icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients.

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.

Near real-time observation reveals increased prevalence of young patients in the ICU during the emerging third SARS-CoV-2 wave in Switzerland.

AIMS OF THE STUDY: During the ongoing COVID-19 pandemic, the launch of a large-scale vaccination campaign and virus mutations have hinted at possible changes in transmissibility and the virulence affecting disease progression up to critical illness, and carry potential for future vaccination failure. To monitor disease development over time with respect to critically ill COVID-19 patients, we report near real-time prospective observational data from the RISC-19-ICU registry that indicate changed characteristics of critically ill patients admitted to Swiss intensive care units (ICUs) at the onset of a third pandemic wave. METHODS: 1829 of 3344 critically ill COVID-19 patients enrolled in the international RISC-19-ICU registry as of 31 May 2021 were treated in Switzerland and were included in the present study. Of these, 1690 patients were admitted to the ICU before 1 February 2021 and were compared with 139 patients admitted during the emerging third pandemic wave RESULTS: Third wave patients were a mean of 5.2 years (95% confidence interval [CI] 3.2–7.1) younger (median 66.0 years, interquartile range [IQR] 57.0–73.0 vs 62.0 years, IQR 54.5–68.0; p <0.0001) and had a higher body mass index than patients admitted in the previous pandemic period. They presented with lower SAPS II and APACHE II scores, less need for circulatory support and lower white blood cell counts at ICU admission. P/F ratio was similar, but a 14% increase in ventilatory ratio was observed over time (p = 0.03) CONCLUSION: Near real-time registry data show that the latest COVID-19 patients admitted to ICUs in Switzerland at the onset of the third wave were on average 5 years younger, had a higher body mass index, and presented with lower physiological risk scores but a trend towards more severe lung failure. These differences may primarily be related to the ongoing nationwide vaccination campaign, but the possibility that changes in virus-host interactions may be a co-factor in the age shift and change in disease characteristics is cause for concern, and should be taken into account in the public health and vaccination strategy during the ongoing pandemic. (ClinicalTrials.gov Identifier: NCT04357275).

Timing of VV-ECMO therapy implementation influences prognosis of COVID-19 patients.

INTRODUCTION: Current knowledge on the use of extracorporeal membrane oxygenation (ECMO) in COVID-19 remains limited to small series and registry data. In the present retrospective monocentric study, we report on our experience, our basic principles, and our results in establishing and managing ECMO in critically ill COVID-19 patients. METHODS: A cohort study was conducted in patients with severe acute respiratory distress syndrome (ARDS) related to COVID-19 pneumonia admitted to the ICU of the Geneva University Hospitals and supported by VV-ECMO from March 14 to May 31. The VV-ECMO implementation criteria were defined according to an institutional algorithm validated by the local crisis unit and the Swiss Society of Intensive Care Medicine. RESULTS: Out of 137 ARDS patients admitted to our ICU, 10 patients (age 57 ± 4 years, BMI 31.5 ± 5 kg/m2 , and SAPS II score 56 ± 3) were put on VV-ECMO. The mean duration of mechanical ventilation before ECMO and mean time under ECMO were 7 ± 3 days and 19 ± 11 days, respectively. The ICU and hospital length of stay were 26 ± 11 and 35 ± 10 days, respectively. The survival rate for patients on ECMO was 40%. The comparative analysis between survivors and non-survivors highlighted that survivors had a significantly shorter mechanical ventilation duration before ECMO (4 ± 2 days vs. 9 ± 2 days, p = 0.01). All the patients who had more than 150 h of mechanical ventilation before the application of ECMO ultimately died. CONCLUSION: The present results suggest that VV-ECMO can be safely utilized in appropriately selected COVID-19 patients with refractory hypoxemia. The main information for clinicians is that late VV-ECMO therapy (i.e., beyond the seventh day of mechanical ventilation) seems futile.

Haemodynamic monitoring and management in COVID-19 intensive care patients: an International survey.

PURPOSE: To survey haemodynamic monitoring and management practices in intensive care patients with the coronavirus disease 2019 (COVID-19). METHODS: A questionnaire was shared on social networks or via email by the authors and by Anaesthesia and/or Critical Care societies from France, Switzerland, Belgium, Brazil, and Portugal. Intensivists and anaesthetists involved in COVID-19 ICU care were invited to answer 14 questions about haemodynamic monitoring and management. RESULTS: Globally, 1000 questionnaires were available for analysis. Responses came mainly from Europe (n = 460) and America (n = 434). According to a majority of respondents, COVID-19 ICU patients frequently or very frequently received continuous vasopressor support (56%) and had an echocardiography performed (54%). Echocardiography revealed a normal cardiac function, a hyperdynamic state (43%), hypovolaemia (22%), a left ventricular dysfunction (21%) and a right ventricular dilation (20%). Fluid responsiveness was frequently assessed (84%), mainly using echo (62%), and cardiac output was measured in 69%, mostly with echo as well (53%). Venous oxygen saturation was frequently measured (79%), mostly from a CVC blood sample (94%). Tissue perfusion was assessed biologically (93%) and clinically (63%). Pulmonary oedema was detected and quantified mainly using echo (67%) and chest X-ray (61%). CONCLUSION: Our survey confirms that vasopressor support is not uncommon in COVID-19 ICU patients and suggests that different haemodynamic phenotypes may be observed. Ultrasounds were used by many respondents, to assess cardiac function but also to predict fluid responsiveness and quantify pulmonary oedema. Although we observed regional differences, current international guidelines were followed by most respondents.

Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high disease burden with 10% of confirmed cases progressing towards critical illness. Nevertheless, the disease course and predictors of mortality in critically ill patients are poorly understood. METHODS: Following the critical developments in ICUs in regions experiencing early inception of the pandemic, the European-based, international RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry was created to provide near real-time assessment of patients developing critical illness due to COVID-19. FINDINGS: As of April 22, 2020, 639 critically ill patients with confirmed SARS-CoV-2 infection were included in the RISC-19-ICU registry. Of these, 398 had deceased or been discharged from the ICU. ICU-mortality was 24%, median length of stay 12 (IQR, 5-21) days. ARDS was diagnosed in 74%, with a minimum P/F-ratio of 110 (IQR, 80-148). Prone positioning, ECCO2R, or ECMO were applied in 57%. Off-label therapies were prescribed in 265 (67%) patients, and 89% of all bloodstream infections were observed in this subgroup (n = 66; RR=3·2, 95% CI [1·7-6·0]). While PCT and IL-6 levels remained similar in ICU survivors and non-survivors throughout the ICU stay (p = 0·35, 0·34), CRP, creatinine, troponin, d-dimer, lactate, neutrophil count, P/F-ratio diverged within the first seven days (p<0·01). On a multivariable Cox proportional-hazard regression model at admission, creatinine, d-dimer, lactate, potassium, P/F-ratio, alveolar-arterial gradient, and ischemic heart disease were independently associated with ICU-mortality. INTERPRETATION: The European RISC-19-ICU cohort demonstrates a moderate mortality of 24% in critically ill patients with COVID-19. Despite high ARDS severity, mechanical ventilation incidence was low and associated with more rescue therapies. In contrast to risk factors in hospitalized patients reported in other studies, the main mortality predictors in these critically ill patients were markers of oxygenation deficit, renal and microvascular dysfunction, and coagulatory activation. Elevated risk of bloodstream infections underscores the need to exercise caution with off-label therapies.