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

Buttia, Chepkoech; Llanaj, Erand; Raeisi-Dehkordi, Hamidreza; Kastrati, Lum; Amiri, Mojgan; Meçani, Renald; Taneri, Petek Eylul; Gomez Ochoa, Sergio Alejandro; Raguindin, Peter Francis; Wehrli, Faina; Khatami, Farnaz; Pano Espinola, Octavio; Rojas, Lyda Z; de Mortanges, Aurélie Pahud; Macharia-Nimietz, Eric Francis; Alijla, Fadi; Minder, Beatrice; Leichtle, Alexander B; Lüthi, Nora; Ehrhard, Simone; ... (2023). Prognostic models in COVID-19 infection that predict severity: a systematic review. European journal of epidemiology, 38(4), pp. 355-372. Springer 10.1007/s10654-023-00973-x

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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.

Item Type:

Journal Article (Review Article)


04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic of Intensive Care
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry
13 Central Units > Administrative Director's Office > University Library of Bern
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

Graduate School:

Graduate School for Health Sciences (GHS)

UniBE Contributor:

Buttia, Chepkoech, Raeisidehkordi, Hamidreza, Kastrati, Lum, Taneri, Petek Eylul, Gomez Ochoa, Sergio Alejandro, Raguindin, Peter Francis, Wehrli, Faina, Khatami, Farnaz, Pano Espinola, Octavio, Alijla, Fadi, Minder, Beatrice, Leichtle, Alexander Benedikt (B), Lüthi, Nora, Ehrhard, Simone, Que, Yok-Ai, Hautz, Wolf, Muka, Taulant


600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services
000 Computer science, knowledge & systems > 020 Library & information sciences






[222] Horizon 2020 ; [4] Swiss National Science Foundation




Pubmed Import

Date Deposited:

27 Feb 2023 13:09

Last Modified:

09 May 2023 10:07

Publisher DOI:


PubMed ID:


Additional Information:

Chepkoech Buttia, Erand Llanaj, Hamidreza Raeisi-Dehkordi and Lum Kastrati contributed equally. Wolf Hautz and Taulant Muka also contributed equally to this work.

Open Access funding provided by the University of Bern.

Uncontrolled Keywords:

Biomarkers COVID-19 ICU Mortality Prediction models Systematic review




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