AUTHOR=Chen Lina , Li Min , Wu Zhenghong , Liu Sibin , Huang Yuanyi 

TITLE=A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

JOURNAL=Frontiers in Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1343661

DOI=10.3389/fmed.2024.1343661

ISSN=2296-858X

ABSTRACT=Objectives: To predict severe COVID-19 progression in patients with increased pneumonia lesions in the early days, a simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computer tomography (CT).
Methods:  From December 17, 2019, to February 20, 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the Neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days.  The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI, Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. 
Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, increase of lesion volume, and changes of SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under the receiver operating characteristic curve (AUC) of 0.961 and a 95% confidence interval (CI) of 0.917–1.000. Decision curve analysis illustrated that a nomogram based on quantitative artificial intelligence was clinically useful. 
Conclusions: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.