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?:abstract
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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
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