Point-of-care lung ultrasound predicts severe disease and death due to COVID-19: a prospective cohort study. (preprint)

Objective: The clinical utility of point-of-care lung ultrasound (LUS) for disease severity triage of hospitalized patients with COVID-19 is unclear. Design: Prospective cohort study Setting: A large tertiary care center in Maryland, USA between April 2020 to September 2021. Patients: Hospitalized adults (18 years of age or greater) with positive SARS-CoV-2 RT-PCR results. Interventions: None. Measurements and Main Results: All patients were scanned using a standardized protocol including 12 lung zones and followed to determine clinical outcomes until hospital discharge and vital status at 28-days. Ultrasounds were independently reviewed for lung and pleural line artifacts and abnormalities, and the mean Lung Ultrasound Score (ranging from 0 to 3) across lung zones (mLUSS) was determined. The primary outcome was time to ICU-level care, defined as high flow oxygen, noninvasive, or mechanical ventilation, within 28-days of the initial ultrasound. Cox proportional hazards regression models adjusted for age and sex were fit for mLUSS and each ultrasound covariate. A total of 264 participants were enrolled in the study; the median age was 59 years and 114 (43.2) % of participants were female. The median mLUSS was 1 (interquartile range: 0.5 to 1.3). Following enrollment, 29 (11.0%) participants went on to require ICU-level care and 14 (5.3%) subsequently died by 28 days. Each increase in mLUSS at enrollment was associated with disease progression to ICU-level care (aHR = 3.63; 95% CI: 1.23 to 10.65) and 28-day mortality (aHR = 4.50; 95% CI: 1.52 to 13.31). Pleural line abnormalities were independently associated with disease progression to ICU-level care (aHR = 18.86; CI: 1.57 to 226.09). Conclusions: Participants with a mLUSS of 1 or more or pleural line changes on LUS had an increased likelihood of subsequent requirement of high flow oxygen or greater. LUS is a promising tool for assessing risk of COVID-19 progression at the bedside.

Topological data analysis identifies distinct biomarker phenotypes during the inflammatory phase of COVID-19 (preprint)

OBJECTIVES: The relationships between baseline clinical phenotypes and the cytokine milieu of the peak inflammatory phase of coronavirus 2019 (COVID-19) are not yet well understood. We used Topological Data Analysis (TDA), a dimensionality reduction technique to identify patterns of inflammation associated with COVID-19 severity and clinical characteristics. DESIGN: Exploratory analysis from a multi-center prospective cohort study. SETTING: Eight military hospitals across the United States between April 2020 and January 2021. PATIENTS: Adult ([≥]18 years of age) SARS-CoV-2 positive inpatient and outpatient participants were enrolled with plasma samples selected from the putative inflammatory phase of COVID-19, defined as 15-28 days post symptom onset. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Concentrations of 12 inflammatory protein biomarkers were measured using a broad dynamic range immunoassay. TDA identified 3 distinct inflammatory protein expression clusters. Peak severity (outpatient, hospitalized, ICU admission or death), Charlson Comorbidity Index (CCI), and body mass index (BMI) were evaluated with logistic regression for associations with each cluster. The study population (n=129, 33.3% female, median 41.3 years of age) included 77 outpatient, 31 inpatient, 16 ICU-level, and 5 fatal cases. Three distinct clusters were found that differed by peak disease severity (p <0.001), age (p <0.001), BMI (p<0.001), and CCI (p=0.001). CONCLUSIONS: Exploratory clustering methods can stratify heterogeneous patient populations and identify distinct inflammation patterns associated with comorbid disease, obesity, and severe illness due to COVID-19.