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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by philanthropic funds to the Feinstein Center for Health Outcomes and Innovation 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. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of the Feinstein Institutes for Medical Research gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors