| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 9
| Issue : 4 | Page : 136-144 |
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Dynamic chest computed tomography change analysis and prediction of length of stay for delta variant COVID-19 patients
Xiaoyan Xin1, Wen Yang1, Ying Wei2, Jun Hu1, Xin Peng1, Yi Sun3, Cong Long1, Xin Zhang1, Chao Du4, Feng Shi2, Bing Zhang1
1 Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Jiangsu, China
2 Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Xuhui, Shanghai, China
3 Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Jiangsu, China
4 Department of Radiology, The Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
Correspondence Address:
Bing Zhang
Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu
China
Feng Shi
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., 701 Yunjin Road, Xuhui District, Shanghai, 200232
China
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/RID.RID_40_22
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OBJECTIVE: As hospital admission rate is high during the COVID-19 pandemic, hospital length of stay (LOS) is a key indicator of medical resource allocation. This study aimed to elucidate specific dynamic longitudinal computed tomography (CT) imaging changes for patients with COVID-19 over in-hospital and predict individual LOS of COVID-19 patients with Delta variant of SARS-CoV-2 using the machine learning method.
MATERIALS AND METHODS: This retrospective study recruited 448 COVID-19 patients with a total of 1761 CT scans from July 14, 2021 to August 20, 2021 with an averaged hospital LOS of 22.5 ± 7.0 days. Imaging features were extracted from each CT scan, including CT morphological characteristics and artificial intelligence (AI) extracted features. Clinical features were obtained from each patient's initial admission. The infection distribution in lung fields and progression pattern tendency was analyzed. Then, to construct a model to predict patient LOS, each CT scan was considered as an independent sample to predict the LOS from the current CT scan time point to hospital discharge combining with the patients' corresponding clinical features. The 1761 follow-up CT data were randomly split into training set and testing set with a ratio of 7:3 at patient-level. A total of 85 most related clinical and imaging features selected by Least Absolute Shrinkage and Selection Operator were used to construct LOS prediction model.
RESULTS: Infection-related features were obtained, such as the percentage of the infected region of lung, ground-glass opacity (GGO), consolidation and crazy-paving pattern, and air bronchograms. Their longitudinal changes show that the progression changes significantly in the earlier stages (0–3 days to 4–6 days), and then, changes tend to be statistically subtle, except for the intensity range between (−470 and −70) HU which exhibits a significant increase followed by a continuous significant decrease. Furthermore, the bilateral lower lobes, especially the right lower lobe, present more severe. Compared with other models, combining the clinical, imaging reading, and AI features to build the LOS prediction model achieved the highest R2 of 0.854 and 0.463, Pearson correlation coefficient of 0.939 and 0.696, and lowest mean absolute error of 2.405 and 4.426, and mean squared error of 9.176 and 34.728 on the training and testing set.
CONCLUSION: The most obvious progression changes were significantly in the earlier stages (0–3 days to 4–6 days) and the bilateral lower lobes, especially the right lower lobe. GGO, consolidation, and crazy-paving pattern and air bronchograms are the most main CT findings according to the longitudinal changes of infection-related features with LOS (day). The LOS prediction model of combining clinical, imaging reading, and AI features achieved optimum performance.
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