Bedside clinical data subphenotypes of critically ill COVID-19 patients: a cohort study - Critical Care Science (CCS)

Abstract

Objective:

To identify more severe COVID-19 presentations.

Methods:

Consecutive intensive care unit-admitted patients were subjected to a stepwise clustering method.

Results:

Data from 147 patients who were on average 56 ± 16 years old with a Simplified Acute Physiological Score 3 of 72 ± 18, of which 103 (70%) needed mechanical ventilation and 46 (31%) died in the intensive care unit, were analyzed. From the clustering algorithm, two well-defined groups were found based on maximal heart rate [Cluster A: 104 (95%CI 99 – 109) beats per minute versus Cluster B: 159 (95%CI 155 – 163) beats per minute], maximal respiratory rate [Cluster A: 33 (95%CI 31 – 35) breaths per minute versus Cluster B: 50 (95%CI 47 – 53) breaths per minute], and maximal body temperature [Cluster A: 37.4 (95%CI 37.1 – 37.7)°C versus Cluster B: 39.3 (95%CI 39.1 – 39.5)°C] during the intensive care unit stay, as well as the oxygen partial pressure in the blood over the oxygen inspiratory fraction at intensive care unit admission [Cluster A: 116 (95%CI 99 – 133) mmHg versus Cluster B: 78 (95%CI 63 – 93) mmHg]. Subphenotypes were distinct in inflammation profiles, organ dysfunction, organ support, intensive care unit length of stay, and intensive care unit mortality (with a ratio of 4.2 between the groups).

Conclusion:

Our findings, based on common clinical data, revealed two distinct subphenotypes with different disease courses. These results could help health professionals allocate resources and select patients for testing novel therapies.