• search hit 14 of 2981
Back to Result List

Prognostic models in COVID-19 infection that predict severity: a systematic review

  • Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination ofCurrent evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.show moreshow less

Download full text files

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Chepkoech Buttia, Erand Llanaj, Hamidreza Raeisi-Dehkordi, Lum Kastrati, Mojgan Amiri, Renald Meçani, Petek Eylul Taneri, Sergio Alejandro Gómez Ochoa, Peter Francis Raguindin, Faina Wehrli, Farnaz Khatami, Octavio Pano Espínola, Lyda Z. Rojas, Aurélie Pahud de Mortanges, Eric Francis Macharia-Nimietz, Fadi Alijla, Beatrice Minder, Alexander B. LeichtleORCiDGND, Nora Lüthi, Simone Ehrhard, Yok-Ai Que, Laurenz Kopp Fernandes, Wolf Hautz, Taulant Muka
URN:urn:nbn:de:bvb:384-opus4-1246372
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/124637
ISSN:0393-2990OPAC
ISSN:1573-7284OPAC
Parent Title (English):European Journal of Epidemiology
Publisher:Springer Science and Business Media LLC
Place of publication:Berlin
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2025/08/22
Volume:38
Issue:4
First Page:355
Last Page:372
DOI:https://doi.org/10.1007/s10654-023-00973-x
Institutes:Medizinische Fakultät
Medizinische Fakultät / Universitätsklinikum
Medizinische Fakultät / Professur für Laboratoriumsmedizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)