Dataset Information

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

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.

SUBMITTER: Cheng J

PROVIDER: S-EPMC8857913 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

Cheng Jianhong J Sollee John J Hsieh Celina C Yue Hailin H Vandal Nicholas N Shanahan Justin J Choi Ji Whae JW Tran Thi My Linh TML Halsey Kasey K Iheanacho Franklin F Warren James J Ahmed Abdullah A Eickhoff Carsten C Feldman Michael M Mortani Barbosa Eduardo E Kamel Ihab I Lin Cheng Ting CT Yi Thomas T Healey Terrance T Zhang Paul P Wu Jing J Atalay Michael M Bai Harrison X HX Jiao Zhicheng Z Wang Jianxin J

European radiology 20220219 7

<h4>Objectives</h4>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).<h4>Methods</h4>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 ...[more]

PMID: 35184218

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Dataset Information

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

Objectives

Methods

Results

Conclusions

Key points

Publications

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

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