Predictive Parameters for the Worsening Clinical Course of Mild COVID-19 Pneumonia

Abstract

Background: This study aimed to determine parameters for worsening oxygenation in mild COVID-19 pneumonia.Methods: This retrospective cohort study included confirmed COVID-19 pneumonia in a single public hospital in South Korea from January to April 2020. Parameters were compared between the two groups on the basis of clinical course: the desaturation group was defined as those with oxygen saturation ≤ 94% on ambient air, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the nonevent group who were without any respiratory event up to 28 days. The severity and extent of viral pneumonia from an initial single chest CT were calculated using artificial intelligence (AI) algorithms and measured visually by a radiologist. Results: We included 136 patients with 32 (23.5%) in the desaturation group, of whom two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups, however, univariate logistic regression analysis revealed that a variety of parameters at admission were associated with an increased risk of a desaturation event. In a sex-, age-, and comorbid illness-matched case-control study, ferritin ≥ 280 μg/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), LDH≥ 240 U/L (OR 3.600, 95% CI 1.142-11.346; p=0.029), pneumonia burden (OR 1.010, 95% CI 1.002-1.019; p=0.021), and extent (OR 1.194, 95% CI 1.017-1.401; p=0.030) by AI, and visual severity scores (OR 1.146, 95% CI 1.005-1.307; p=0.042) were the predictive parameters for worsening clinical course with desaturation. Conclusion: Our study presents initial CT parameters measured by AI or visual severity scoring as well as serum markers of inflammation at admission as the best parameters for predicting worsening oxygenation in the COVID-19 pneumonia cohort. Initial chest CT scans may help clinicians diagnose viral pneumonia and evaluate the prognosis in mild COVID-19.

Deep Learning–Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study

“…Despite these limitations, the potential utility of our software has been shown in a series of studies. 33,37,38 Several limitations exist in this study. First, we retrospectively collected data from 146 patients, and their CT images may not cover a diverse range of radiologic manifestations of COVID-19.…”

“…5 and 6), whereas minute lesions in the apical end of lung parenchyma tended to be missed. Despite these limitations, the potential utility of our software has been shown in a series of studies 33,37,38 …”