Identifying clinical and biochemical phenotypes in acute respiratory distress syndrome secondary to coronavirus disease-2019

Abstract

Background: Acute respiratory distress syndrome (ARDS) secondary to coronavirus disease-2019 (COVID-19) is characterized by substantial heterogeneity in clinical, biochemical, and physiological characteristics. However, the pathophysiology of severe COVID-19 infection is poorly understood. Previous studies established clinical and biological phenotypes among classical ARDS cohorts, with important therapeutic implications. The phenotypic profile of COVID-19 associated ARDS remains unknown.

Methods: We used latent class modeling via a multivariate mixture model to identify phenotypes from clinical and biochemical data collected from 263 patients admitted to Massachusetts General Hospital intensive care unit with COVID-19-associated ARDS between March 13 and August 2, 2020.

Findings: We identified two distinct phenotypes of COVID-19-associated ARDS, with substantial differences in biochemical profiles despite minimal differences in respiratory dynamics. The minority phenotype (class 2, n = 70, 26·6%) demonstrated increased markers of coagulopathy, with mild relative hyper-inflammation and dramatically increased markers of end-organ dysfunction (e.g., creatinine, troponin). The odds of 28-day mortality among the class 2 phenotype was more than double that of the class 1 phenotype (40·0% vs.· 23·3%, OR = 2·2, 95% CI [1·2, 3·9]).

Interpretation: We identified distinct phenotypic profiles in COVID-19 associated ARDS, with little variation according to respiratory physiology but with important variation according to systemic and extra-pulmonary markers. Phenotypic identity was highly associated with short-term mortality. The class 2 phenotype exhibited prominent signatures of coagulopathy, suggesting that vascular dysfunction may play an important role in the clinical progression of severe COVID-19-related disease.

Keywords: ARDS; COVID-19; Phenotypes; Statistical inference.

Fig. 1.

A: Differences in the mean standardized values of continuous class-defining variables by latent class. For variable standardization, means are scaled to zero and standard deviations to one. B: Distribution of raw data for D-dimer, fibrinogen, and IL-6 by latent class. P-values calculated by Kruskall-Wallis test for D-dimer and fibrinogen, and by T-test for IL-6. Boxes denote the inter-quartile range (IQR) of the data, and whiskers extend to 1.5 times the edge of the IQR. Individual outliers are shown as individual points in black. The raw, jittered data are overlain in light gray.

Fig. 2.

Interval between hospital admission and ICU transfer (A) and interval between hospital admission and intubation (B) by latent class, with p-values calculated by Wilcoxon rank sum test. Boxes denote the inter-quartile range (IQR) of the data, and whiskers extend to 1.5 times the edge of the IQR. Individual outliers are shown as individual points in black. The raw, jittered data are overlain in light gray.