Background & aims

In patients with chronic liver disease (CLD) with or without cirrhosis, existing studies on the outcomes with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have limited generalizability. We used the National COVID Cohort Collaborative (N3C), a harmonized electronic health record dataset of 6.4 million, to describe SARS-CoV-2 outcomes in patients with CLD and cirrhosis.

Methods

We identified all patients with CLD with or without cirrhosis who had SARS-CoV-2 testing in the N3C Data Enclave as of July 1, 2021. We used survival analyses to associate SARS-CoV-2 infection, presence of cirrhosis, and clinical factors with the primary outcome of 30-day mortality.

Results

We isolated 220,727 patients with CLD and SARS-CoV-2 test status: 128,864 (58%) were noncirrhosis/negative, 29,446 (13%) were noncirrhosis/positive, 53,476 (24%) were cirrhosis/negative, and 8941 (4%) were cirrhosis/positive patients. Thirty-day all-cause mortality rates were 3.9% in cirrhosis/negative and 8.9% in cirrhosis/positive patients. Compared to cirrhosis/negative patients, cirrhosis/positive patients had 2.38 times adjusted hazard of death at 30 days. Compared to noncirrhosis/positive patients, cirrhosis/positive patients had 3.31 times adjusted hazard of death at 30 days. In stratified analyses among patients with cirrhosis with increased age, obesity, and comorbid conditions (ie, diabetes, heart failure, and pulmonary disease), SARS-CoV-2 infection was associated with increased adjusted hazard of death.

Conclusions

In this study of approximately 221,000 nationally representative, diverse, and sex-balanced patients with CLD; we found SARS-CoV-2 infection in patients with cirrhosis was associated with 2.38 times mortality hazard, and the presence of cirrhosis among patients with CLD infected with SARS-CoV-2 was associated with 3.31 times mortality hazard. These results provide an additional impetus for increasing vaccination uptake and further research regarding immune responses to vaccines in patients with severe liver disease.

Background

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

Methods

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

Findings

We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.

Interpretation

Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

Funding

NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.