1. COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.
- Author
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Cheng J, Sollee J, Hsieh C, Yue H, Vandal N, Shanahan J, Choi JW, Tran TML, Halsey K, Iheanacho F, Warren J, Ahmed A, Eickhoff C, Feldman M, Mortani Barbosa E Jr, Kamel I, Lin CT, Yi T, Healey T, Zhang P, Wu J, Atalay M, Bai HX, Jiao Z, and Wang J
- Subjects
- Humans, Intensive Care Units, Radiography, X-Rays, COVID-19, Deep Learning
- Abstract
Objectives: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU)., Methods: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models., Results: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732., Conclusions: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU., Key Points: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)
- Published
- 2022
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