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.

Critical care staffing ratio and outcome of COVID-19 patients requiring intensive care unit admission during the first pandemic wave: a retrospective analysis across Switzerland from the RISC-19-ICU observational cohort.

STUDY AIM: The surge of admissions due to severe COVID-19 increased the patients-to-critical care staffing ratio within the ICUs. We investigated whether the daily level of staffing was associated with an increased risk of ICU mortality (primary endpoint), length of stay (LOS), mechanical ventilation and the evolution of disease (secondary endpoints). METHODS: We employed a retrospective multicentre analysis of the international Risk Stratification in COVID-19 patients in the ICU (RISC-19-ICU) registry, limited to the period between March 1 and May 31, 2020, and to Switzerland. Hierarchical regression models were used to investigate crude and adjusted effects of the critical care staffing ratio on study endpoints. We adjusted for disease severity and weekly caseload. RESULTS: Among the 38 participating Swiss ICUs, 17 recorded staffing information. The study population included 437 patients and 2,342 daily assessments of patient-to-critical care staffing ratio. Median of daily patient-to-nurse ratio started at 1.0 [IQR 0.5-1.5; calendar week 9] and peaked at 2.4 (IQR 0.4-2.0; calendar week 16), while the median of daily patient-to-physician ratio started at 4.0 (IQR 2.1-5.0; calendar week 9) and peaked at 6.8 (IQR 6.3-7.3; calendar week 19). Neither the patient-to-nurse (adjusted OR 1.28, 95% CI 0.85-1.93; doubling of ratio) nor the patient-to-physician ratio (adjusted OR 1.07, 95% CI 0.87-1.32; doubling of ratio) were associated with ICU mortality. We found no association of daily critical care staffing on the secondary endpoints in adjusted models. CONCLUSION: We found no association of reduced availability of critical care staffing resources in Swiss ICUs with overall ICU length of stay nor mortality. Whether long-term outcome of critically ill patients with COVID-19 have been affected remains to be studied.

The SARS-CoV-2 Pandemic Impacts the Management of Swiss Pediatric Intensive Care Units.

The impact of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic on pediatric intensive care units (PICUs) is difficult to quantify. We conducted an observational study in all eight Swiss PICUs between 02/24/2020 and 06/15/2020 to characterize the logistical and medical aspects of the pandemic and their impact on the management of the Swiss PICUs. The nine patients admitted to Swiss PICUs during the study period suffering from pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) and constituting 14% (9/63) of all SARS-CoV-2 positive hospitalized patients in Swiss children's hospitals caused a higher workload [total Nine Equivalents of nursing Manpower use Score (NEMS) points, p = 0.0008] and were classified to higher workload categories (p < 0.0001) than regular PICU patients (n = 4,881) admitted in 2019. The comparison of the characteristics of the eight Swiss PICUs shows that they were confronted by different organizational issues arising from temporary regulations put in place by the federal council. These general regulations had different consequences for the eight individual PICUs due to the differences between the PICUs. In addition, the temporal relationship of these different regulations influenced the available PICU resources, dependent on the characteristics of the individual PICUs. As pandemic continues, reflecting and learning from experience is essential to reduce workload, optimize bed occupancy and manage resources in each individual PICU. In a small country as Switzerland, with a relatively decentralized health care local differences between PICUs are considerable and should be taken into account when making policy decisions.

How to Synchronize Longitudinal Patient Data With the Underlying Disease Progression: A Pilot Study Using the Biomarker CRP for Timing COVID-19.

The continued digitalization of medicine has led to an increased availability of longitudinal patient data that allows the investigation of novel and known diseases in unprecedented detail. However, to accurately describe any underlying pathophysiology and allow inter-patient comparisons, individual patient trajectories have to be synchronized based on temporal markers. In this pilot study, we use longitudinal data from critically ill ICU COVID-19 patients to compare the commonly used alignment markers "onset of symptoms," "hospital admission," and "ICU admission" with a novel objective method based on the peak value of the inflammatory marker C-reactive protein (CRP). By applying our CRP-based method to align the progression of neutrophils and lymphocytes, we were able to define a pathophysiological window that improved mortality risk stratification in our COVID-19 patient cohort. Our data highlights that proper synchronization of longitudinal patient data is crucial for accurate interpatient comparisons and the definition of relevant subgroups. The use of objective temporal disease markers will facilitate both translational research efforts and multicenter trials.

Capillary Leukocytes, Microaggregates, and the Response to Hypoxemia in the Microcirculation of Coronavirus Disease 2019 Patients.

OBJECTIVES: In this study, we hypothesized that coronavirus disease 2019 patients exhibit sublingual microcirculatory alterations caused by inflammation, coagulopathy, and hypoxemia. DESIGN: Multicenter case-controlled study. SETTING: Two ICUs in The Netherlands and one in Switzerland. PATIENTS: Thirty-four critically ill coronavirus disease 2019 patients were compared with 33 healthy volunteers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The microcirculatory parameters quantified included total vessel density (mm × mm-2), functional capillary density (mm × mm-2), proportion of perfused vessels (%), capillary hematocrit (%), the ratio of capillary hematocrit to systemic hematocrit, and capillary RBC velocity (µm × s-1). The number of leukocytes in capillary-postcapillary venule units per 4-second image sequence (4 s-1) and capillary RBC microaggregates (4 s-1) was measured. In comparison with healthy volunteers, the microcirculation of coronavirus disease 2019 patients showed increases in total vessel density (22.8 ± sd 5.1 vs 19.9 ± 3.3; p < 0.0001) and functional capillary density (22.2 ± 4.8 vs 18.8 ± 3.1; p < 0.002), proportion of perfused vessel (97.6 ± 2.1 vs 94.6 ± 6.5; p < 0.01), RBC velocity (362 ± 48 vs 306 ± 53; p < 0.0001), capillary hematocrit (5.3 ± 1.3 vs 4.7 ± 0.8; p < 0.01), and capillary-hematocrit-to-systemic-hematocrit ratio (0.18 ± 0.0 vs 0.11 ± 0.0; p < 0.0001). These effects were present in coronavirus disease 2019 patients with Sequential Organ Failure Assessment scores less than 10 but not in patients with Sequential Organ Failure Assessment scores greater than or equal to 10. The numbers of leukocytes (17.6 ± 6.7 vs 5.2 ± 2.3; p < 0.0001) and RBC microaggregates (0.90 ± 1.12 vs 0.06 ± 0.24; p < 0.0001) was higher in the microcirculation of the coronavirus disease 2019 patients. Receiver-operating-characteristics analysis of the microcirculatory parameters identified the number of microcirculatory leukocytes and the capillary-hematocrit-to-systemic-hematocrit ratio as the most sensitive parameters distinguishing coronavirus disease 2019 patients from healthy volunteers. CONCLUSIONS: The response of the microcirculation to coronavirus disease 2019-induced hypoxemia seems to be to increase its oxygen-extraction capacity by increasing RBC availability. Inflammation and hypercoagulation are apparent in the microcirculation by increased numbers of leukocytes and RBC microaggregates.

Bacterial pulmonary superinfections are associated with longer duration of ventilation in critically ill COVID-19 patients.

The impact of secondary bacterial infections (superinfections) in coronavirus disease 2019 (COVID-19) is not well understood. In this prospective, monocentric cohort study, we aim to investigate the impact of superinfections in COVID-19 patients with acute respiratory distress syndrome. Patients are assessed for concomitant microbial infections by longitudinal analysis of tracheobronchial secretions, bronchoalveolar lavages, and blood cultures. In 45 critically ill patients, we identify 19 patients with superinfections (42.2%). Superinfections are detected on day 10 after intensive care admission. The proportion of participants alive and off invasive mechanical ventilation at study day 28 (ventilator-free days [VFDs] at 28 days) is substantially lower in patients with superinfection (subhazard ratio 0.37; 95% confidence interval [CI] 0.15-0.90; p = 0.028). Patients with pulmonary superinfections have a higher incidence of bacteremia, virus reactivations, yeast colonization, and required intensive care treatment for a longer time. Superinfections are frequent and associated with reduced VFDs at 28 days despite a high rate of empirical antibiotic therapy.

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

RATIONALE: The COVID-19 pandemic induces considerable strain on intensive care unit resources. OBJECTIVES: We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. METHODS: We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. MEASUREMENTS: The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. MAIN RESULTS: The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. CONCLUSION: A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.