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Review
. 2021 Jun 18;79(1):105.
doi: 10.1186/s13690-021-00630-3.

A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers

Médéa Locquet #  1 , Anh Nguyet Diep #  2 , Charlotte Beaudart  1 , Nadia Dardenne  2 , Christian Brabant  1 , Olivier Bruyère #  3 , Anne-Françoise Donneau #  2
Affiliations
Review

A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers

Médéa Locquet et al. Arch Public Health. .

Abstract

Background: The COVID-19 pandemic is putting significant pressure on the hospital system. To help clinicians in the rapid triage of patients at high risk of COVID-19 while waiting for RT-PCR results, different diagnostic prediction models have been developed. Our objective is to identify, compare, and evaluate performances of prediction models for the diagnosis of COVID-19 in adult patients in a health care setting.

Methods: A search for relevant references has been conducted on the MEDLINE and Scopus databases. Rigorous eligibility criteria have been established (e.g., adult participants, suspicion of COVID-19, medical setting) and applied by two independent investigators to identify suitable studies at 2 different stages: (1) titles and abstracts screening and (2) full-texts screening. Risk of bias (RoB) has been assessed using the Prediction model study Risk of Bias Assessment Tool (PROBAST). Data synthesis has been presented according to a narrative report of findings.

Results: Out of the 2334 references identified by the literature search, 13 articles have been included in our systematic review. The studies, carried out all over the world, were performed in 2020. The included articles proposed a model developed using different methods, namely, logistic regression, score, machine learning, XGBoost. All the included models performed well to discriminate adults at high risks of presenting COVID-19 (all area under the ROC curve (AUROC) > 0.500). The best AUROC was observed for the model of Kurstjens et al (AUROC = 0.940 (0.910-0.960), which was also the model that achieved the highest sensitivity (98%). RoB was evaluated as low in general.

Conclusion: Thirteen models have been developed since the start of the pandemic in order to diagnose COVID-19 in suspected patients from health care centers. All these models are effective, to varying degrees, in identifying whether patients were at high risk of having COVID-19.

Keywords: COVID-19; Hospitalisation; Prediction model.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram for the inclusion of prediction models to diagnose COVID-19 developed during the pandemic until March 2021
Fig. 2
Fig. 2
Risk of Bias for each individual study in the systematic review of prediction models to diagnose COVID-19 from the start of the epidemic to March 2021 using the PROBAST tool
See this image and copyright information in PMC

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