (odds ratio, 4.10; 95% CI, 2.34–7.18; P < 0.001).Conclusions: The ICU Activity Index appeared to be a valid marker of ICU strain during the COVID-19 pandemic. It may be useful as a real-time clinical indicator of ICU activity and predict the need for redistribution of critical ill patients.

The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study

BackgroundThe literature paints a complex picture of the association between mortality risk and ICU strain.In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain.MethodsA national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).ResultsOne hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)].ConclusionIncreasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.

Case fatality inequalities of critically ill COVID-19 patients according to patient-, hospital- and region-related factors: a French nationwide study

BackgroundThe COVID-19 sanitary crisis inflicted different challenges regarding the reorganization of the human and logistic resources, particularly in intensive care unit (ICU). Interdependence between regional pandemic burden and individual outcome remains unknown. The study aimed to assess the association between ICU bed occupancy and case fatality rate of critically ill COVID-19 patients.MethodsA cross-sectional study was performed in France, using the national hospital discharge database from March to May, 2020. All patients admitted to ICU for COVID-19 were included. Case fatality was described according to: (i) patient’s characteristics (age, sex, comorbid conditions, ICU interventions); (ii) hospital’s characteristics (baseline ICU experience assessed by the number of ICU stays in 2019, number of ICU physicians per bed), and (iii) the regional outbreak-related profiles (workload indicator based on ICU bed occupancy). The determinants of lethal outcome were identified using a logistic regression model.Results14,513 COVID-19 patients were admitted to ICU; 4256 died (29.3%), with important regional inequalities in case fatality (from 17.6 to 33.5%). Older age, multimorbidity and clinical severity were associated with higher mortality, as well as a lower baseline ICU experience of the health structure. Regions with more than 10 days with ≥ 75% of ICU occupancy by COVID-19 patients experienced an excess of mortality (up to adjusted OR = 2.2 [1.9–2.6] for region with the highest occupancy rate of ICU beds).ConclusionsThe regions with the highest burden of care in ICU were associated with up to 2.2-fold increase of death rate.

Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and factors supporting effective strategies of prevention of pandemic threats

The Coronavirus Disease 2019 (COVID-19) continues to generate a constant pandemic threat with new mutations of the viral agent (SARS-CoV-2) that create socioeconomic issues. One of the fundamental problems is the evaluation of the preparedness of countries to cope with COVID-19 pandemic crisis to detect and support factors associated with the reduction of mortality and the growth of vaccinations in society. The study here confronts this problem by developing two basic indexes, which measure the performance of countries to face pandemic threats. In particular, the Index r (as resilience) detects the countries having the best performance in the reduction of the negative impact of mortality related to COVID-19 pandemic and the Index p (as preparedness and prevention) assesses best-performer countries to support COVID-19 vaccinations in order to constrain future pandemic threats and support the recovery of socioeconomic systems. Index of resilience is a composite measure based on three indicators associated with COVID-19, given by average mortality, hospital occupancy and Intensive Care Units occupancy per 100 000 people, producing an overall score; Index of preparedness/prevention is a composite measure of two indicators related to COVID-19 vaccinations (i.e., doses of vaccines administered and total vaccinates per 100 000 people), producing also an overall score of performance. The application of these indexes on a case study of European countries, having a homogenous socioeconomic area, shows the strategic positioning of countries to cope with a major pandemic threat. Findings reveal that all countries have some weaknesses and no country has a high preparedness to cope with a major epidemic or pandemic. Moreover, results suggest that best-performer countries to cope with COVID-19 pandemic crisis have a smaller size of population and/or better public governance, associated with high expenditures in health system. These indexes can help policymakers for designing effective strategies to improve preparedness and prevention of countries to face future pandemic threats.