Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study

Yuan-Cheng Wang; Shu-Hang Zhang; Wen-Hui Lv; Wei-Lang Wang; Shan Huang; Yue Qiu; Jian-Feng Xie; Yi Yang; Shenghong Ju

doi:10.1007/s42058-023-00116-x

Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study

Chin J Acad Radiol. 2023;6(1):47-56. doi: 10.1007/s42058-023-00116-x. Epub 2023 Jan 29.

Authors

Yuan-Cheng Wang^{1

2}, Shu-Hang Zhang¹, Wen-Hui Lv¹, Wei-Lang Wang¹, Shan Huang¹, Yue Qiu¹, Jian-Feng Xie^{3

2}, Yi Yang^{3

2}, Shenghong Ju¹

Affiliations

¹ Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009 Jiangsu China.
² Jiangsu Provincial Key Laboratory of Critical Care Medicine, Southeast University, Nanjing, 210009 China.
³ Department of Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, 87 Ding Jia Qiao Road, Nanjing, 210009 Jiangsu China.

Abstract

Background: Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information.

Methods: The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.

Results: Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, P < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, P = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, P = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815).

Conclusions: The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation.

Supplementary information: The online version contains supplementary material available at 10.1007/s42058-023-00116-x.

Keywords: Acute respiratory distress syndrome; Computed tomography; Mechanical ventilation; Prediction model; Radiomics.

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