Drivers of Mortality in COVID ARDS Depend on Patient Sub-Type

Background The most common cause of death in people with COVID-19 is acute respiratory distress syndrome (ARDS). ARDS is a heterogeneous syndrome, however, subgroups that have been identified among non-COVID-19 ARDS patients do not clearly apply to COVID-19 ARDS patients. Additionally, studies of COVID-19 ARDS have been limited by sample size. Methods We applied an iterative clustering and machine learning framework to electronic health record data from thousands of hospitalized COVID-19 ARDS patients with the goal of defining and characterizing clinically-relevant COVID-19 ARDS subgroups (phenoclusters). We then applied a supervised model to identify risk factors for hospital mortality for each phenocluster and compared these between phenoclusters and the entire cohort. Findings Risk factors that predict mortality in the overall cohort of COVID-19 ARDS patients do not necessarily predict mortality in phenoclusters. In fact, some risk factors increase the risk of hospital mortality in some phenoclusters, but decrease mortality in others. Interpretation 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 drivers of mortality may partially explain challenges in finding effective treatments when applied to all patients with ARDS. Funding This work was supported by philanthropic funds to the Feinstein Institutes for Medical Research. The funding source did not control any aspect of the study and did not review the results. All authors had full access to the full data in the study and accept responsibility to submit for publication.