Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1364-8535
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study ; volume:23 ; number:1 ; day:12 ; month:12 ; year:2019 ; pages:1-9 ; date:12.2019
Critical care ; 23, Heft 1 (12.12.2019), 1-9, 12.2019
- Urheber
-
Klein Klouwenberg, Peter M. C.
Spitoni, Cristian
van der Poll, Tom
Bonten, Marc J.
Cremer, Olaf L.
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s13054-019-2687-z
- URN
-
urn:nbn:de:101:1-2020050806571743526331
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 11:04 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Klein Klouwenberg, Peter M. C.
- Spitoni, Cristian
- van der Poll, Tom
- Bonten, Marc J.
- Cremer, Olaf L.
- SpringerLink (Online service)
Ähnliche Objekte (12)
Correction to: Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study
Novel plasma protein biomarkers from critically ill sepsis patients
Robustness of sepsis-3 criteria in critically ill patients
Vitamin A deficiency in critically ill children with sepsis
Clinical sepsis phenotypes in critically ill COVID-19 patients
The critically ill older patient with sepsis: a narrative review
Hypothermia predicts mortality in critically ill elderly patients with sepsis
Factors predicting prolonged empirical antifungal treatment in critically ill patients
Machine-learning model for predicting oliguria in critically ill patients
Transcranial Doppler to assess sepsis-associated encephalopathy in critically ill patients
Comparison of 6-month outcomes of sepsis versus non-sepsis critically ill patients receiving mechanical ventilation
Identification of sepsis subtypes in critically ill adults using gene expression profiling
Correction to: Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study
Novel plasma protein biomarkers from critically ill sepsis patients
Robustness of sepsis-3 criteria in critically ill patients
Vitamin A deficiency in critically ill children with sepsis
Clinical sepsis phenotypes in critically ill COVID-19 patients
The critically ill older patient with sepsis: a narrative review
Hypothermia predicts mortality in critically ill elderly patients with sepsis
Factors predicting prolonged empirical antifungal treatment in critically ill patients
Machine-learning model for predicting oliguria in critically ill patients
Transcranial Doppler to assess sepsis-associated encephalopathy in critically ill patients
Comparison of 6-month outcomes of sepsis versus non-sepsis critically ill patients receiving mechanical ventilation
Identification of sepsis subtypes in critically ill adults using gene expression profiling
Correction to: Predicting the clinical trajectory in critically ill patients with sepsis: a cohort study
Novel plasma protein biomarkers from critically ill sepsis patients
Robustness of sepsis-3 criteria in critically ill patients
Vitamin A deficiency in critically ill children with sepsis
Clinical sepsis phenotypes in critically ill COVID-19 patients
The critically ill older patient with sepsis: a narrative review
Hypothermia predicts mortality in critically ill elderly patients with sepsis
Factors predicting prolonged empirical antifungal treatment in critically ill patients
Machine-learning model for predicting oliguria in critically ill patients
Transcranial Doppler to assess sepsis-associated encephalopathy in critically ill patients
Comparison of 6-month outcomes of sepsis versus non-sepsis critically ill patients receiving mechanical ventilation