Comparative incidence of early and late bloodstream and respiratory tract co-infection in patients admitted to ICU with COVID-19 pneumonia versus Influenza A or B pneumonia versus no viral pneumonia: wales multicentre ICU cohort study.

OBJECTIVE: The aim is to characterise early and late respiratory and bloodstream co-infection in patients admitted to intensive care units (ICUs) with SARS-CoV-2-related acute hypoxemic respiratory failure (AHRF) needing respiratory support in seven ICUs within Wales, during the first wave of the COVID-19 pandemic. We compare the rate of positivity of different secondary pathogens and their antimicrobial sensitivity in three different patient groups: patients admitted to ICU with COVID-19 pneumonia, Influenza A or B pneumonia, and patients without viral pneumonia. DESIGN: Multicentre, retrospective, observational cohort study with rapid microbiology data from Public Health Wales, sharing of clinical and demographic data from seven participating ICUs. SETTING: Seven Welsh ICUs participated between 10 March and 31 July 2020. Clinical and demographic data for COVID-19 disease were shared by each participating centres, and microbiology data were extracted from a data repository within Public Health Wales. Comparative data were taken from a cohort of patients without viral pneumonia admitted to ICU during the same period as the COVID-19 cohort (referred to as no viral pneumonia or 'no viral' group), and to a retrospective non-matched cohort of consecutive patients with Influenza A or B admitted to ICUs from 20 November 2017. The comparative data for Influenza pneumonia and no viral pneumonia were taken from one of the seven participating ICUs. PARTICIPANTS: A total of 299 consecutive patients admitted to ICUs with COVID-19 pneumonia were compared with 173 and 48 patients admitted with no viral pneumonia or Influenza A or B pneumonia, respectively. MAIN OUTCOME MEASURES: Primary outcome was to calculate comparative incidence of early and late co-infection in patients admitted to ICU with COVID-19, Influenza A or B pneumonia and no viral pneumonia. Secondary outcome was to calculate the individual group of early and late co-infection rate on a per-patient and per-sample basis, with their antimicrobial susceptibility and thirdly to ascertain any statistical correlation between clinical and demographic variables with rate of acquiring co-infection following ICU admission. RESULTS: A total of 299 adults (median age 57, M/F 2:1) were included in the COVID-19 ICU cohort. The incidence of respiratory and bloodstream co-infection was 40.5% and 15.1%, respectively. Staphylococcus aureus was the predominant bacterial pathogen within the first 48 h. Gram-negative organisms from Enterobacterales group were predominantly seen after 48 h in COVID-19 cohort. Comparative no viral pneumonia cohort had lower rates of respiratory tract infection and bloodstream infection. The influenza cohort had similar rates respiratory tract infection and bloodstream infection. Mortality in all three groups was similar, and no clinical or demographic variables were found to increase the rate of co-infection and ICU mortality. CONCLUSIONS: Higher incidence of bacterial co-infection was found in COVID-19 cohort as compared to the no viral pneumonia cohort admitted to ICUs for respiratory support.

Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges.

OBJECTIVES: As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios. DESIGN: Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU. SETTING: Combined data from two ICUs in United Kingdom. PATIENTS: The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization. CONCLUSIONS: Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased.