Preparing for the Worst-Case Scenario in a Pandemic: Intensivists Simulate Prioritization and Triage of Scarce ICU Resources.

OBJECTIVES: Simulation and evaluation of a prioritization protocol at a German university hospital using a convergent parallel mixed methods design. DESIGN: Prospective single-center cohort study with a quantitative analysis of ICU patients and qualitative content analysis of two focus groups with intensivists. SETTING: Five ICUs of internal medicine and anesthesiology at a German university hospital. PATIENTS: Adult critically ill ICU patients ( n = 53). INTERVENTIONS: After training the attending senior ICU physicians ( n = 13) in rationing, an impending ICU congestion was simulated. All ICU patients were rated according to their likelihood to survive their acute illness (good-moderate-unfavorable). From each ICU, the two patients with the most unfavorable prognosis ( n = 10) were evaluated by five prioritization teams for triage. MEASUREMENTS AND MAIN RESULTS: Patients nominated for prioritization visit ( n = 10) had higher Sequential Organ Failure Assessment scores and already a longer stay at the hospital and on the ICU compared with the other patients. The order within this worst prognosis group was not congruent between the five teams. However, an in-hospital mortality of 80% confirmed the reasonable match with the lowest predicted probability of survival. Qualitative data highlighted the tremendous burden of triage and the need for a team-based consensus-oriented decision-making approach to ensure best possible care and to support professionals. Transparent communication within the teams, the hospital, and to the public was seen as essential for prioritization implementation. CONCLUSIONS: To mitigate potential bias and to reduce the emotional burden of triage, a consensus-oriented, interdisciplinary, and collaborative approach should be implemented. Prognostic comparative assessment by intensivists is feasible. The combination of long-term ICU stay and consistently high Sequential Organ Failure Assessment scores resulted in a greater risk for triage in patients. It remains challenging to reliably differentiate between patients with very low chances to survive and requires further conceptual and empirical research.

Dexamethasone therapy and rates of secondary pulmonary and bloodstream infections in critically ill COVID-19 patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a pandemic. Bacterial superinfections seem to be associated with higher mortality in COVID-19 patients in intensive care units (ICUs). However, details on the prevalence and species distribution of secondary infections are limited. Moreover, the increasing use of dexamethasone may pose an additional risk of superinfections. METHODS: We performed a single-center retrospective study of the clinical and microbiological characteristics of 154 COVID-19 patients admitted to the ICU between March 2020 and January 2021, focusing on bacterial infections, use of antimicrobial agents and dexamethasone therapy. RESULTS: The median age was 68 years; 67.5% of the patients were men. Critically ill COVID-19 patients were treated with dexamethasone since July 2020 (second wave), which was not common during the first wave of the pandemic. In the dexamethasone group (n=90, 58.4%), respiratory pathogens were detected more frequently, as were multidrugresistant pathogens. The number of patients with polymicrobial detection of respiratory pathogens was significantly increased (p=0.013). The most frequently detected species were Enterobacterales, Staphylococcus aureus, and Aspergillus fumigatus. The rates of bloodstream infections did not differ between the groups. The use of dexamethasone in ICU COVID-19 patients was associated with higher rates of respiratory infectious complications. CONCLUSIONS: Secondary infections are present in a substantial fraction of critically ill COVID-19 patients. Respiratory pathogens were detectable in the majority of COVID-19 ICU patients. The use of dexamethasone poses a potential risk of secondary pulmonary infections. Infectious complications in patients with dexamethasone therapy could be associated with worse outcomes.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Rapid clinical evolution for COVID-19 translates into early hospital admission and unfavourable outcome: a preliminary report.

BACKGROUND: A wide range of mortality rates has been reported in COVID-19 patients on the intensive care unit. We wanted to describe the clinical course and determine the mortality rate in our institution's intensive care units. METHODS: To this end, we performed a retrospective cohort study of 50 COVID-19 patients admitted to the ICU at a large German tertiary university hospital. Clinical features are reported with a focus on ICU interventions, such as mechanical ventilation, prone positioning and extracorporeal organ support. Outcome is presented using a 7-point ordinal scale on day 28 and 60 following ICU admission. RESULTS: The median age was 64 years, 78% were male. LDH and D-Dimers were elevated, and patients were low on Vitamin D. ARDS incidence was 75%, and 43/50 patients needed invasive ventilation. 22/50 patients intermittently needed prone positioning, and 7/50 required ECMO. The interval from onset of the first symptoms to admission to the hospital and to the ICU was shorter in non-survivors than in survivors. By day 60 after ICU admission, 52% of the patients had been discharged. 60-day mortality rate was 32%; 37% for ventilated patients, and 42% for those requiring both: ventilation and renal replacement therapy. CONCLUSIONS: Early deterioration might be seen as a warning signal for unfavourable outcome. Lung-protective ventilation including prone positioning remain the mainstay of the treatment.

Invasive pulmonary aspergillosis in critically ill patients with severe COVID-19 pneumonia: Results from the prospective AspCOVID-19 study.

BACKGROUND: Superinfections, including invasive pulmonary aspergillosis (IPA), are well-known complications of critically ill patients with severe viral pneumonia. Aim of this study was to evaluate the incidence, risk factors and outcome of IPA in critically ill patients with severe COVID-19 pneumonia. METHODS: We prospectively screened 32 critically ill patients with severe COVID-19 pneumonia for a time period of 28 days using a standardized study protocol for oberservation of developement of COVID-19 associated invasive pulmonary aspergillosis (CAPA). We collected laboratory, microbiological, virological and clinical parameters at defined timepoints in combination with galactomannan-antigen-detection from nondirected bronchial lavage (NBL). We used logistic regression analyses to assess if COVID-19 was independently associated with IPA and compared it with matched controls. FINDINGS: CAPA was diagnosed at a median of 4 days after ICU admission in 11/32 (34%) of critically ill patients with severe COVID-19 pneumonia as compared to 8% in the control cohort. In the COVID-19 cohort, mean age, APACHE II score and ICU mortality were higher in patients with CAPA than in patients without CAPA (36% versus 9.5%; p<0.001). ICU stay (21 versus 17 days; p = 0.340) and days of mechanical ventilation (20 versus 15 days; p = 0.570) were not different between both groups. In regression analysis COVID-19 and APACHE II score were independently associated with IPA. INTERPRETATION: CAPA is highly prevalent and associated with a high mortality rate. COVID-19 is independently associated with invasive pulmonary aspergillosis. A standardized screening and diagnostic approach as presented in our study can help to identify affected patients at an early stage.

Rates of bacterial co-infections and antimicrobial use in COVID-19 patients: a retrospective cohort study in light of antibiotic stewardship.

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. Bacterial co-infections are associated with unfavourable outcomes in respiratory viral infections; however, microbiological and antibiotic data related to COVID-19 are sparse. Adequate use of antibiotics in line with antibiotic stewardship (ABS) principles is warranted during the pandemic. We performed a retrospective study of clinical and microbiological characteristics of 140 COVID-19 patients admitted between February and April 2020 to a German University hospital, with a focus on bacterial co-infections and antimicrobial therapy. The final date of follow-up was 6 May 2020. Clinical data of 140 COVID-19 patients were recorded: The median age was 63.5 (range 17-99) years; 64% were males. According to the implemented local ABS guidelines, the most commonly used antibiotic regimen was ampicillin/sulbactam (41.5%) with a median duration of 6 (range 1-13) days. Urinary antigen tests for Legionella pneumophila and Streptococcus peumoniae were negative in all cases. In critically ill patients admitted to intensive care units (n = 50), co-infections with Enterobacterales (34.0%) and Aspergillus fumigatus (18.0%) were detected. Blood cultures collected at admission showed a diagnostic yield of 4.2%. Bacterial and fungal co-infections are rare in COVID-19 patients and are mainly prevalent in critically ill patients. Further studies are needed to assess the impact of antimicrobial therapy on therapeutic outcome in COVID-19 patients to prevent antimicrobial overuse. ABS guidelines could help in optimising the management of COVID-19. Investigation of microbial patterns of infectious complications in critically ill COVID-19 patients is also required.

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.