TY - JOUR
T1 - Prognostic models in COVID-19 infection that predict severity
T2 - a systematic review
AU - Buttia, Chepkoech
AU - Llanaj, Erand
AU - Raeisi-Dehkordi, Hamidreza
AU - Kastrati, Lum
AU - Amiri, Mojgan
AU - Meçani, Renald
AU - Taneri, Petek Eylul
AU - Ochoa, Sergio Alejandro Gómez
AU - Raguindin, Peter Francis
AU - Wehrli, Faina
AU - Khatami, Farnaz
AU - Espínola, Octavio Pano
AU - Rojas, Lyda Z.
AU - de Mortanges, Aurélie Pahud
AU - Macharia-Nimietz, Eric Francis
AU - Alijla, Fadi
AU - Minder, Beatrice
AU - Leichtle, Alexander B.
AU - Lüthi, Nora
AU - Ehrhard, Simone
AU - Que, Yok Ai
AU - Fernandes, Laurenz Kopp
AU - Hautz, Wolf
AU - Muka, Taulant
N1 - Funding Information:
Open access funding provided by University of Bern. This project has received funding from: (i) European Union's Horizon 2020 research and innovation programme under grant agreement No. 101017915 and (ii) Swiss National Science Foundation (SNF): #320030_176216.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/4
Y1 - 2023/4
N2 - 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 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.
AB - 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 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.
KW - Biomarkers
KW - COVID-19
KW - ICU
KW - Mortality
KW - Prediction models
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=85149044317&partnerID=8YFLogxK
U2 - 10.1007/s10654-023-00973-x
DO - 10.1007/s10654-023-00973-x
M3 - Review article
C2 - 36840867
AN - SCOPUS:85149044317
SN - 0393-2990
VL - 38
SP - 355
EP - 372
JO - European Journal of Epidemiology
JF - European Journal of Epidemiology
IS - 4
ER -