Dataset Information

Early triage of critically ill COVID-19 patients using deep learning.

ABSTRACT: The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

SUBMITTER: Liang W

PROVIDER: S-EPMC7363899 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Early triage of critically ill COVID-19 patients using deep learning.

Liang Wenhua W Yao Jianhua J Chen Ailan A Lv Qingquan Q Zanin Mark M Liu Jun J Wong SookSan S Li Yimin Y Lu Jiatao J Liang Hengrui H Chen Guoqiang G Guo Haiyan H Guo Jun J Zhou Rong R Ou Limin L Zhou Niyun N Chen Hanbo H Yang Fan F Han Xiao X Huan Wenjing W Tang Weimin W Guan Weijie W Chen Zisheng Z Zhao Yi Y Sang Ling L Xu Yuanda Y Wang Wei W Li Shiyue S Lu Ligong L Zhang Nuofu N Zhong Nanshan N Huang Junzhou J He Jianxing J

Nature communications 20200715 1

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further ...[more]

PMID: 32669540

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Project description:Study objectiveTriage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep-learning approaches could identify critically ill patients only using data immediately available at triage.MethodsWe conducted a retrospective, cross-sectional study at an urban tertiary care center, from January 1, 2012-January 1, 2020. De-identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep-learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver-operator curve (AUC).ResultsA total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519-0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671-0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802-0.804), 2-layer neural network with structured data with AUC 0.811 (95% CI = 0.807-0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818-0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849-0.852). All successive increases in AUC were statistically significant.ConclusionDeep-learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.

Dataset Information

Early triage of critically ill COVID-19 patients using deep learning.

Publications

Early triage of critically ill COVID-19 patients using deep learning.

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