Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables

PeerJ

article

Xiaoran Li¹  Peilin Ge¹  Jocelyn Zhu¹  Haifang Li¹  James Graham¹  Adam Singer²  Paul S. Richman³  Tim Q. Duong⁴ 

[1] Department of Radiology, Renaissance School of Medicine, Stony Brook University;Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University;Department of Medicine, Renaissance School of Medicine, Stony Brook University;Department of Radiology, Albert Einstein College of Medicine

关键词: Machine learning;    Coronavirus;    Pneumonia;    SARS-CoV-2;    Prediction model;   

DOI  :  10.7717/peerj.10337

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. Conclusions Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.

【 授权许可】

CC BY