Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

Abstract

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

Social Determinants of Health Correlating with Mechanical Ventilation of COVID-19 Patients: A Multi-Center Observational Study

“…Social determinants of health, particularly elderly and socioeconomically vulnerable patients, have even been associated with COVID-19 subphenotypes that yield higher mortality rates. 17 Our findings further support the need for health policy to protect these more vulnerable communities. 17 …”

“… 17 Our findings further support the need for health policy to protect these more vulnerable communities. 17 …”

“…Social determinants of health, particularly elderly and socioeconomically vulnerable patients, have even been associated with COVID-19 subphenotypes that yield higher mortality rates. 17 Our findings further support the need for health policy to protect these more vulnerable communities. 17 Our study focuses on a southeast Michigan population with a primarily urban and suburban demographic, though results can be generalized to the population as similar findings have been observed nationally and internationally.…”

“…17 Our findings further support the need for health policy to protect these more vulnerable communities. 17 Our study focuses on a southeast Michigan population with a primarily urban and suburban demographic, though results can be generalized to the population as similar findings have been observed nationally and internationally. Limitations of this study include retrospective nature as well as potential inaccuracies and missing data in the medical record.…”

A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

“…The extremely heterogeneous phenotypic manifestations characterizing COVID-19 patients ( 23 ) and the different long-term consequences of the infection ( 24 ) highlighted the need for personalized treatment interventions. To this aim it is of paramount importance to be able to identify potential subpopulations of patients characterized by similar disease manifestations with a common evolution over time.…”