Intensive Care Unit Bed Occupancy Research Articles

Exploring Antibacterial Usage and Pathogen Surveillance over Five Years in a Tertiary Referral Teaching Hospital Adult General Intensive Care Unit (ICU).

Antimicrobial resistance is a globally recognised health emergency. Intensive care is an area with significant antimicrobial consumption, particularly increased utilisation of broad-spectrum antibacterials, making stewardship programmes essential. We aimed to explore antibacterial consumption, partnered with pathogen surveillance, over a five-year period (2018 to 2023) in a tertiary referral adult general intensive care unit (ICU). The mean number of admissions was 1645 per annum. A comparison between the ICU populations admitted before and after the COVID-19 pandemic peak (2020/21) identified several notable differences with increased average daily unit bed occupancy (21.6 vs. 25.2, respectively) and a higher proportion of admissions with sepsis (28.4% vs. 32.5%, respectively) in the post-pandemic period. Over the entire five years, the overall proportion of antibacterial use by the WHO AWaRe classification was 42.6% access, 54.7% watch and 2.6% reserve. One hundred and forty-seven positive blood culture isolates were reported, with the most concerning antibacterial resistance identified in 7.5% (9 Escherichia coli and 2 Klebsiella pneumoniae isolates). The COVID-19 pandemic peak year was associated with increased ICU bed occupancy, as well as a greater number of positive blood cultures but lower antibacterial consumption. Despite an increasingly complex workload, a large proportion of overall antibacterial consumption remained within the access category. However, the mortality rate and the incidence of most concerning antimicrobial resistance with respect to pathogens remained satisfyingly consistent, suggesting the positive consequences of real-world antibiotic stewardship in an intensive care setting.

Limitation of life-sustaining therapies in critically ill patients with COVID-19: a descriptive epidemiological investigation from the COVID-ICU study

BackgroundLimitations of life-sustaining therapies (LST) practices are frequent and vary among intensive care units (ICUs). However, scarce data were available during the COVID-19 pandemic when ICUs were under intense pressure. We aimed to investigate the prevalence, cumulative incidence, timing, modalities, and factors associated with LST decisions in critically ill COVID-19 patients.MethodsWe did an ancillary analysis of the European multicentre COVID-ICU study, which collected data from 163 ICUs in France, Belgium and Switzerland. ICU load, a parameter reflecting stress on ICU capacities, was calculated at the patient level using daily ICU bed occupancy data from official country epidemiological reports. Mixed effects logistic regression was used to assess the association of variables with LST limitation decisions.ResultsAmong 4671 severe COVID-19 patients admitted from February 25 to May 4, 2020, the prevalence of in-ICU LST limitations was 14.5%, with a nearly six-fold variability between centres. Overall 28-day cumulative incidence of LST limitations was 12.4%, which occurred at a median of 8 days (3–21). Median ICU load at the patient level was 126%. Age, clinical frailty scale score, and respiratory severity were associated with LST limitations, while ICU load was not. In-ICU death occurred in 74% and 95% of patients, respectively, after LST withholding and withdrawal, while median survival time was 3 days (1–11) after LST limitations.ConclusionsIn this study, LST limitations frequently preceded death, with a major impact on time of death. In contrast to ICU load, older age, frailty, and the severity of respiratory failure during the first 24 h were the main factors associated with decisions of LST limitations.

A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study

The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Intensive Care Unit (ICU) beds. We analyse time series of confirmed cases, hospitalised patients, ICU bed occupancy, recovered patients and deaths. We employ state-of-the-art forecasting algorithms, such as ARTXP, ARIMA, SARIMAX, and Multivariate Regression models. We combine these into three forecasting models culminating to a tri-model approach in time series analysis and compare them. The results of this study show that the combination of ARIMA with SARIMAX is more accurate for the majority of the investigated regions in short term 1-week ahead predictions, while Multivariate Regression outperforms the other two models for 2-weeks ahead predictions. Finally, for the medium term 3-weeks ahead predictions the Multivariate Regression and ARIMA with SARIMAX show the best results. We report on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE) values, for one-week, two-week and three-week ahead predictions for ICU bed requirements. Such timely insights offer new capabilities for efficient management of healthcare resources.

Evolution of the COVID-19 pandemic in Italy at the national and regional levels from February 2020 to March 2022

<abstract> <p>The COVID-19 pandemic initially hit Italy at the end of February 2020. In the following two years, there were about 13 million diagnosed cases and more than 150000 deaths. For this period, we have performed a study that evaluates and compares the impacts on public health of the SARS-CoV-2 waves at the national and regional levels. Based on contagion trends, we considered four pandemic waves. For each wave, we also analyzed a restricted time interval of one month around the peak. We found an underestimation of diagnosed cases and a saturation of available intensive care unit (ICU) beds during the first wave. The second wave had a more significant impact on public health, highlighting that the system of physical distancing measures was less effective than the lockdown implemented during the first wave. Instead, during the first three peaks, the incidence of ICU bed occupancy was superimposable. In general, odds ratios of case fatality rate showed a more significant risk for males than females from the age of thirty onwards. We verified that regional responses to the health emergency were not homogeneous. In particular, in contrast with the national temporal trend, some regions showed a significant increase in the incidence of deaths. Furthermore, we found positive correlations between availability and occupancy of hospital beds from the second wave onward. In contrast, a significant correlation was verified for ICU bed occupancy and deaths only during the second peak. In light of what happened, it would be desirable to plan an increase in public health funds for new emergencies and issue a rigorous plan to face pandemics.</p> </abstract>

Impact of the COVID-19 pandemic on critical care utilization in Japan: a nationwide inpatient database study

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has disrupted critical care services worldwide. Examining how critical care systems responded to the COVID-19 pandemic on a national level will be useful in setting future critical care plans. The present study aimed to describe the utilization of critical care services before and during the COVID-19 pandemic using a nationwide Japanese inpatient administrative database.MethodsAll patients admitted to an intensive care unit (ICU) or a high-dependency care unit (HDU) from February 9, 2019, to February 8, 2021, in the Japanese Diagnosis Procedure Combination inpatient database were included. February 9, 2020, was used as the breakpoint separating the periods before and during COVID-19 pandemic. Hospital and patient characteristics were compared before and during the COVID-19 pandemic. Change in ICU and HDU bed occupancy before and during the COVID-19 pandemic was evaluated using interrupted time-series analysis.ResultsThe number of ICU patients before and during the COVID-19 pandemic was 297,679 and 277,799, respectively, and the number of HDU patients was 408,005 and 384,647, respectively. In the participating hospitals (383 ICU-equipped hospitals and 460 HDU-equipped hospitals), the number of hospitals which increased the ICU and HDU beds capacity were 14 (3.7%) and 33 (7.2%), respectively. Patient characteristics and outcomes in ICU and HDU were similar before and during the COVID-19 pandemic except main etiology for admission of COVID-19. The mean ICU bed occupancy before and during the COVID-19 pandemic was 51.5% and 47.5%, respectively. The interrupted time-series analysis showed a downward level change in ICU bed occupancy during the COVID-19 pandemic (− 4.29%, 95% confidence intervals − 5.69 to − 2.88%), and HDU bed occupancy showed similar trends. Of 383 hospitals with ICUs, 232 (60.6%) treated COVID-19 patients in their ICUs. Their annual hospital case volume of COVID-19 ICU patients varied greatly, with a median of 10 (interquartile range 3–25, min 1, max 444).ConclusionsThe ICU and HDU bed capacity did not increase while their bed occupancy decreased during the COVID-19 pandemic in Japan. There was no change in clinicians’ decision-making to forego ICU/HDU care for selected patients, and there was no progress in the centralization of critically ill COVID-19 patients.

Quality metrics and outcomes among critically ill patients in China: results of the national clinical quality control indicators for critical care medicine survey 2015-2019.

Background:It is crucial to improve the quality of care provided to ICU patient, therefore a national survey of the medical quality of intensive care units (ICUs) was conducted to analyze adherence to quality metrics and outcomes among critically ill patients in China from 2015 to 2019.Methods:This was an ICU-level study based on a 15-indicator online survey conducted in China. Considering that ICU care quality may vary between secondary and tertiary hospitals, direct standardization was adopted to compare the rates of ICU quality indicators among provinces/regions. Multivariate analysis was performed to identify potential factors for in-hospital mortality and factors related to ventilator-associated pneumonia (VAP), catheter-related bloodstream infections (CRBSIs), and catheter-associated urinary tract infections (CAUTIs).Results:From the survey, the proportions of structural indicators were 1.83% for the number of ICU inpatients relative to the total number of inpatients, 1.44% for ICU bed occupancy relative to the total inpatient bed occupancy, and 51.08% for inpatients with Acute Physiology and Chronic Health Evaluation II scores ≥15. The proportions of procedural indicators were 74.37% and 76.60% for 3-hour and 6-hour surviving sepsis campaign bundle compliance, respectively, 62.93% for microbiology detection, 58.24% for deep vein thrombosis prophylaxis, 1.49% for unplanned endotracheal extubations, 1.99% for extubated inpatients reintubated within 48 hours, 6.38% for unplanned transfer to the ICU, and 1.20% for 48-hour ICU readmission. The proportions of outcome indicators were 1.28‰ for VAP, 3.06‰ for CRBSI, 3.65‰ for CAUTI, and 10.19% for in-hospital mortality. Although the indicators varied greatly across provinces and regions, the treatment level of ICUs in China has been stable and improved based on various quality control indicators in the past 5 years. The overall mortality rate has dropped from 10.19% to approximately 8%.Conclusions:The quality indicators of medical care in China's ICUs are heterogeneous, which is reflected in geographic disparities and grades of hospitals. This study is of great significance for improving the homogeneity of ICUs in China.

Impact of hospital strain on excess deaths during the COVID-19 pandemic—United States, july 2020–july 2021

This article describes the excess deaths observed when hospitals are strained by COVID-19 admissions, as defined by intensive care unit bed occupancy. Specifically, when intensive care unit bed occupancy reaches 75% of capacity, there are an estimated 12,000 additional excess deaths; when hospitals exceed 100% of their intensive care unit bed capacity, approximately 80,000 excess deaths are expected in the following 2 weeks nationally. This report suggests that all patient populations, including transplant candidates and recipients, will experience additional excess deaths when there is increased strain on hospitals due to COVID-19 surges.

Associations of Government-issued Intensive Care Unit Admission Criteria with Clinical Practices, Outcomes, and Intensive Care Unit Bed Occupancy.

Rationale: In Japan, the government officially issued intensive care unit (ICU) admission criteria that require ICUs to admit patients who need a certain level of monitoring and procedures to ensure their reimbursement for ICU costs from April 2014. Objectives: To assess whether the newly issued health policy on ICU admission criteria based on financial incentives for monitoring and procedures had any impact on clinical practices, outcomes, and ICU bed occupancy. Methods: Using a nationwide inpatient health claims database in Japan, we identified patients who were admitted to the ICU from April 2012 to March 2018. Outcomes were monitoring and procedures in the ICU, clinical outcomes, and ICU bed occupancy. The outcomes of monitoring and procedures and clinical outcomes were adjusted for patient characteristics. Interrupted time-series analyses were used to compare the trends in outcomes for two separate periods before and after the issue of the new health policy on ICU admission criteria in April 2014. Results: A total of 1,660,601 patients in 259 ICU-equipped hospitals were eligible. There were significant upward slope changes between the pre- and post-issue periods for all monitoring and procedures in the ICU, including invasive arterial pressure monitoring (5.62% change in trend per year; 95% confidence interval [CI], 4.75-6.49%) and central venous pressure monitoring (1.22% change in trend per year; 95% CI, 0.78-1.67%). There was no significant slope change between the pre- and post-issue periods for in-hospital mortality, but there were significant upward slope changes for complications of pneumonia (0.27% change in trend per year; 95% CI, 0.14-0.39%) and catheter-related bloodstream infection (0.02% change in trend per year; 95% CI, 0.00-0.14%). There were also significant upward slope changes in length of hospital stay, length of ICU stay, and hospitalization costs between the pre- and post-issue periods. There was no significant slope change between the pre- and post-issue periods for ICU bed occupancy. Conclusions: The health policy on ICU admission criteria based on financial incentives for actions taken by providers was associated with increased monitoring and procedures, complications, lengths of hospital and ICU stay, and hospitalization costs without decreasing ICU bed occupancy.

Smart testing and critical care bed sharing for COVID-19 control.

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.

Setting a benchmark for resource utilization and quality of care in patients undergoing transcatheter aortic valve implantation in Europe-Rationale and design of the international BENCHMARK registry.

BackgroundThe use of transcatheter aortic valve implantation (TAVI) for treating aortic stenosis (AS) has increased exponentially in recent years. Despite the availability of clinical practice guidelines for the management of valvular heart disease, disparities in quality of care (QoC) for TAVI patients remain widespread across Europe. Tailored QoC measures will help to reduce resource utilization and improve patient outcomes without compromising patient safety. Using a clear set of QoC measures, the BENCHMARK registry aims to document the progress that can be achieved if such tailored QoC measures are implemented.MethodsThe BENCHMARK registry (BENCHMARK) is a non‐interventional, multicenter registry in patients with severe symptomatic AS undergoing TAVI with a 1‐ and 12‐months follow‐up. BENCHMARK will be conducted at 30 centers across Europe and will enroll a total of 2400 consecutive TAVI patients. Patients suffering from severe symptomatic AS who undergo TAVI with a balloon‐expandable transcatheter aortic valve will be included. The registry will comprise four phases: (1) a retrospective baseline evaluation phase; (2) an education phase; (3) an implementation phase; and (4) a prospective effect documentation phase (prospective phase). The registry's primary objectives are to reduce the length of hospital stay and accelerate the post‐procedural patient recovery pathway, but without compromising safety. The study started in April 2021 and has an estimated completion date of May 2023.DiscussionBENCHMARK will establish QoC measures to reduce resource utilization, intensive care unit bed occupancy, and overall length of hospitalization with uncompromised patient safety post‐TAVI (ClinicalTrials.gov Identifier: NCT04579445).

Analysis of the Effects of COVID-19 Mask Mandates on Hospital Resource Consumption and Mortality at the County Level.

Coronavirus disease 2019 (COVID-19) threatens vulnerable patient populations, resulting in immense pressures at the local, regional, national, and international levels to contain the virus. Laboratory-based studies demonstrate that masks may offer benefit in reducing the spread of droplet-based illnesses, but few data are available to assess mask effects via executive order on a population basis. We assess the effects of a county-wide mask order on per-population mortality, intensive care unit (ICU) utilization, and ventilator utilization in Bexar County, Texas. We used publicly reported county-level data to perform a mixed-methods before-and-after analysis along with other sources of public data for analyses of covariance. We used a least-squares regression analysis to adjust for confounders. A Texas state-level mask order was issued on July 3, 2020, followed by a Bexar County-level order on July 15, 2020. We defined the control period as June 2 to July 2 and the postmask order period as July 8, 2020-August 12, 2020, with a 5-day gap to account for the median incubation period for cases; longer periods of 7 and 10 days were used for hospitalization and ICU admission/death, respectively. Data are reported on a per-100,000 population basis using respective US Census Bureau-reported populations. From June 2, 2020 through August 12, 2020, there were 40,771 reported cases of COVID-19 within Bexar County, with 470 total deaths. The average number of new cases per day within the county was 565.4 (95% confidence interval [CI] 394.6-736.2). The average number of positive hospitalized patients was 754.1 (95% CI 657.2-851.0), in the ICU was 273.1 (95% CI 238.2-308.0), and on a ventilator was 170.5 (95% CI 146.4-194.6). The average deaths per day was 6.5 (95% CI 4.4-8.6). All of the measured outcomes were higher on average in the postmask period as were covariables included in the adjusted model. When adjusting for traffic activity, total statewide caseload, public health complaints, and mean temperature, the daily caseload, hospital bed occupancy, ICU bed occupancy, ventilator occupancy, and daily mortality remained higher in the postmask period. There was no reduction in per-population daily mortality, hospital bed, ICU bed, or ventilator occupancy of COVID-19-positive patients attributable to the implementation of a mask-wearing mandate.

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.

Geographic distribution of bed occupancy during the COVID-19 epidemic in the United States: A nationwide study.

Geographic distribution of bed occupancy during the COVID-19 epidemic in the United States: A nationwide study.

COVID–19の第1波への対応についてのJAAM全国調査(JAAM nationwide survey on the response to the first wave of COVID–19 in Japan)

ABSTRACT Aim: To investigate and clarify the surge capacity of staff/equipment/space, and patient outcome in the first wave of coronavirus disease (COVID–19) in Japan. Methods: We analyzed questionnaire data from the end of May 2020 from 180 hospitals (total of 102,578 beds) with acute medical centers. Results: A total of 4,938 hospitalized patients with COVID–19 were confirmed. Of 1,100 severe COVID–19 inpatients, 112 remained hospitalized and 138 died. There were 4,852 patients presumed to be severe COVID–19 patients who were confirmed later to be not infected. Twenty–seven hospitals (15% of 180 hospitals) converted their intensive care unit (ICU) to a unit for COVID–19 patients only, and 107 (59%) had to manage both severe COVID–19 patients and others in the same ICU. Restriction of ICU admission occurred in one of the former 27 hospitals and 21 of the latter 107 hospitals. Shortage of N95 masks was the most serious concern regarding personal protective equipment. As for issues that raised ICU bed occupancy, difficulty undertaking or progressing rehabilitation for severe patients (42%), and the improved patients (28%), long–lasting severely ill patients (36%), and unclear isolation criteria (34%) were mentioned. Many acute medicine physicians assisted regional governmental agencies, functioning as advisors and volunteer coordinators. Conclusion: The mortality rate of COVID–19 in this study was 4.1% of all hospitalized patients and 12.5% (one in eight) severe patients. The hospitals with dedicated COVID–19 ICUs accepted more patients with severe COVID–19 and had lower ICU admission restrictions, which could be helpful as a strategy in the next pandemic.

Intensive Care Unit Occupancy in Japan, 2015-2018: A Nationwide Inpatient Database Study.

Detailed data on intensive care unit (ICU) occupancy in Japan are lacking. Using a nationwide inpatient database in Japan, we aimed to assess ICU bed occupancy to guide critical care utilization planning. We identified all ICU patients admitted from January 1, 2015 to December 31, 2018 to ICU-equipped hospitals participating in the Japanese Diagnosis Procedure Combination inpatient database. We assessed the trends in daily occupancy by counting the total number of occupied ICU beds on a given day divided by the total number of licensed ICU beds in the participating hospitals. We also assessed ICU occupancy for patients with mechanical ventilation, patients with extracorporeal membrane oxygenation, and patients without life-supportive therapies. Over the 4 study years, 1,379,618 ICU patients were admitted to 495 hospitals equipped with 5,341 ICU beds, accounting for 75% of all ICU beds in Japan. Mean ICU occupancy on any given day was 60%, with a range of 45.0% to 72.5%. Mean ICU occupancy did not change over the 4 years. Mean ICU occupancy was about 9% higher on weekdays than on weekends and about 5% higher in the coldest season than in the warmest season. For patients with mechanical ventilation, patients with extracorporeal membrane oxygenation, and patients without life-supportive therapies, mean ICU occupancy was 24%, 0.5%, and 30%, respectively. Only one-fourth of ICU beds were occupied by mechanically ventilated patients, suggesting that the critical care system in Japan has substantial surge capacity under normal temporal variation to care for critically ill patients.

Discrepancy between the Demand and Supply of Intensive Care Unit Beds in South Korea from 2011 to 2019: A Cross-Sectional Analysis

PurposeIntensive care unit (ICU) bed availability is key to critical patient care. In many countries, older patients generally account for a significant proportion of hospitalizations and ICU admissions. Therefore, considering the rapidly increasing aging population in South Korea, it is important to establish whether the demand for critical care is currently met by available ICU beds.Materials and MethodsWe evaluated a 9-year trend in ICU bed supply and ICU length of stay in South Korea between 2011 and 2019 in a population-based cross-sectional analysis, using data from the Korean Health Insurance Review & Assessment Service and Statistics database. We described the changes in ICU bed rates in adult (≥20 years) and older adult (≥65 years) populations. ICU length of stay was categorized similarly and was used to predict future ICU bed demands.ResultsThe ICU bed rate per 100000 adults increased from 18.5 in 2011 to 19.5 in 2019. In contrast, the ICU bed rate per 100000 older adults decreased from 127.6 in 2011 to 104.0 in 2019. ICU length of stay increased by 43.8% for adults and 55.6% for older adults. In 2019, the regional differences in the ICU bed rate nearly doubled, and the ICU length of stay increased six-fold. The ICU bed occupancy rate in South Korea is expected to rise to 102.7% in 2030.ConclusionThe discrepancy between the demand and supply of ICU beds in South Korea requires urgent action to anticipate future ICU demands.

Outcomes for patients with COVID-19 admitted to Australian intensive care units during the first four months of the pandemic.

To describe the characteristics and outcomes of patients with COVID-19 admitted to intensive care units (ICUs) during the initial months of the pandemic in Australia. Prospective, observational cohort study in 77 ICUs across Australia. Patients admitted to participating ICUs with laboratory-confirmed COVID-19 during 27 February - 30 June 2020. ICU mortality and resource use (ICU length of stay, peak bed occupancy). The median age of the 204 patients with COVID-19 admitted to intensive care was 63.5 years (IQR, 53-72 years); 140 were men (69%). The most frequent comorbid conditions were obesity (40% of patients), diabetes (28%), hypertension treated with angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (24%), and chronic cardiac disease (20%); 73 patients (36%) reported no comorbidity. The most frequent source of infection was overseas travel (114 patients, 56%). Median peak ICU bed occupancy was 14% (IQR, 9-16%). Invasive ventilation was provided for 119 patients (58%). Median length of ICU stay was greater for invasively ventilated patients than for non-ventilated patients (16 days; IQR, 9-28 days v 3 days; IQR, 2-5 days), as was ICU mortality (26 deaths, 22%; 95% CI, 15-31% v four deaths, 5%; 95% CI, 1-12%). Higher Acute Physiology and Chronic Health Evaluation II (APACHE-II) scores on ICU day 1 (adjusted hazard ratio [aHR], 1.15; 95% CI, 1.09-1.21) and chronic cardiac disease (aHR, 3.38; 95% CI, 1.46-7.83) were each associated with higher ICU mortality. Until the end of June 2020, mortality among patients with COVID-19 who required invasive ventilation in Australian ICUs was lower and their ICU stay longer than reported overseas. Our findings highlight the importance of ensuring adequate local ICU capacity, particularly as the pandemic has not yet ended.

COVID-19 cumulative incidence, intensive care, and mortality in Italian regions compared to selected European countries

BackgroundThe high contagiousness and rapid spreading of the coronavirus disease 2019 (COVID-19) has caused a high number of critical to severe life-threatening cases, which required urgent hospital admission and treatment in intensive care units (ICUs). The pandemic has been a tough test for all European national health systems and their capability to provide an adequate reaction. MethodsThe present work aims to reveal correlations between parameters such as COVID-19 incidence, ICU bed occupancy, ICU excess area, and mortality in Italian regions. Public data for the period of March 1 to July 16, 2020, were analyzed using several mathematical and statistical methods. ResultsThe analysis defined two separate groups of Italian regions. The examined variables considered within these groups were interlinked and dependent on each other. The regions of the two groups shared the same kind of fitted model (linear) explaining mortality as a function of cumulative incidence, but with higher value of the constant in one group, so characterized by a high intrinsic “strength” of the pandemic, certainly playing a major role in the generation of a large number of severe and life-threatening cases. These results are confirmed at European level. Other factors may condition mortality and be linked to incidence, such as ICU saturation and excess. ConclusionsThese quantitative results could be a very helpful tool to set up preventive measures and optimize biomedical interventions before the pandemic, in its recurrent waves, could overcome the reaction capacity of any public health system.

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

AbstractIn a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.

Projecting demand for critical care beds during COVID-19 outbreaks in Canada.

Increasing numbers of coronavirus disease 2019 (COVID-19) cases in Canada may create substantial demand for hospital admission and critical care. We evaluated the extent to which self-isolation of mildly ill people delays the peak of outbreaks and reduces the need for this care in each Canadian province. We developed a computational model and simulated scenarios for COVID-19 outbreaks within each province. Using estimates of COVID-19 characteristics, we projected the hospital and intensive care unit (ICU) bed requirements without self-isolation, assuming an average number of 2.5 secondary cases, and compared scenarios in which different proportions of mildly ill people practised self-isolation 24 hours after symptom onset. Without self-isolation, the peak of outbreaks would occur in the first half of June, and an average of 569 ICU bed days per 10 000 population would be needed. When 20% of cases practised self-isolation, the peak was delayed by 2-4 weeks, and ICU bed requirement was reduced by 23.5% compared with no self-isolation. Increasing self-isolation to 40% reduced ICU use by 53.6% and delayed the peak of infection by an additional 2-4 weeks. Assuming current ICU bed occupancy rates above 80% and self-isolation of 40%, demand would still exceed available (unoccupied) ICU bed capacity. At the peak of COVID-19 outbreaks, the need for ICU beds will exceed the total number of ICU beds even with self-isolation at 40%. Our results show the coming challenge for the health care system in Canada and the potential role of self-isolation in reducing demand for hospital-based and ICU care.