AUTHOR=Xu Jingjing , Cao Zhengye , Miao Chunqin , Zhang Minming , Xu Xiaojun 

TITLE=Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1192376

DOI=10.3389/fmed.2023.1192376

ISSN=2296-858X

ABSTRACT=Background: In December, 2022, there was a large Omicron epidemic in Hangzhou, China. Many of them were diagnosed as Omicron pneumonia with variable symptom severity and outcome.  We hypothesized that Computed tomography (CT)-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI) related features.
Methods: Our study included 238 patients with the omicron variant who have been admitted in our hospital in China during December 15, 2022 to January 16, 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to omicron pneumonia. Support vector machine (SVM) model was used to predict the disease severity and outcome.
Results: Receiver-operating-characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI related features was 0.85 (Accuracy = 87.40%, p < 0.001) for predicting severity, while using CT-based features only 0.70 (Accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased showing 0.84 (Accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI related features (Accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC=0.67, Accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (Accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6 and CT infiltration showed great importance in both predicting severity and outcome.
Conclusions: The predictive model accurately predicts severity and outcome in Omicron infection. Oxygen saturation, IL-6 and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful and potentially resource-constrained environments.