Intensive Care Unit Occupancy Research Articles

Intensive care unit caseload and workload and their association with outcomes in critically unwell patients: a large registry-based cohort analysis

BackgroundToo high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU).MethodsRetrospective cohort analysis of data from an Austrian ICU registry. Data on patients aged ≥ 18 years admitted to 144 Austrian ICUs between 2013 and 2022 were included. A Cox proportional hazards model with ICU mortality as the outcome of interest adjusted with patients’ respective SAPS 3, current ICU caseload (measured by ICU occupancy rates), and current ICU workload (measured by median TISS-28 per ICU) as time-dependent covariables was constructed. Subgroup analyses were performed for types of ICUs, hospital care level, and pre-COVID or intra-COVID period.Results415 584 patient admissions to 144 ICUs were analysed. Compared to ICU caseloads of 76 to 100%, there was no significant relationship between overuse of ICU capacity and risk of death [HR (95% CI) 1.06 (0.99–1.15), p = 0.110 for > 100%], but for lower utilisation [1.09 (1.02–1.16), p = 0.008 for ≤ 50% and 1.10 (1.05–1.15), p < 0.0001 for 51–75%]. Exceptions were significant associations for caseloads > 100% between 2020 and 2022 [1.18 (1.06–1.30), p = 0.001], i.e., the intra-COVID period. Compared to the reference category of median TISS-28 21–30, lower [0.88 (0.78–0.99), p = 0.049 for ≤ 20], but not higher workloads were significantly associated with risk of death. High workload may be associated with higher mortality in local hospitals [1.09 (1.01–1.19), p = 0.035 for 31–40, 1.28 (1.02–1.60), p = 0.033 for > 40].ConclusionsIn a system with comparably high intensive care resources and mandatory staffing levels, patients’ survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death.

Predictive risk factors for one-year mortality in idiopathic inflammatory myopathy patients with interstitial lung disease: A retrospective, single-center cohort study.

This study aimed to analyze the risk factors for mortality of idiopathic inflammatory myopathy (IIM) patients admitted with interstitial lung disease (ILD) to guide rapid and accurate judgment of clinical prognosis. This retrospective, single-center cohort study was conducted with 135 participants (37 males, 98 females; mean age: 54.8±11.1 years; range, 24 to 85 years) between June 1, 2016, and June 30, 2021. The participants were categorized into the survival group (n=111) and nonsurvivors (n=24) according to whether they survived during the one-year follow-up. The independent risk factors for mortality in one year after discharge were analyzed. Receiver operating characteristic curve analysis was used to determine the accuracy of oxygenation index at baseline combined with pulmonary infection (PI) at follow-up to indicate death in IIM-ILD patients. Compared to the survival group, nonsurvivors were older (p=0.006) and had a higher proportion of anti-MDA5 (melanoma differentiation-associated protein 5) positivity (p<0.001). The ILD duration was shorter (p=0.006), the oxygenation index was lower (p<0.001), and the intensive care unit occupancy rate (p<0.001) and ventilator utilization rate (p<0.001) were elevated in nonsurvivors compared to the survival group. Oxygenation index at baseline (odds ratio [OR]=1.021, 95% confidence interval [CI]: 1.001-1.023, p=0.040) and PI (clinical judgment) at follow-up (OR=16.471, 95% CI: 1.565-173.365, p=0.020) were found as independent risk factors for death in the year after discharge in IIM inpatients with ILD. An oxygenation index ≤279 mmHg at baseline combined with PI at follow-up exhibited a promising predictive value for all-cause death in IIM-ILD patients within one year. Oxygenation index at baseline and PI during follow-up were independent risk factors for death of IIM-ILD patients within one year after discharge. Patients with an oxygenation index ≤279 mmHg at baseline had an increased risk of death once they developed PI during the one-year follow-up.

Factors Influencing Morbidity and Mortality Rates in Tertiary Intensive Care Units in Turkey: A Retrospective Cross-Sectional Study.

The objective of this study was to determine the correlation between the prognosis of patients admitted to a tertiary intensive care unit (ICU) and the admitted patient population, intensive care conditions, and the workload of intensive care staff. This was a retrospective cross-sectional study that analyzed data from all tertiary ICUs (a minimum of 40 and a maximum of 59 units per month) of eight training and research hospitals between January 2022 and May 2023. We compared monthly data across hospitals and analyzed factors associated with patient prognosis, including mortality and pressure injuries (PIs). This study analyzed data from 54,312 patients, of whom 51% were male and 58.8% were aged 65 or older. The median age was 69 years. The average number of tertiary ICU beds per unit was 15 ± 6 beds, and the average occupancy rate was 83.57 ± 19.28%. On average, 7 ± 9 pressure injuries (PI) and 10 ± 7 patient deaths per unit per month were reported. The mortality rate (18.66%) determined per unit was similar to the expected rate (15-25%) according to the Acute Physiology and Chronic Health Evaluation (APACHE) II score. There was a statistically significant difference among hospitals on a monthly basis across various aspects, including bed occupancy rate, length of stay (LOS), number of patients per ICU bed, number of patients per nurse in a shift, rate of patients developing PI, hospitalization rate from the emergency department, hospitalization rate from wards, hospitalization rate from the external center, referral rate, and mortality rate (p < 0.05). Although generally reliable in predicting prognosis in tertiary ICUs, the APACHE II scoring system may have limitations when analyzed on a unit-specific basis. ICU-related conditions have an impact on patient prognosis. ICU occupancy rate, work intensity, patient population, and number of working nurses are important factors associated with ICU mortality. In particular, data on the patient population admitted to the unit (emergency patients and patients with a history of malignancy) were most strongly associated with unit mortality.

Establishment of sex-specific predictive models for critical illness in Chinese people with the Omicron variant.

The Omicron variant has rapidly spread throughout the world compared to the Delta variant and poses a great threat to global healthcare systems due to its immune evasion and rapid spread. Sex has been identified as a factor significantly associated with COVID-19 mortality, but it remains unclear which clinical indicators could be identified as risk factors in each sex group and which sex-specific risk factors might shape the worse clinical outcome, especially for Omicrons. This study aimed to confirm the relationship between sex and the progression of the Omicron variant and to explore its sex-biased risk factors. We conducted a retrospective study including 1,132 hospitalized patients with the COVID-19 Omicron variant from 5 December 2022 to 25 January 2023 at Shanghai General Hospital, and the medical history data and clinical index data of the inpatients for possible sex differences were compared and analyzed. Then, a sex-specific Lasso regression was performed to select the variables significantly associated with critical illness, including intensive care unit admission, invasive mechanical ventilation, or death. A logistic regression was used to construct a sex-specific predictive model distinctively for the critical illness outcome using selected covariates. Among the collected 115 clinical indicators, up to 72 showed significant sex differences, including the difference in merit and the proportion of people with abnormalities. More importantly, males had greater critical illness (28.4% vs. 19.9%) and a significantly higher intensive care unit occupancy (20.96% vs. 14.49%) and mortality (13.2% vs. 4.9%), and males over 80 showed worse outcomes than females. Predictive models (AUC: 0.861 for males and 0.898 for females) showed 12 risk factors for males and 10 for females. Through a comprehensive sex-stratified analysis of a large cohort of hospitalized Omicron-infected patients, we identified the specific risk factors for critical illness by developing prediction models. Sex disparities and the identified risk factors should be considered, especially in the personalized prevention and treatment of the COVID-19 Omicron variant.

Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe.

Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic. Pearson correlation coefficients and time-lagged analysis were used to examine the relationship between NPIs, vaccinations and seasonality (using the average incidence of endemic human beta-coronaviruses in Sweden over a 10-year period as a proxy) and the progression of the COVID-19 pandemic as tracked by deaths; cases; hospitalisations; intensive care unit occupancy and testing positivity rates in six Northern European countries (population 99.12 million) using a population-based, observational, ecological study method. The waves of the pandemic correlated well with the seasonality of human beta-coronaviruses (HCoV-OC43 and HCoV-HKU1). In contrast, we could not find clear or consistent evidence that the stringency of NPIs or vaccination reduced the progression of the pandemic. However, these results are correlations and not causations. We hypothesise that the apparent influence of NPIs and vaccines might instead be an effect of coronavirus seasonality. We suggest that policymakers consider these results when assessing policy options for future pandemics. The study is limited to six temperate Northern European countries with spatial and temporal variations in metrics used to track the progression of the COVID-19 pandemic. Caution should be exercised when extrapolating these findings.

COVID-19 Admission Rates and Changes in US Hospital Inpatient and Intensive Care Unit Occupancy

The COVID-19 pandemic had unprecedented effects on hospital occupancy, with consequences for hospital operations and patient care. Previous studies of occupancy during COVID-19 have been limited to small samples of hospitals. To measure the association between COVID-19 admission rates and hospital occupancy in different US areas and at different time periods during 2020. This cross-sectional study used data from the Healthcare Cost and Utilization Project State Inpatient Databases (2019-2020) for patients in nonfederal acute care hospitals in 45 US states, including the District of Columbia. Data analysis was performed between September 1, 2022, and April 30, 2023. Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds (<1 [low], 1-4.9, 5-9.9, 10-14.9, or ≥15 [high]). The main outcomes were inpatient and intensive care unit (ICU) occupancy. We used regression analysis to estimate the average change in occupancy for each hospital-week in 2020 relative to the same hospital week in 2019. This study included 3960 hospitals and 54 355 916 admissions. Of the admissions in the 40 states used for race and ethnicity analyses, 15.7% were for Black patients, 12.9% were for Hispanic patients, 62.5% were for White patients, and 7.2% were for patients of other race or ethnicity; 1.7% of patients were missing these data. Weekly COVID-19 admission rates in 2020 were less than 4 per 100 beds for 63.9% of hospital-weeks and at least 10 in only 15.9% of hospital-weeks. Inpatient occupancy decreased by 12.7% (95% CI, 12.1% to 13.4%) during weeks with low COVID-19 admission rates and increased by 7.9% (95% CI, 6.8% to 9.0%) during weeks with high COVID-19 admission rates. Intensive care unit occupancy rates increased by 67.8% (95% CI, 60.5% to 75.3%) during weeks with high COVID-19 admissions. Increases in ICU occupancy were greatest when weighted to reflect the experience of Hispanic patients. Changes in occupancy were most pronounced early in the pandemic. During weeks with high COVID-19 admissions, occupancy decreased for many service lines, with occupancy by surgical patients declining by 43.1% (95% CI, 38.6% to 47.2%) early in the pandemic. In this cross-sectional study of US hospital discharges in 45 states in 2020, hospital occupancy decreased during weeks with low COVID-19 admissions and increased during weeks with high COVID-19 admissions, with the largest changes occurring early in the pandemic. These findings suggest that surges in COVID-19 strained ICUs and were associated with large decreases in the number of surgical patients. These occupancy fluctuations may have affected quality of care and hospital finances.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: “contact restriction (up to 5 people)”, “strict contact restriction”, “curfew”, “events permitted up to 100 people”, “mask requirement in shopping malls”, “restaurant closure”, “restaurants permitted only with test”, “school closure” and “general behavioral changes”. We found a considerable reduction in the instantaneous reproduction number by “general behavioral changes”, “strict contact restriction”, “restaurants permitted only with test”, “contact restriction (up to 5 people)”, “restaurant closure” and “curfew”. No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

Discharge of dependent patients directly from the intensive care units after a critical illness: a single-center retrospective cohort study.

This study aimed to determine the mortality and predictive factors affecting mortality of patients discharged directly from the intensive care unit (ICU) at the third- and sixth-month post-discharge. Additionally, it assessed the rate of hospital readmissions within 30 days post-discharge and the satisfaction level of family members with post-ICU care. In this single-center, retrospective, observational cohort study, dependent patients discharged directly from the ICU between July 1, 2019, and July 1, 2022, were included. Data on patients' demographics, reasons for admission, pre-existing diseases, length of ICU stay, advanced clinical interventions, discharge destination, physiological status on discharge, readmission within 30 days, and clinical outcomes and mortality at the third- and sixth-months post-discharge were collected. The study included 240 patients, representing 45.8% of all patients discharged directly from the ICU. Of these, 122 were discharged to their homes, and 118 to intermediate care units intermediate care units (IMCUs). The mean age was 71 years [interquartile range (IQR): 22.8]. Among the patients, 113 (47.1%) were readmitted within 30 days post-discharge, and 23 (20.35%) were re-hospitalized. The overall mortality rate in the third and sixth months was 25.4% and 32.5%, respectively. Logistic regression identified age, readmission within 30 days, and Glasgow Outcome Scale-Extended (GOS-E) at discharge as predictive factors for short-term and long-term mortality. Patients discharged to IMCUs had significantly higher long-term mortality. Family members of patients discharged to their homes had significantly higher satisfaction with post-ICU care (p=0.002). Discharging patients directly from the ICU to their homes may yield better outcomes in selected patients compared to discharging to IMCUs. This approach may also enable more efficient use of hospital resources and reduce ICU occupancy and associated costs.

Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic.

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.

Triage of Critically Ill Patients: Characteristics and Outcomes of Patients Refused as Too Well for Intensive Care.

The appropriate selection of patients for the intensive care unit (ICU) is a concern in acute care settings. However, the description of patients deemed too well for the ICU has been rarely reported. We conducted a single-centre retrospective observational study of all patients either deemed "too well" for or admitted to the ICU during one year. Refused patients were screened for unexpected events within 7 days, defined as either ICU admission without another indication, or death without treatment limitations. Patients' characteristics and organisational factors were analysed according to refusal status, outcome and delay in ICU admission. Among 2219 enrolled patients, the refusal rate was 10.4%. Refusal was associated with diagnostic groups, treatment limitations, patients' location on a ward, night time and ICU occupancy. Unexpected events occurred in 16 (6.9%) refused patients. A worse outcome was associated with time spent in hospital before refusal, patients' location on a ward, SOFA score and physician's expertise. Delayed ICU admissions were associated with ICU and hospital length of stay. ICU triage selected safely most patients who would have probably not benefited from the ICU. We identified individual and organisational factors associated with ICU refusal, subsequent ICU admission or death.

The Association of Bedside Nurse Staffing on Patient Outcomes and Throughput in a Pediatric Cardiac Intensive Care Unit

AbstractHealth care throughput is the progression of patients from admission to discharge, limited by bed occupancy and hospital capacity. This study examines heart center throughput, cascading effects of limited beds, transfer delays, and nursing staffing on outcomes utilizing elective surgery cancellation during the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic wave. This study was a retrospective single-center study of staffing, adverse events, and transfers. The study period was January 1, 2018 to December 31, 2020 with the SARS-CoV-2 period March to May 2020. There were 2,589 patients, median age 5 months (6 days–4 years), 1,543 (60%) surgical and 1,046 (40%) medical. Mortality was 3.9% (n = 101), median stay 5 days (3–11 days), median 1:1 nurse staffing 40% (33–48%), median occupancy 54% (43–65%) for step-down unit, and 81% (74–85%) for cardiac intensive care unit. Every 10% increase in step-down unit occupancy had a 0.5-day increase in cardiac intensive care unit stay (p = 0.044), 2.1% increase in 2-day readmission (p = 0.023), and 2.6% mortality increase (p &lt; 0.001). Every 10% increase in cardiac intensive care unit occupancy had 3.4% increase in surgical delay (p = 0.016), 6.5% increase in transfer delay (p = 0.020), and a 15% increase in total reported adverse events (p &lt; 0.01). Elective surgery cancellation is associated with reduced high occupancy days (23–10%, p &lt; 0.001), increased 1:1 nursing (34–55%, p &lt; 0.001), decreased transfer delays (19–4%, p = 0.008), and decreased mortality (3.7–1.5%, p = 0.044). In conclusion, Elective surgery cancellation was associated with increased 1:1 nursing and decreased mortality. Increased cardiac step-down unit occupancy was associated with longer cardiac intensive care unit stay, increased transfer, and surgical delays.

Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing.

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.

Comparison of maternal-fetal outcomes between laparoscopic cervicoisthmic cerclage and McDonald cerclage in women with cervical insufficiency

Objective: To compare the maternal and fetal outcomes of women with cervical insufficiency (CI) undergoing McDonald cerclage (MC) and laparoscopic cervicoisthmic cerclage (LCC), so as to provide evidence for the selection of cerclage methods. Methods: A retrospective trial was carried out in the First Affiliated Hospital of Sun Yat-sen University from January 2010 to December 2020. A total of 221 women who underwent the prophylactic cerclage were divided into MC group (n=54), LCC with MC history group (n=28) and LCC without MC history group (n=129) by the mode of operation and whether the pregnant women who underwent LCC had MC history. General clinical data, pregnancy complications and pregnancy outcomes were compared between the three groups. Results: (1) General clinical data: the proportion of women accepted cervical cerclage during pregnancy in MC group, LCC with MC history group and LCC without MC history group were 100.0% (54/54), 7.1% (2/28) and 27.1% (35/129), respectively (P<0.001). The indications of the three groups showed statistical significance (P=0.003), and the main indication was the history of abortion in the second and third trimester [75.9% (41/54) vs 89.3% (25/28) vs 84.5% (109/129)]. (2) Pregnancy complications: the incidence of abnormal fetal position [7.8% (4/51) vs 17.4% (4/23) vs 19.8% (24/121)], placenta accrete [5.9% (3/51) vs 13.0% (3/23) vs 11.6% (14/121)], uterine rupture [0 vs 4.3% (1/23) vs 5.8% (7/121)] in the MC group were all lower than those in LCC with MC history and LCC without MC history groups. However, there were no statistical significances (all P>0.05). Intrauterine inflammation or chorioamnionitis [15.7% (8/51) vs 0 vs 0.8% (1/121)] and premature rupture of membrane [23.5% (12/51) vs 4.3% (1/23) vs 0] were both significantly higher in MC group than those in LCC with MC history and LCC without MC history groups (all P<0.001). (3) Pregnancy outcomes: the cesarean section rate was significantly lower in MC group (41.2%, 21/51) than that in LCC with MC history group (100.0%, 23/23) and LCC without MC history group (100.0%, 121/121; P<0.001). MC group was associated with lower expenditure than LCC with MC history and LCC without MC history groups (12 169 vs 26 438 vs 27 783 yuan, P<0.001). The success rates of live birth cerclage did not differ significantly in MC (94.4%, 51/54), LCC with MC history (82.1%, 23/28) and LCC without MC history (93.8%, 121/129) groups (χ2=5.649, P=0.059). There was no significant difference in neonatal intensive care unit occupancy, neonatal birth weight and neonatal asphyxia between the three groups (all P>0.05). Conclusions: Both LCC and MC are the treatment choice for women with CI, which may get similar liver birth. However, MC has the advantages of low cesarean section rate, economical and easy operation. Therefore, MC is recommended as the first choice for CI patients, and LCC is for women with failed MC.

A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19

ObjectiveThis study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methodsThe estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. ResultsThe tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. DiscussionIn the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. ConclusionsOur easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Modeling the impact of the Omicron infection wave in Germany.

In November 2021, the first infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) B.1.1.529 ('Omicron') was reported in Germany, alongside global reports of reduced vaccine efficacy (VE) against infections with this variant. The potential threat posed by its rapid spread in Germany was, at the time, difficult to predict. We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered infectious-disease model that included information about variant-specific and waning VEs based on empirical data available at the time. Compared to other approaches, our method aimed for minimal structural and computational complexity and therefore enabled us to respond to changes in the situation in a more agile manner while still being able to analyze the potential influence of (non-)pharmaceutical interventions (NPIs) on the emerging crisis. Thus, the model allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs), the efficacy of contact reduction strategies, and the success of the booster vaccine rollout campaign. We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity, nevertheless, even without additional NPIs. The projected figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid-February and mid-March. Most surprisingly, our model showed that early, strict, and short contact reductions could have led to a strong 'rebound' effect with high incidences after the end of the respective NPIs, despite a potentially successful booster campaign. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

Neonatal intensive care unit occupancy rate and probability of discharge of very preterm infants.

To assess the association of NICU occupancy with probability of discharge and length of stay (LOS) among infants born <33 weeks gestational age (GA). Retrospective study of 3388 infants born 23-32 weeks GA, admitted to five Level 3/4 NICUs (2014-2018) and discharged alive. Standardized ratios of observed-to-expected number of discharges were calculated for each quintile of unit occupancy. Multivariable linear regression models were used to assess the association between occupancy and LOS. At the lowest unit occupancy quintiles (Q1 and Q2), infants were 12% and 11% less likely to be discharged compared to the expected number. At the highest unit occupancy quintile (Q5), infants were 20% more likely to be discharged. Highest occupancy (Q5) was also associated with a 4.7-day (95% CI 1.7, 7.7) reduction in LOS compared Q1. NICU occupancy was associated with likelihood of discharge and LOS among infants born <33 weeks GA.

723: BEDSIDE NURSE STAFFING ON PATIENT OUTCOMES AND THROUGHPUT IN A PEDIATRIC CARDIAC INTENSIVE CARE UNIT

Introduction: Healthcare throughput is the progression of patients from admission to discharge, limited by bed occupancy and hospital capacity. This study examines Heart Center throughput, cascading effects of limited beds, transfer delays, and nursing assignments on outcomes utilizing elective surgery cancellation during the initial COVID-19 pandemic wave. Methods: Nursing assignments, patient data, and transfers were collected. Elective surgery cancellation was March-May 2020. Heart Center occupancy (Stepdown Unit and Cardiac Intensive Care Unit), transfer delays, and patient outcomes were analyzed controlling for patient factors, surgical risk, staffing, and time effects. Setting: Retrospective single-center study Patients: Heart Center admissions January 1, 2018 – December 31, 2020. Results: There were 2,589 patients, median age 5 months (6 days-4 years), 1,543 (60%) surgical, 1,046 (40%) medical. Mortality was 3.9% (n=101), median stay 5 days (3-11 days), median 1:1 nursing assignments 40% (33%-48%), median occupancy 54% (43%-65%) for Stepdown Unit and 81% (74%-85%) for Cardiac Intensive Care Unit. Every 10% increase in Stepdown Unit occupancy had a 0.5-day increase in Cardiac Intensive Care Unit stay (p=0.044), 2.1% increase 2-day readmission (p=0.023), and 2.6% mortality increase (p< 0.001). Every 10% increase in Cardiac Intensive Care Unit occupancy had 3.4% increase in surgical delay (p=0.016) and 6.5% increase in transfer delay (p=0.020). Elective surgery cancellation reduced high occupancy days (23% to 10%, p< 0.001), increased 1:1 nursing (34% to 55%, p< 0.001), decreased transfer delays (19% to 4%, p=0.008), and decreased mortality (3.7% to 1.5%, p=0.044). Conclusions: Cancelation of elective surgery was associated with increased 1:1 nursing assignments and decreased mortality. Increased Cardiac Stepdown Unit occupancy resulted in longer Cardiac Intensive Care Unit stay, and increased Cardiac Intensive Care Unit occupancy increased transfer and surgical delays. Additional studies are need to understand the interaction of staffing and outcomes.

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.

Impact of sarcopenia on the surgical outcomes in patients with hepatolithiasis: A multicenter 10-year experience

BackgroundThe impact of sarcopenia on the surgical outcomes of hepatectomy for hepatolithiasis has not been investigated. The present study elucidated the effect of sarcopenia on short-term outcomes after hemihepatectomy for hepatolithiasis and investigated the benefit of different surgical approaches to hepatectomy in patients with sarcopenia. MethodsPatients who underwent hemihepatectomy for hepatolithiasis at Fujian Provincial Hospital and 5 other medical centers from 2010 to 2020 were enrolled. The sarcopenic obesity subgroup had sarcopenia coexisting with obesity, and the sarcopenic nonobesity subgroup had sarcopenia without obesity. We analyzed the postoperative outcomes of the sarcopenia group, sarcopenic obesity subgroup and sarcopenic nonobesity subgroup and the corresponding benefits of different surgical approaches. ResultsPatients with sarcopenia (n = 481) had worse surgical outcomes than nonsarcopenia, such as longer postoperative hospital duration of stay, longer time to oral intake, longer time to bowel movement, and longer time to off-bed activities. In postoperative short-term outcomes, we also found that sarcopenia had higher rates of major complications, bile leakage, and intensive care unit occupancy than the nonsarcopenic group. Subgroup analysis showed that sarcopenic obesity subgroup (n = 182) had the worst results in intraoperative outcomes and postoperative short-term outcomes. Multivariate analysis identified sarcopenic obesity as a significant risk factor for postoperative hospital duration of stay (hazard ratio = 2.994, P < .001). Furthermore, the sarcopenic obesity and sarcopenic nonobesity (n = 299) subgroups benefited from laparoscopic surgery compared with open surgery, including postoperative recovery and major complications (all P < .05). However, sarcopenic nonobesity subgroup had more significant benefits of laparoscopy than the sarcopenic obesity subgroup. The learning curve for laparoscopic hemihepatectomy for the sarcopenic obesity subgroup had a plateau, and the surgical outcomes of the sarcopenic obesity subgroup were closer to the sarcopenic nonobesity subgroup after the plateau. ConclusionSarcopenia is associated with more adverse events after hepatectomy and patients with sarcopenic obesity have a higher incidence of adverse events. Patients with sarcopenia could benefit from laparoscopy. Compared with the sarcopenic obesity patients, the sarcopenic nonobesity patients benefited more from laparoscopy. Although the sarcopenic obesity patients had more complications and slower postoperative recovery than the sarcopenic nonobesity patients, laparoscopic also could improve their short-term outcomes, but a longer learning curve was required.

Dissemination of fast-track concepts in Germany

HintergrundFast-Track-Konzepte haben die perioperative Betreuung von Patienten in den letzten 30 Jahren grundlegend verändert. Evidenz für diese Konzepte ist ausreichend vorhanden.Ziel der ArbeitZiel dieses Artikels war es, die Verbreitung von Fast-Track-Konzepten in Deutschland anhand von Routinedaten zu evaluieren.Material und MethodenEs wurde eine retrospektive Analyse aller kolorektalen Resektionen der Helios-Kliniken der Jahre 2016 bis 2021 durchgeführt. Diese wurden bezüglich Krankenhausverweildauer, Intensivbelegung, Beatmungsanteil, Mortalität, Komplikationsrate und Zugangsweg analysiert.Ergebnisse und DiskussionEine intensivmedizinische Belegung nach kolorektalen Resektionen ist in Deutschland weiterhin die Regel. Insbesondere bei onkologischen kolorektalen Resektionen werden nach wie vor zwei Drittel der Patienten perioperativ intensivmedizinisch betreut. Die stationäre Liegedauer bei kolorektalen Resektionen ist im internationalen Vergleich weiter überdurchschnittlich lang und auch über die letzten Jahre nur langsam rückläufig (z. B. Kolonkarzinom 2016: 18,6 ± 11,9 Tage/2021: 13,8 ± 9,3). Der Anteil offen-chirurgischer kolorektaler Eingriffe ist rückläufig, macht aber auch 30 Jahre nach Einführung der Laparoskopie einen relevanten Anteil aus (z. B. Kolonkarzinom 2016: 71,10 %/2021: 56,44 %). Die Versorgung von Patienten mit kolorektalen Resektionen scheint demnach weiterhin nicht nach Fast-Track-Prinzipen umgesetzt zu sein.