Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups among COVID-19 Patients in the Emergency Department

Abstract

Background:

Early detection and intervention are the key factors for improving outcomes in COVID-19.

Objective:

To detect severity subgroups among COVID-19 patients, based only on clinical data and standard laboratory tests obtained during the assessment at the emergency department.

Methods:

We applied unsupervised machine learning to a dataset of 853 COVID-19 patients from HM hospitals in Spain.

Results:

From a total of 850 variables, four tests, the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH) and C-reactive protein (CRP), and the number of neutrophils, were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished the cluster 3 from the other two clusters. The cluster 1 was characterized by the higher mortality rate and higher levels of AST, ALT, LDH, CRP and number of neutrophils, and low percentage of monocytes and lymphocytes. The cluster 2 included patients with a moderate mortality rate and medium levels of the previous laboratory determinations. The cluster 3 was characterized by the lower mortality rate and lower levels of AST, ALT, LDH, CRP and number of neutrophils, and higher percentage of monocytes and lymphocytes. Age, sex, comorbidities, and vital signs did not allow us to separate the three clusters. An online cluster assignment tool can be found at https://g-nec.car.upm-csic.es/COVID19-severity-group-assessment/.

Conclusions:

A few standard laboratory tests, deemed to be available in all emergency departments, have shown far discriminative power for characterization of severity subgroups among COVID-19 patients.