Neu: CareLit 5.0 testen

Zusammenfassung

     Keine Erkrankung hat in den letzten Jahrzehnten die Welt innerhalb kürzester Zeit so nachhaltig verändert wie COVID-19. Innerhalb weniger Wochen sahen wir uns mit einem Erreger konfrontiert, der nicht nur hochinfektiös, sondern potenziell auch mit einer hohen Mortalität vergesellschaftet war. Nahezu alle Gesundheitssysteme wurden während der 2 Jahre der Pandemie an die Grenzen ihrer Leistungsfähigkeit geführt oder überschritten diese deutlich. Abstract The COVID-19 pandemic has changed the world significantly within the last two years and has put a major burden on health care systems worldwide. Due to the imbalance between the number of patients requiring treatment and the shortage of necessary healthcare resources, a new mode of triage had to be established. The allocation of resources and definition of treatment priorities could be supported by taking the actual short-term mortality risk of patients with COVID-19 into account. We therefore analyzed the current literature for criteria to predict mortality in COVID-19. Kernaussagen Validierte Risiko-Scores können unterstützen, jene Patientengruppen mit COVID-19 zu identifizieren, welche die dringlichsten Behandlungsprioritäten aufweisen und somit die Ressourcenallokation in Zeiten der Pandemie optimieren. Höheres Alter (> 65 Jahre), männliches Geschlecht, Adipositas, Diabetes und arterielle Hypertonsie sind Risikofaktoren für eine erhöhte Mortalität im Rahmen einer COVID-19-Infektion. Erhöhte Werte von Procalcitonin sind mit einer höheren Wahrscheinlichkeit eines intensivstationspflichtigen Krankheitsverlaufs, das Vorliegen einer disseminierten intravasalen Koagulation mit einer höheren Mortalitätswahrscheinlichkeit vergesellschaftet. Das Vorliegen der Blutgruppe 0 dürfte sich auf das Erkrankungsrisiko und die Erkrankungsschwere bei COVID-19 positiv auswirken. Mittels strukturierter Untersuchungsprotokolle kann durch Lungensonografie und Computertomografie das Mortalitätsrisiko von Patienten mit COVID-19 abgeschätzt werden. Schlüsselwörter COVID-19 - Mortalität - Risikofaktoren - Triage - Prädiktion Keywords COVID-19 - mortality - risc factors - triage - prediction 29 June 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany Literatur 1 Streckbein S, Kohlmann T, Luxen J. et al. Sichtungskonzepte bei Massenanfällen von Verletzten und Erkrankten: Ein Überblick 30 Jahre nach START. Unfallchirurg 2016; 119: 620-631 DOI: 10.1007/s00113-014-2717-x. CrossrefPubMedGoogle Scholar 2 Truog RD, Mitchell C, Daley GQ. The Toughest Triage — Allocating Ventilators in a Pandemic. N Engl J Med 2020; 382: 1973-1975 DOI: 10.1056/NEJMp2005689. (PMID: 32202721) CrossrefPubMedGoogle Scholar 3 Buoro S, Di Marco F, Rizzi M. et al. Papa Giovanni XXIII Bergamo Hospital at the time of the COVID-19 outbreak: Letter from the warfront…. Int J Lab Hematol 2020; 42: 8-10 DOI: 10.1111/ijlh.13207. CrossrefPubMedGoogle Scholar 4 Marckmann G, Neitzke G, Schildmann J. et al. Entscheidungen über die Zuteilung intensivmedizinischer Ressourcen im Kontext der COVID-19-Pandemie: Klinisch-ethische Empfehlungen der DIVI, der DGINA, der DGAI, der DGIIN, der DGNI, der DGP, der DGP und der AEM. Med Klin Intensivmed Notfmed 2020; 115: 477-485 DOI: 10.1007/s00063-020-00708-w. CrossrefPubMedGoogle Scholar 5 Bundesgesundheitsministerium. Kabinett beschließt Regelungen zur Triage. Accessed November 20, 2022 at: www.bundesgesundheitsministerium.de/presse/pressemitteilungen/kabinett-beschliesst-regelungen-zur-triage.html PubMedGoogle Scholar 6 de Jong VMT, Rousset RZ, Antonio-Villa NE. et al. Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis. BMJ 2022; 378: e069881 DOI: 10.1136/bmj-2021-069881. (PMID: 35820692) CrossrefPubMedGoogle Scholar 7 Knight SR, Ho A, Pius R. et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ 2020; 370: m3339 DOI: 10.1136/bmj.m3339. CrossrefPubMedGoogle Scholar 8 Wang K, Zuo P, Liu Y. et al. Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China. Clin Infect Dis 2020; 71: 2079-2088 DOI: 10.1093/cid/ciaa538. CrossrefPubMedGoogle Scholar 9 Tai W, He L, Zhang X. et al. Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: implication for development of RBD protein as a viral attachment inhibitor and vaccine. Cell Mol Immunol 2020; 17: 613-620 DOI: 10.1038/s41423-020-0400-4. (PMID: 32203189) CrossrefPubMedGoogle Scholar 10 Jin Y, Yang H, Ji W. et al. Virology, Epidemiology, Pathogenesis, and Control of COVID-19. Viruses 2020; 12: 372 DOI: 10.3390/v12040372. (PMID: 32230900) CrossrefPubMedGoogle Scholar 11 Wallentin L, Lindbäck J, Eriksson N. et al. Angiotensin-converting enzyme 2 (ACE2) levels in relation to risk factors for COVID-19 in two large cohorts of patients with atrial fibrillation. Eur Heart J 2020; 41: 4037-4046 DOI: 10.1093/eurheartj/ehaa697. CrossrefPubMedGoogle Scholar 12 Hendren NS, de Lemos JA, Ayers C. et al. Association of Body Mass Index and Age With Morbidity and Mortality in Patients Hospitalized With COVID-19. Circulation 2021; 143: 135-144 DOI: 10.1161/CIRCULATIONAHA.120.051936. CrossrefPubMedGoogle Scholar 13 Goodman KE, Magder LS, Baghdadi JD. et al. Impact of Sex and Metabolic Comorbidities on Coronavirus Disease 2019 (COVID-19) Mortality Risk Across Age Groups: 66646 Inpatients Across 613 U.S. Hospitals. Clin Infect Dis 2021; 73: e4113-e4123 DOI: 10.1093/cid/ciaa1787. CrossrefPubMedGoogle Scholar 14 Moreira A, Chorath K, Rajasekaran K. et al. Demographic predictors of hospitalization and mortality in US children with COVID-19. Eur J Pediatr 2021; 180: 1659-1663 DOI: 10.1007/s00431-021-03955-x. CrossrefPubMedGoogle Scholar 15 Zhang J-J, Dong X, Cao Y-Y. et al. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 2020; 75: 1730-1741 DOI: 10.1111/all.14238. CrossrefPubMedGoogle Scholar 16 Tang N, Li D, Wang X. et al. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost 2020; 18: 844-847 DOI: 10.1111/jth.14768. CrossrefPubMedGoogle Scholar 17 Weber GM, Hong C, Xia Z. et al. International comparisons of laboratory values from the 4 CE collaborative to predict COVID-19 mortality. NPJ Digit Med 2022; 5: 74 DOI: 10.1038/s41746-022-00601-0. CrossrefPubMedGoogle Scholar 18 Li X, Shen C, Wang L. et al. Pulmonary fibrosis and its related factors in discharged patients with new corona virus pneumonia: a cohort study. Respir Res 2021; 22: 203 DOI: 10.1186/s12931-021-01798-6. CrossrefPubMedGoogle Scholar 19 Wu X, Liu X, Zhou Y. et al. 3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study. Lancet Respir Med 2021; 9: 747-754 DOI: 10.1016/S2213-2600(21)00174-0. (PMID: 33964245) CrossrefPubMedGoogle Scholar 20 Basheer M, Saad E, Kananeh M. et al. Cytokine Patterns in COVID-19 Patients: Which Cytokines Predict Mortality and Which Protect Against?. Curr Issues Mol Biol 2022; 44: 4735-4747 DOI: 10.3390/cimb44100323. CrossrefPubMedGoogle Scholar 21 Zhao J, Yang Y, Huang H. et al. Relationship Between the ABO Blood Group and the Coronavirus Disease 2019 (COVID-19) Susceptibility. Clin Infect Dis 2021; 73: 328-331 DOI: 10.1093/cid/ciaa1150. CrossrefPubMedGoogle Scholar 22 Efat A, Shoeib S, ElKholy A. et al. Blood phenotype O and indirect bilirubin are associated with lower, early COVID-19-related mortality: A retrospective study. Int J Immunopathol Pharmacol 2022; 36: 3946320221133952 DOI: 10.1177/03946320221133952. CrossrefPubMedGoogle Scholar 23 Khalil A, Feghali R, Hassoun M. The Lebanese COVID-19 Cohort; A Challenge for the ABO Blood Group System. Front Med (Lausanne) 2020; 7: 585341 DOI: 10.3389/fmed.2020.585341. (PMID: 33330542) CrossrefPubMedGoogle Scholar 24 Muñiz-Diaz E, Llopis J, Parra R. et al. Relationship between the ABO blood group and COVID-19 susceptibility, severity and mortality in two cohorts of patients. Blood Transfus 2021; 19: 54-63 DOI: 10.2450/2020.0256-20. CrossrefPubMedGoogle Scholar 25 Shibeeb S, Khan A. ABO blood group association and COVID-19. COVID-19 susceptibility and severity: a review. Hematol Transfus Cell Ther 2022; 44: 70-75 DOI: 10.1016/j.htct.2021.07.006. (PMID: 34541459) CrossrefPubMedGoogle Scholar 26 Takagi H. Down the Rabbit-Hole of blood groups and COVID-19. Br J Haematol 2020; 190: e268-e270 DOI: 10.1111/bjh.17059. (PMID: 32740915) CrossrefPubMedGoogle Scholar 27 Bardakci O, Daş M, Akdur G. et al. Point-of-care Lung Ultrasound, Lung CT and NEWS to Predict Adverse Outcomes and Mortality in COVID-19 Associated Pneumonia. J Intensive Care Med 2022; 37: 1614-1624 DOI: 10.1177/08850666221111731. (PMID: 36317355) CrossrefPubMedGoogle Scholar 28 Abbasi B, Akhavan R, Ghamari Khameneh A. et al. Evaluation of the relationship between inpatient COVID-19 mortality and chest CT severity score. Am J Emerg Med 2021; 45: 458-463 DOI: 10.1016/j.ajem.2020.09.056. CrossrefPubMedGoogle Scholar 29 Xu Y, Trivedi A, Becker N. et al. Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19. Sci Rep 2022; 12: 16913 DOI: 10.1038/s41598-022-20724-4. CrossrefPubMedGoogle Scholar

Vorschau