Predict Score: A New Biological and Clinical Tool to Help Predict Risk of Intensive Care Transfer for COVID-19 Patients
| Autor: | Mickael Gette, Sara Fernandes, Marion Marlinge, Marine Duranjou, Wijayanto Adi, Maelle Dambo, Pierre Simeone, Pierre Michelet, Nicolas Bruder, Regis Guieu, Julien Fromonot |
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| Přispěvatelé: | Aix-Marseille Université - École de médecine (AMU SMPM MED), Aix-Marseille Université - Faculté des sciences médicales et paramédicales (AMU SMPM), Aix Marseille Université (AMU)-Aix Marseille Université (AMU), Assistance Publique - Hôpitaux de Marseille (APHM), Centre d'études et de recherche sur les services de santé et la qualité de vie (CEReSS), Aix Marseille Université (AMU), Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Timone [CHU - APHM] (TIMONE), Lucas, Nelly |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
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Bruder QH301-705.5 W Duranjou Dambo Article M [SDV.BBM] Life Sciences [q-bio]/Biochemistry Molecular Biology score [SDV.BBM]Life Sciences [q-bio]/Biochemistry Molecular Biology Biology (General) intensive care R P biology Fernandes COVID-19 Marlinge N et al. Predict COVID-19 Guieu Michelet Gette Adi Simeone |
| Zdroj: | Biomedicines Volume 9 Issue 5 Biomedicines, MDPI, 2021, 9 (5), pp.566. ⟨10.3390/biomedicines9050566⟩ Biomedicines, 2021, 9 (5), pp.566. ⟨10.3390/biomedicines9050566⟩ Biomedicines, Vol 9, Iss 566, p 566 (2021) |
| ISSN: | 2227-9059 |
| DOI: | 10.3390/biomedicines9050566 |
| Popis: | International audience; Background: The COVID-19 crisis has strained world health care systems. This study aimed to develop an innovative prediction score using clinical and biological parameters (PREDICT score) to anticipate the need of intensive care of COVID-19 patients already hospitalized in standard medical units. Methods: PREDICT score was based on a training cohort and a validation cohort retrospectively recruited in 2020 in the Marseille University Hospital. Multivariate analyses were performed, including clinical, and biological parameters, comparing a baseline group composed of COVID-19 patients exclusively treated in standard medical units to COVID-19 patients that needed intensive care during their hospitalization. Results: Independent variables included in the PREDICT score were: age, Body Mass Index, Respiratory Rate, oxygen saturation, C-reactive protein, neutrophil–lymphocyte ratio and lactate dehydrogenase. The PREDICT score was able to correctly identify more than 83% of patients that needed intensive care after at least 1 day of standard medical hospitalization. Conclusions: The PREDICT score is a powerful tool for anticipating the intensive care need for COVID-19 patients already hospitalized in a standard medical unit. It shows limitations for patients who immediately need intensive care, but it draws attention to patients who have an important risk of needing intensive care after at least one day of hospitalization. |
| Databáze: | OpenAIRE |
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