Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 29, 2020
Open Peer Review Period: Jun 28, 2020 - Jul 7, 2020
Date Accepted: Oct 20, 2020
Date Submitted to PubMed: Oct 22, 2020
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Clinical Characteristics and Prognostic Factors for ICU Admission of Patients with COVID-19 Using Machine Learning And Natural Language Processing
ABSTRACT
Background:
There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic.
Objective:
Here we aimed to describe the clinical characteristics and predictors of ICU use in a large cohort of COVID-19 patients in real time.
Methods:
To achieve the research objective, we used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19.
Results:
A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with a mean age of 58.2 and S.D. 19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39º C, (or >39º C without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care.
Conclusions:
Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.