Phenotypes and Subphenotypes of Patients with COVID-19: a Latent Class Modeling Analysis

Wang, Xiaofeng; Jehi, Lara; Ji, Xinge; Mazzone, Peter J.

Background Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts. Research Question Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? Study Design and Methods: We included adult patients (≥18 years old) who were laboratory-confirmed SARS-CoV-2 positive from a prospective COVID-19 registry database in the Cleveland Clinic Health System in Ohio and Florida. The patients were split into training and testing sets. Using latent class analysis (LCA), we first identified phenotype clusters of patients with COVID-19 based on demographics, comorbidities, and presenting symptoms. We then identified subphenotypes of hospitalized patients with additional blood biomarker data measured at hospital admission. The association of phenotypes/subphenotypes and clinical outcomes were investigated. Multivariable prediction models were established to predict assignment to the LCA-defined phenotypes and subphenotypes and then evaluated on an independent testing set. Results We analyzed data for 20,572 patients. Seven phenotypes were identified based on different profiles of presenting COVID-19 symptoms and existing comorbidities, including Young-no-symptom; Young-symptom; Middle-aged-no-symptom; Middle-aged-symptom; Middle-aged-comorbidities; Old-no-symptom; and Old-with-symptom groups. The rates of inpatient hospitalization for the phenotypes were significantly different (p<0.001). Five subphenotypes were identified for the subgroup of hospitalized patients, including Young, elevated WBC and platelet counts; Middle-aged, lymphopenic with elevated CRP; Middle-aged, hyperinflammatory; Old, leukopenic with comorbidities; Old, hyperinflammatory with kidney dysfunction subgroups. The hospital mortality and the times from hospitalization to intensive care unit transfer or death were significantly different (p<0.001). The models for predicting the LCA-defined phenotypes and subphenotypes showed high discrimination (C-index, 0.92 and 0.91). Interpretation Hypothesis-free LCA-defined phenotypes and subphenotypes of COVID-19 patients can be identified. These may help clinical investigators conduct stratified analysis in clinical trials and assist basic science researchers in characterizing the pathobiology of the spectrum of COVID-19 presentations.