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A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics - PubMed

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics

Cong Feng et al. Ann Transl Med. 2021 Feb.

Abstract

Background: Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator.

Methods: Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission.

Results: The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed.

Conclusions: A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.

Keywords: Suspected COVID-19 pneumonia (S-COVID-19-P); diagnosis aid model; fever clinics; machine learning.

2021 Annals of Translational Medicine. All rights reserved.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-3073). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1

The study overview of the Artificial Intelligence-Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia, including (I) development and validation cohorts, (II) outcomes, (III) diagnosis aid model and candidate features, (IV) feature selection and diagnosis aid model development, (V) model validation, and (VI) feature importance ranking and comparison of diagnostic performance between model and biomarker. COVID-19, 2019 novel coronavirus disease; S-COVID-19-P, suspected COVID-19 pneumonia; AUC, area under the ROC curve; ROC, receiver operating characteristic; CRP, C-reactive protein; IL-6, interleukin-6.

Figure 2
Figure 2

Feature importance ranking. Feature importance was determined in the development cohort. The associated coefficient weights corresponding to the logistic regression model were used for identifying and ranking feature importance. FC: °C, normal: ≤37.0; mild fever: 37.1–38.0; moderate fever: 38.1–39.0; severe fever: ≥39.1. FC, fever classification; IL-6, interleukin-6; SYS_BP, systolic blood pressure; MONO%, monocyte ratio; PLT, platelet count; DIAS_BP, diastolic blood pressure; HR, heart rate; MCH, mean corpuscular hemoglobin content; TEM, temperature; EO#, eosinophil count; BASO#, basophil count.

Figure 3
Figure 3

Flow chart for improved early S-COVID-19-P identification strategies in adult fever clinics in PLAGH, China. COVID-19, 2019 novel coronavirus disease; S-COVID-19-P, suspected COVID-19 pneumonia; PLAGH, People’s Liberation Army General Hospital; CRP, C-reactive protein; IL-6, interleukin-6; CT, computed tomography.

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