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001-es BibID:BIBFORM109087
035-os BibID:(WoS)000939624800001 (Scopus)85149044317
Első szerző:Buttia, Chepkoech
Cím: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, Ochoa Sergio Alejandro Gómez, Raguindin Peter Francis, Wehrli Faina, Khatami Farnaz, Espínola Octavio Pano, Rojas Lyda Z., de Mortanges Aurélie Pahud, Macharia-Nimietz Eric Francis, Alijla Fadi, Minder Beatrice, Leichtle Alexander B., Lüthi Nora, Ehrhard Simone, Que Yok-Ai, Fernandes Laurenz Kopp, Hautz Wolf, Muka Taulant
Dátum:2023
ISSN:0393-2990
Megjegyzések:Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We per- formed 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), extracor- poreal 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.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
COVID-19
Prediction models
Mortality
ICU
Systematic review
Biomarkers
Megjelenés:European Journal Of Epidemiology. - 38 : 4 (2023), p. 355-372. -
További szerzők:Llanaj, Erand (1988-) (táplálkozási epidemiológus) Raeisi-Dehkordi, Hamidreza Kastrati, Lum Amiri, Mojgan Meçani, Renald Taneri, Petek Eylul Ochoa, Sergio Alejandro Gómez Raguindin, Peter Francis Wehrli, Faina Khatami, Farnaz Espínola, Octavio Pano Rojas, Lyda Z. de Mortanges, Aurélie Pahud Macharia-Nimietz, Eric Francis Alijla, Fadi Minder, Beatrice Leichtle, Alexander B. Lüthi, Nora Ehrhard, Simone Que, Yok-Ai Fernandes, Laurenz Kopp Hautz, Wolf E. Muka, Taulant
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