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

PeerJ
Xiaoran LiTim Q Duong

Abstract

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. 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). 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]), res...Continue Reading

References

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Citations

Apr 2, 2021·Journal of Intensive Care·Benjamin MusheyevTim Q Duong
Jun 3, 2021·Journal of Clinical Medicine·Md Mohaimenul IslamYu-Chuan Jack Li
Jul 3, 2021·International Journal of Environmental Research and Public Health·Irfan Ullah KhanSara Mhd Bachar Chrouf
Aug 4, 2021·Scientific Reports·Maurizio BartolucciUNKNOWN COVID Working Group
Aug 20, 2021·International Journal of General Medicine·Miaomiao LiuYandong Nan
Aug 30, 2021·International Journal of Medical Informatics·Maleeha NaseemFahad Shabbir Ahmed

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Methods Mentioned

BETA
X-ray

Software Mentioned

Multivariate Imputation by Chained Equations R
Adam
SPSS
R
Boruta

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