Using Lung Base Covid-19 Findings to Predict Future Disease Trends and New Variant Outbreaks: Study of First New York City (NYC) Outbreak. Academic Article uri icon

Overview

abstract

  • RATIONALE AND OBJECTIVES: Asymptomatic COVID-19 carriers and insufficient testing make containment of the virus difficult. The purpose of this study was to determine if unexpected lung base findings on abdominopelvic CTs concerning for COVID-19 infection could serve as a surrogate for the diagnosis of COVID-19 in the community. MATERIALS AND METHODS: A database search of abdominopelvic CT reports from March 1,2020 to May 2,2020 was performed for keywords suggesting COVID-19 infection by lung base findings. COVID-19 status, respiratory symptoms, laboratory parameters and patient outcomes (hospitalization, ICU admission and/or intubation, and death) were recorded. The trend in cases of unexpected concerning lung base findings on abdominopelvic CT at our institution was compared to the total number of confirmed new cases in NYC over the same time period. RESULTS: The trend in abnormal lung base findings on abdominopelvic CT at our institution correlated with the citywide number of confirmed new cases, including rise and subsequent fall in total cases. The trend was not mediated by COVID-19 testing status or number of tests performed. Patients with respiratory symptoms had significantly higher ferritin (median = 995ng/ml vs 500ng/ml, p = 0.027) and death rate (8/24, 33% vs 4/54, 9%, p = 0.018) compared to those without. CONCLUSION: The rise and fall of unexpected lung base findings suggestive of COVID-19 infection on abdominopelvic CT in patients without COVID-19 symptoms correlated with the number of confirmed new cases throughout NYC from the same time period. A model using abdominopelvic CT lung base findings can serve as a surrogate for future COVID-19 outbreaks.

publication date

  • October 1, 2021

Research

keywords

  • COVID-19

Identity

PubMed Central ID

  • PMC8484077

Scopus Document Identifier

  • 85119062493

Digital Object Identifier (DOI)

  • 10.1016/j.acra.2021.09.023

PubMed ID

  • 34740527

Additional Document Info

volume

  • 28

issue

  • 12