Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

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Abstract

Purpose

To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients.

Methods

Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020.

Results

4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%–50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors.

Conclusions

ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06705-1.

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Most cited references 31

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Kidney disease is associated with in-hospital death of patients with COVID-19.

Yichun Cheng, Ran Luo, Kun Wang … (2020)

In December 2019, a coronavirus 2019 (COVID-19) disease outbreak occurred in Wuhan, Hubei Province, China, and rapidly spread to other areas worldwide. Although diffuse alveolar damage and acute respiratory failure were the main features, the involvement of other organs needs to be explored. Since information on kidney disease in patients with COVID-19 is limited, we determined the prevalence of acute kidney injury (AKI) in patients with COVID-19. Further, we evaluated the association between markers of abnormal kidney function and death in patients with COVID-19. This was a prospective cohort study of 701 patients with COVID-19 admitted in a tertiary teaching hospital that also encompassed three affiliates following this major outbreak in Wuhan in 2020 of whom 113 (16.1%) died in hospital. Median age of the patients was 63 years (interquartile range, 50-71), including 367 men and 334 women. On admission, 43.9% of patients had proteinuria and 26.7% had hematuria. The prevalence of elevated serum creatinine, elevated blood urea nitrogen and estimated glomerular filtration under 60 ml/min/1.73m2 were 14.4, 13.1 and 13.1%, respectively. During the study period, AKI occurred in 5.1% patients. Kaplan-Meier analysis demonstrated that patients with kidney disease had a significantly higher risk for in-hospital death. Cox proportional hazard regression confirmed that elevated baseline serum creatinine (hazard ratio: 2.10, 95% confidence interval: 1.36-3.26), elevated baseline blood urea nitrogen (3.97, 2.57-6.14), AKI stage 1 (1.90, 0.76-4.76), stage 2 (3.51, 1.49-8.26), stage 3 (4.38, 2.31-8.31), proteinuria 1+ (1.80, 0.81-4.00), 2+∼3+ (4.84, 2.00-11.70), and hematuria 1+ (2.99, 1.39-6.42), 2+∼3+ (5.56,2.58- 12.01) were independent risk factors for in-hospital death after adjusting for age, sex, disease severity, comorbidity and leukocyte count. Thus, our findings show the prevalence of kidney disease on admission and the development of AKI during hospitalization in patients with COVID-19 is high and is associated with in-hospital mortality. Hence, clinicians should increase their awareness of kidney disease in patients with severe COVID-19.

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COVID-19 pneumonia: different respiratory treatments for different phenotypes?

Luciano Gattinoni, Davide Chiumello, Pietro Caironi … (2020)

The Surviving Sepsis Campaign panel recently recommended that “mechanically ventilated patients with COVID-19 should be managed similarly to other patients with acute respiratory failure in the ICU [1].” Yet, COVID-19 pneumonia [2], despite falling in most of the circumstances under the Berlin definition of ARDS [3], is a specific disease, whose distinctive features are severe hypoxemia often associated with near normal respiratory system compliance (more than 50% of the 150 patients measured by the authors and further confirmed by several colleagues in Northern Italy). This remarkable combination is almost never seen in severe ARDS. These severely hypoxemic patients despite sharing a single etiology (SARS-CoV-2) may present quite differently from one another: normally breathing (“silent” hypoxemia) or remarkably dyspneic; quite responsive to nitric oxide or not; deeply hypocapnic or normo/hypercapnic; and either responsive to prone position or not. Therefore, the same disease actually presents itself with impressive non-uniformity. Based on detailed observation of several cases and discussions with colleagues treating these patients, we hypothesize that the different COVID-19 patterns found at presentation in the emergency department depend on the interaction between three factors: (1) the severity of the infection, the host response, physiological reserve and comorbidities; (2) the ventilatory responsiveness of the patient to hypoxemia; (3) the time elapsed between the onset of the disease and the observation in the hospital. The interaction between these factors leads to the development of a time-related disease spectrum within two primary “phenotypes”: Type L, characterized by Low elastance (i.e., high compliance), Low ventilation-to-perfusion ratio, Low lung weight and Low recruitability and Type H, characterized by High elastance, High right-to-left shunt, High lung weight and High recruitability. COVID-19 pneumonia, Type L At the beginning, COVID-19 pneumonia presents with the following characteristics: Low elastance. The nearly normal compliance indicates that the amount of gas in the lung is nearly normal [4]. Low ventilation-to-perfusion (VA/Q) ratio. Since the gas volume is nearly normal, hypoxemia may be best explained by the loss of regulation of perfusion and by loss of hypoxic vasoconstriction. Accordingly, at this stage, the pulmonary artery pressure should be near normal. Low lung weight. Only ground-glass densities are present on CT scan, primarily located subpleurally and along the lung fissures. Consequently, lung weight is only moderately increased. Low lung recruitability. The amount of non-aerated tissue is very low; consequently, the recruitability is low [5]. To conceptualize these phenomena, we hypothesize the following sequence of events: the viral infection leads to a modest local subpleural interstitial edema (ground-glass lesions) particularly located at the interfaces between lung structures with different elastic properties, where stress and strain are concentrated [6]. Vasoplegia accounts for severe hypoxemia. The normal response to hypoxemia is to increase minute ventilation, primarily by increasing the tidal volume [7] (up to 15–20 ml/kg), which is associated with a more negative intrathoracic inspiratory pressure. Undetermined factors other than hypoxemia markedly stimulate, in these patients, the respiratory drive. The near normal compliance, however, explains why some of the patients present without dyspnea as the patient inhales the volume he expects. This increase in minute ventilation leads to a decrease in PaCO2. The evolution of the disease: transitioning between phenotypes The Type L patients may remain unchanging for a period and then improve or worsen. The possible key feature which determines the evolution of the disease, other than the severity of the disease itself, is the depth of the negative intrathoracic pressure associated with the increased tidal volume in spontaneous breathing. Indeed, the combination of a negative inspiratory intrathoracic pressure and increased lung permeability due to inflammation results in interstitial lung edema. This phenomenon, initially described by Barach in [8] and Mascheroni in [9] both in an experimental setting, has been recently recognized as the leading cause of patient self-inflicted lung injury (P-SILI) [10]. Over time, the increased edema increases lung weight, superimposed pressure and dependent atelectasis. When lung edema reaches a certain magnitude, the gas volume in the lung decreases, and the tidal volumes generated for a given inspiratory pressure decrease [11]. At this stage, dyspnea develops, which in turn leads to worsening P-SILI. The transition from Type L to Type H may be due to the evolution of the COVID-19 pneumonia on one hand and the injury attributable to high-stress ventilation on the other. COVID-19 pneumonia, Type H The Type H patient: High elastance. The decrease in gas volume due to increased edema accounts for the increased lung elastance. High right-to-left shunt. This is due to the fraction of cardiac output perfusing the non-aerated tissue which develops in the dependent lung regions due to the increased edema and superimposed pressure. High lung weight. Quantitative analysis of the CT scan shows a remarkable increase in lung weight (> 1.5 kg), on the order of magnitude of severe ARDS [12]. High lung recruitability. The increased amount of non-aerated tissue is associated, as in severe ARDS, with increased recruitability [5]. The Type H pattern, 20–30% of patients in our series, fully fits the severe ARDS criteria: hypoxemia, bilateral infiltrates, decreased the respiratory system compliance, increased lung weight and potential for recruitment. Figure 1 summarizes the time course we described. In panel a, we show the CT in spontaneous breathing of a Type L patient at admission, and in panel b, its transition in Type H after 7 days of noninvasive support. As shown, a similar degree of hypoxemia was associated with different patterns in lung imaging. Fig. 1 a CT scan acquired during spontaneous breathing. The cumulative distribution of the CT number is shifted to the left (well-aerated compartments), being the 0 to − 100 HU compartment, the non-aerated tissue virtually 0. Indeed, the total lung tissue weight was 1108 g, 7.8% of which was not aerated and the gas volume was 4228 ml. Patient receiving oxygen with venturi mask inspired oxygen fraction of 0.8. b CT acquired during mechanical ventilation at end-expiratory pressure at 5 cmH2O of PEEP. The cumulative distribution of the CT scan is shifted to the right (non-aerated compartments), while the left compartments are greatly reduced. Indeed, the total lung tissue weight was 2744 g, 54% of which was not aerated and the gas volume was 1360 ml. The patient was ventilated in volume controlled mode, 7.8 ml/kg of tidal volume, respiratory rate of 20 breaths per minute, inspired oxygen fraction of 0.7 Respiratory treatment Given this conceptual model, it follows that the respiratory treatment offered to Type L and Type H patients must be different. The proposed treatment is consistent with what observed in COVID-19, even though the overwhelming number of patients seen in this pandemic may limit its wide applicability. The first step to reverse hypoxemia is through an increase in FiO2 to which the Type L patient responds well, particularly if not yet breathless. In Type L patients with dyspnea, several noninvasive options are available: high-flow nasal cannula (HFNC), continuous positive airway pressure (CPAP) or noninvasive ventilation (NIV). At this stage, the measurement (or the estimation) of the inspiratory esophageal pressure swings is crucial [13]. In the absence of the esophageal manometry, surrogate measures of work of breathing, such as the swings of central venous pressure [14] or clinical detection of excessive inspiratory effort, should be assessed. In intubated patients, the P0.1 and P occlusion should also be determined. High PEEP, in some patients, may decrease the pleural pressure swings and stop the vicious cycle that exacerbates lung injury. However, high PEEP in patients with normal compliance may have detrimental effects on hemodynamics. In any case, noninvasive options are questionable, as they may be associated with high failure rates and delayed intubation, in a disease which typically lasts several weeks. The magnitude of inspiratory pleural pressures swings may determine the transition from the Type L to the Type H phenotype. As esophageal pressure swings increase from 5 to 10 cmH2O—which are generally well tolerated—to above 15 cmH2O, the risk of lung injury increases and therefore intubation should be performed as soon as possible. Once intubated and deeply sedated, the Type L patients, if hypercapnic, can be ventilated with volumes greater than 6 ml/kg (up to 8–9 ml/kg), as the high compliance results in tolerable strain without the risk of VILI. Prone positioning should be used only as a rescue maneuver, as the lung conditions are “too good” for the prone position effectiveness, which is based on improved stress and strain redistribution. The PEEP should be reduced to 8–10 cmH2O, given that the recruitability is low and the risk of hemodynamic failure increases at higher levels. An early intubation may avert the transition to Type H phenotype. Type H patients should be treated as severe ARDS, including higher PEEP, if compatible with hemodynamics, prone positioning and extracorporeal support. In conclusion, Type L and Type H patients are best identified by CT scan and are affected by different pathophysiological mechanisms. If not available, signs which are implicit in Type L and Type H definition could be used as surrogates: respiratory system elastance and recruitability. Understanding the correct pathophysiology is crucial to establishing the basis for appropriate treatment.

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Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China

Hua Su, Ming Ming Yang, Cheng Wan … (2020)

Although the respiratory and immune systems are the major targets of Coronavirus Disease 2019 (COVID-19), acute kidney injury and proteinuria have also been observed. Currently, detailed pathologic examination of kidney damage in critically ill patients with COVID-19 has been lacking. To help define this we analyzed kidney abnormalities in 26 autopsies of patients with COVID-19 by light microscopy, ultrastructural observation and immunostaining. Patients were on average 69 years (19 male and 7 female) with respiratory failure associated with multiple organ dysfunction syndrome as the cause of death. Nine of the 26 showed clinical signs of kidney injury that included increased serum creatinine and/or new-onset proteinuria. By light microscopy, diffuse proximal tubule injury with the loss of brush border, non-isometric vacuolar degeneration, and even frank necrosis was observed. Occasional hemosiderin granules and pigmented casts were identified. There were prominent erythrocyte aggregates obstructing the lumen of capillaries without platelet or fibrinoid material. Evidence of vasculitis, interstitial inflammation or hemorrhage was absent. Electron microscopic examination showed clusters of coronavirus particles with distinctive spikes in the tubular epithelium and podocytes. Furthermore, the receptor of SARS-CoV-2, ACE2 was found to be upregulated in patients with COVID-19, and immunostaining with SARS-CoV nucleoprotein antibody was positive in tubules. In addition to the direct virulence of SARS-CoV-2, factors contributing to acute kidney injury included systemic hypoxia, abnormal coagulation, and possible drug or hyperventilation-relevant rhabdomyolysis. Thus, our studies provide direct evidence of the invasion of SARSCoV-2 into kidney tissue. These findings will greatly add to the current understanding of SARS-CoV-2 infection.

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Author and article information

Contributors

Jan J. De Waele:

ORCID: http://orcid.org/0000-0003-1017-9748

Jan.DeWaele@UGent.be

Journal

Journal ID (nlm-ta): Intensive Care Med

Journal ID (iso-abbrev): Intensive Care Med

Title: Intensive Care Medicine

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0342-4642

ISSN (Electronic): 1432-1238

Publication date (Electronic): 21 May 2022

Publication date PMC-release: 21 May 2022

Pages: 1-16

Affiliations

[1 ]GRID grid.452490.e, Department of Biomedical Sciences, , Humanitas University, ; Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy

[2 ]GRID grid.417728.f, ISNI 0000 0004 1756 8807, IRCCS Humanitas Research Hospital, ; Via Manzoni 56, Rozzano, 20089 Milan, Italy

[3 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Department of Internal Medicine and Pediatrics, Faculty of Medicine and Health Sciences, , Ghent University, ; Ghent, Belgium

[4 ]GRID grid.410566.0, ISNI 0000 0004 0626 3303, Department of Intensive Care Medicine, , Ghent University Hospital, ; Ghent, Belgium

[5 ]GRID grid.5335.0, ISNI 0000000121885934, Cambridge Centre for Artificial Intelligence in Medicine, , University of Cambridge, ; Cambridge, UK

[6 ]GRID grid.120073.7, ISNI 0000 0004 0622 5016, University of Cambridge Division of Anaesthesia, Addenbrooke’s Hospital, ; Hills Road, Cambridge, UK

[7 ]GRID grid.8142.f, ISNI 0000 0001 0941 3192, Dipartimento di Scienze dell’Emergenza, Anestesiologiche e della Rianimazione, , Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, ; Rome, Italy

[8 ]GRID grid.8142.f, ISNI 0000 0001 0941 3192, Istituto di Anestesiologia e Rianimazione, , Università Cattolica del Sacro Cuore, ; Rome, Italy

[9 ]GRID grid.508487.6, ISNI 0000 0004 7885 7602, Médecine Intensive et Réanimation, APHP, Saint-Louis Hospital, , Paris University, ; Paris, France

[10 ]GRID grid.508487.6, ISNI 0000 0004 7885 7602, Université de Paris, ; Paris, France

[11 ]GRID grid.7563.7, ISNI 0000 0001 2174 1754, School of Medicine and Surgery, , University of Milano-Bicocca, ; Milan, Italy

[12 ]Department Neuroscience, Neurointensive Care, ASST-Monza, Monza, Italy

[13 ]GRID grid.5335.0, ISNI 0000000121885934, Division of Anaesthesia, Department of Medicine, , University of Cambridge, ; Cambridge, UK

[14 ]GRID grid.5335.0, ISNI 0000000121885934, Division of Immunology, Department of Pathology, , University of Cambridge, ; Cambridge, UK

[15 ]GRID grid.120073.7, ISNI 0000 0004 0622 5016, JVF Intensive Care Unit, , Addenbrookes Hospital, ; Cambridge, UK

[16 ]GRID grid.4491.8, ISNI 0000 0004 1937 116X, Department of Anaesthesia and Intensive Care, Third Faculty of Medicine, , Charles University, ; Prague, Czech Republic

[17 ]GRID grid.412819.7, ISNI 0000 0004 0611 1895, FNKV University Hospital in Prague, ; Prague, Czech Republic

[18 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Department of Intensive Care Medicine, Laboratory of Critical Care Computational Intelligence, , Amsterdam UMC, Vrije Universiteit, ; Amsterdam, The Netherlands

[19 ]GRID grid.415593.f, ISNI 0000 0004 0470 7791, General Intensive Care Unit of the Shaare Zedek Medical Center, ; Jerusalem, Israel

[20 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, Faculty of Medicine, , Hebrew University, ; Jerusalem, Israel

[21 ]GRID grid.5475.3, ISNI 0000 0004 0407 4824, Department of Critical Care, Royal Surrey Hospital and Faculty of Experimental Medicine, , University of Surrey, ; Guildford, UK

[22 ]GRID grid.470634.2, Intensive Care Unit, , Hospital General Universitario de Castellón, ; Castellón de la Plana, Spain

[23 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), UMC, , Location VUmc, VU Amsterdam, ; Amsterdam, The Netherlands

[24 ]GRID grid.414818.0, ISNI 0000 0004 1757 8749, Department of Anesthesia, Intensive Care and Emergency, , Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, ; Milan, Italy

[25 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, Department of Pathophysiology and Transplantation, , University of Milan, ; Milan, Italy

[26 ]Pirogov National Medical and Surgical Center, Moscow, 105203 Russian Federation

[27 ]GRID grid.5335.0, ISNI 0000000121885934, Division of Anaesthesia, , University of Cambridge Department of Medicine, ; Cambridge, UK

[28 ]GRID grid.24029.3d, ISNI 0000 0004 0383 8386, Neurosciences and Trauma Critical Care Unit, , Cambridge University Hospitals, ; Cambridge, UK

[29 ]GRID grid.498239.d, Cancer Research UK-Cambridge Institute, ; Cambridge, UK

[30 ]GRID grid.5477.1, ISNI 0000000120346234, Department of Intensive Care Medicine, , University Medical Center Utrecht, Utrecht University, ; Utrecht, The Netherlands

[31 ]GRID grid.24029.3d, ISNI 0000 0004 0383 8386, Neurosciences and Trauma Critical Care Unit (NCCU), , Anaesthesia Medical Examiner and Clinical Lead Organ Donation-Cambridge University Hospitals NHS Foundation Trust, ; Cambridge, UK

[32 ]GRID grid.488600.2, ISNI 0000 0004 1777 7270, Intensive Care Unit, , Hospital Universitario de Torrejón, ; Madrid, Spain

[33 ]GRID grid.449795.2, ISNI 0000 0001 2193 453X, Universidad Francisco de Vitoria, ; Madrid, Spain

[34 ]GRID grid.12477.37, ISNI 0000000121073784, School of Sports and Health Sciences, , University of Brighton, ; Brighton, UK

[35 ]GRID grid.450257.1, ISNI 0000 0004 1775 9822, Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, , Homi Bhabha National Institute, ; Mumbai, India

[36 ]GRID grid.420545.2, ISNI 0000 0004 0489 3985, Department of Critical Care, King’s College London, , Guy’s & St Thomas’ Hospital, ; London, UK

[37 ]Intensive Care Unit, AnOpIVA, Akademiska sjukhuset, Uppsala, Sweden

[38 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Hedenstierna Laboratory, Department of Surgical Science, , Uppsala University, ; Uppsala, Sweden

[39 ]GRID grid.10772.33, ISNI 0000000121511713, CHRC, CEDOC, NOVA Medical School, , New University of Lisbon, ; Lisbon, Portugal

[40 ]GRID grid.414462.1, ISNI 0000 0001 1009 677X, Polyvalent Intensive Care Unit, , Hospital de São Francisco Xavier, CHLO, ; Lisbon, Portugal

[41 ]GRID grid.7143.1, ISNI 0000 0004 0512 5013, Center for Clinical Epidemiology and Research Unit of Clinical Epidemiology, , OUH Odense University Hospital, ; Odense, Denmark

[42 ]GRID grid.7468.d, ISNI 0000 0001 2248 7639, Department of Anesthesiology and Operative Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, , Humboldt-Universität zu Berlin, and Berlin Institute of Health, ; Berlin, Germany

[43 ]GRID grid.6936.a, ISNI 0000000123222966, School of Medicine, Klinikum rechts der Isar, Department of Anesthesiology and Intensive Care, , Technical University of Munich, ; Munich, Germany

[44 ]GRID grid.413784.d, ISNI 0000 0001 2181 7253, Service de Médecine Intensive-Réanimation, , Hôpital Bicêtre, AP-HP Université Paris-Saclay, Inserm UMR S_999, ; Le Kremlin-Bicêtre, France

[45 ]GRID grid.46699.34, ISNI 0000 0004 0391 9020, Department of Critical Care, , King’s College Hospital, ; London, UK

Author information

Massimiliano Greco http://orcid.org/0000-0003-1003-4637

Thomas De Corte http://orcid.org/0000-0001-5011-6640

Ari Ercole http://orcid.org/0000-0001-8350-8093

Massimo Antonelli http://orcid.org/0000-0003-3007-1670

Elie Azoulay http://orcid.org/0000-0002-8162-1508

Giuseppe Citerio http://orcid.org/0000-0002-5374-3161

Andy Conway Morris http://orcid.org/0000-0002-3211-3216

Gennaro De Pascale http://orcid.org/0000-0002-8255-0676

Frantisek Duska http://orcid.org/0000-0003-1559-4078

Paul Elbers http://orcid.org/0000-0003-0447-6893

Sharon Einav http://orcid.org/0000-0001-8963-9633

Lui Forni http://orcid.org/0000-0002-0617-5309

Laura Galarza http://orcid.org/0000-0002-4658-748X

Armand R. J. Girbes http://orcid.org/0000-0002-0711-0494

Giacomo Grasselli http://orcid.org/0000-0002-1735-1400

Vitaly Gusarov http://orcid.org/0000-0002-2900-1459

Alasdair Jubb http://orcid.org/0000-0001-5593-866X

Jozef Kesecioglu http://orcid.org/0000-0002-3007-8445

Andrea Lavinio http://orcid.org/0000-0002-8832-918X

Maria Cruz Martin Delgado http://orcid.org/0000-0002-7468-4594

Johannes Mellinghoff http://orcid.org/0000-0002-5455-8953

Sheila Nainan Myatra http://orcid.org/0000-0001-6761-163X

Marlies Ostermann http://orcid.org/0000-0001-9500-9080

Mariangela Pellegrini http://orcid.org/0000-0001-5668-7399

Pedro Povoa http://orcid.org/0000-0002-7069-7304

Stefan J. Schaller http://orcid.org/0000-0002-6683-9584

Jean-Louis Teboul http://orcid.org/0000-0002-5748-7820

Adrian Wong http://orcid.org/0000-0003-4968-7328

Jan J. De Waele http://orcid.org/0000-0003-1017-9748

Maurizio Cecconi http://orcid.org/0000-0002-4376-6538

Article

Publisher ID: 6705

DOI: 10.1007/s00134-022-06705-1

PMC ID: 9123859

PubMed ID: 35596752

SO-VID: 50c6e41b-d641-45aa-a0b2-6070c29d2a51

License:

Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

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Supplementary Information

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Novel Coronavirus Disease COVID-19

Most cited references 31

Kidney disease is associated with in-hospital death of patients with COVID-19.

COVID-19 pneumonia: different respiratory treatments for different phenotypes?

Renal histopathological analysis of 26 postmortem findings of patients with COVID-19 in China

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