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Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.
- Source :
-
BMJ open [BMJ Open] 2020 Sep 23; Vol. 10 (9), pp. e041983. Date of Electronic Publication: 2020 Sep 23. - Publication Year :
- 2020
-
Abstract
- Objectives: Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.<br />Design: The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.<br />Setting: 3 secondary and tertiary level centres in Greater Manchester, the UK.<br />Participants: 392 hospitalised patients with a diagnosis of COVID-19.<br />Results: 392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.<br />Conclusions: This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.<br />Competing Interests: Competing interests: Swedish Orphan Biovitrum have provided investigational medicinal product for public-funded, peer-reviewed trials on which AK, AV, JG, HCP and SH are coinvestigators. The other authors declare no competing interests.<br /> (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)
- Subjects :
- Aged
Aged, 80 and over
Area Under Curve
Betacoronavirus
Biomarkers blood
COVID-19
Cohort Studies
Coronavirus Infections blood
Female
Hospitalization
Humans
Leukocyte Count
Male
Middle Aged
Pandemics
Pneumonia, Viral blood
Prognosis
ROC Curve
Retrospective Studies
Risk Assessment
SARS-CoV-2
United Kingdom
Bilirubin blood
C-Reactive Protein metabolism
Coronavirus Infections mortality
Creatinine blood
Lymphocyte Count
Neutrophils
Pneumonia, Viral mortality
Urea blood
Subjects
Details
- Language :
- English
- ISSN :
- 2044-6055
- Volume :
- 10
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- BMJ open
- Publication Type :
- Academic Journal
- Accession number :
- 32967887
- Full Text :
- https://doi.org/10.1136/bmjopen-2020-041983