A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores
© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America..
BACKGROUND: Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19).
METHODS: We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system).
RESULTS: Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far.
CONCLUSIONS: The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:78 |
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| Enthalten in: |
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America - 78(2024), 4 vom: 10. Apr., Seite 889-899 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Appel, Katharina S [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 12.04.2024 Date Revised 25.04.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/cid/ciad618 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM363713093 |
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| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. | ||
| 520 | |a BACKGROUND: Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19) | ||
| 520 | |a METHODS: We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system) | ||
| 520 | |a RESULTS: Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far | ||
| 520 | |a CONCLUSIONS: The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future | ||
| 650 | 4 | |a Systematic Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a pandemic preparedness | |
| 650 | 4 | |a prediction models | |
| 650 | 4 | |a predictors | |
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| 700 | 1 | |a Geisler, Ramsia |e verfasserin |4 aut | |
| 700 | 1 | |a Maier, Daniel |e verfasserin |4 aut | |
| 700 | 1 | |a Miljukov, Olga |e verfasserin |4 aut | |
| 700 | 1 | |a Hopff, Sina M |e verfasserin |4 aut | |
| 700 | 1 | |a Vehreschild, J Janne |e verfasserin |4 aut | |
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