Drivers of mortality in COVID ARDS depend on patient sub-type.

ABSTRACT

The most common cause of death in people with COVID-19 is Acute Respiratory Distress Syndrome (ARDS). Prior studies have demonstrated that ARDS is a heterogeneous syndrome and have identified ARDS sub-types (phenoclusters). However, non-COVID-19 ARDS phenoclusters do not clearly apply to COVID-19 ARDS patients. In this retrospective cohort study, we implemented an iterative approach, combining supervised and unsupervised machine learning methodologies, to identify clinically relevant COVID-19 ARDS phenoclusters, as well as characteristics that are predictive of the outcome for each phenocluster. To this end, we applied a supervised model to identify risk factors for hospital mortality for each phenocluster and compared these between phenoclusters and the entire cohort. We trained the models using a comprehensive, preprocessed dataset of 2,864 hospitalized COVID-19 ARDS patients. Our research demonstrates that the risk factors predicting mortality in the overall cohort of COVID-19 ARDS may not necessarily apply to specific phenoclusters. Additionally, some risk factors increase the risk of hospital mortality in some phenoclusters but decrease mortality in others. These phenocluster-specific risk factors would not have been observed with a single predictive model. Heterogeneity in phenoclusters of COVID-19 ARDS as well as the drivers of mortality may partially explain challenges in finding effective treatments for all patients with ARDS.