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BackgroundReported mortality of hospitalised COVID-19 patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under optimised care conditions have not been systematically studied. MethodsThis retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced ARDS and ECMO referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers. ResultsBetween February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) extracorporeal membrane-oxygenation (ECMO) support. According to the multistate model the probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the ICU and 57% if mechanical ventilation was required at study entry. Age [≥]65 years and male sex were predictors for in-hospital death. Predominant complications - as judged by two independent reviewers - determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications. ConclusionIn a dynamic care model COVID-19-related in-hospital mortality remained substantial. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources. RegistrationGerman Clinical Trials Register (identifier DRKS00021775), retrospectively registered June 10, 2020.
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BackgroundThe pressures exerted by the pandemic of COVID-19 pose an unprecedented demand on health care services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. We here describe methods used by a university hospital to forecast caseloads and time to peak incidence. MethodsWe developed a set of models to forecast incidence among the hospital catchment population and describe the COVID-19 patient hospital care-path. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care path model according to expert opinion (static model). Once sufficient local data were available, trends for the time dependent effective reproduction number were fitted and the care-path was parameterized using hazards for real patient admission, referrals, and discharge (dynamic model). ResultsThe static model, deployed before the epidemic, exaggerated the bed occupancy (general wards 116 forecasted vs 66 observed, ICU 47 forecasted vs 34 observed) and predicted the peak too late (general ward forecast April 9, observed April 8, ICU forecast April 19, observed April 8). After April 5, the dynamic model could be run daily and precision improved with increasing availability of empirical local data. ConclusionsThe models provided data-based guidance in the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.
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BackgroundCoronavirus disease 2019 (COVID-19) patients who are admitted to intensive care units (ICU) have a high risk of requiring renal replacement therapy (RRT) due to acute kidney injury (AKI). Concomitantly, COVID-19 patients exhibit a state of hypercoagulability that can affect circuit lifespan. An optimal anticoagulation strategy is therefore needed in order to maintain circuit patency and therapeutic efficiency of RRT. MethodsRetrospective single-centre cohort study on 71 critically ill COVID-19 patients at the University of Freiburg Medical Center. Included were all patients aged 18 years and older with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that were admitted to ICU between February 26 and May 21, 2020. We collected data on the COVID-19 disease course, AKI, RRT, thromboembolic events and anticoagulation. Primary outcome of the study was the effect of different anticoagulation strategies during RRT on extracorporeal circuit lifespans. ResultsAnticoagulation during continuous veno-venous haemodialysis (CVVHD) was performed with unfractionated heparin (UFH) or citrate. Mean treatment time in the UFH group was 21.3h (SEM: {+/-}5.6h). Mean treatment time in the citrate group was 45.6h (SEM: {+/-}2.7h). Citrate anticoagulation prolonged treatment duration significantly by 24.4h (p=0.0014). Anticoagulation during sustained low-efficiency daily dialysis (SLEDD) was performed with UFH, argatroban or low molecular weight heparin (LMWH). Mean dialysis time with UFH was 8.1h (SEM: {+/-}1.3h), argatroban 8.0h (SEM: {+/-}0.9h) and LMWH 11.8h (SEM: {+/-}0.5h). Compared to UFH and argatroban, LMWH significantly prolonged treatment times by 3.7h (p=0.0082) and 3.8h (p=0.0024), respectively. ConclusionsUFH fails to prevent early clotting events in dialysis circuits. For patients, who do not require an effective systemic anticoagulation, regional citrate dialysis is the most effective strategy in our cohort. For patients, who require an effective systemic anticoagulation treatment, the usage of LMWH results in the longest circuit life spans. FundingBerta-Ottenstein-Programme for Clinician Scientists, Faculty of Medicine, University of Freiburg, Germany. Else Kroner-Fresenius-Stiftung, Bad Homburg, Germany. Deutsche Forschungsgemeinschaft, Bonn, Germany.
