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In Tuberculosis and respiratory diseases

Background : This study aimed to determine parameters for worsening oxygenation in non-severe COVID-19 pneumonia.

Methods : This retrospective cohort study included confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as those with SpO2 ≤ 94%, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening group who were without any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest CT were calculated using artificial intelligence (AI) and measured visually by a radiologist.

Results : We included 136 patients with 32 (23.5%) in the worsening oxygenation 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. A subset of patients were studied to eliminate potential bias, that ferritin ≥ 280 μg/L (p=0.029), LDH ≥ 240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52).

Conclusion : Our study presents initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation at admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

Hahm Cho Rom, Lee Young Kyung, Oh Dong Hyun, Ahn Mi Young, Choi Jae-Phil, Kang Na Ree, Oh Jungkyun, Choi Hanzo, Kim Suhyun

2021-Jan-05

COVID-19, Computed tomography, Oxygenation, Pneumonia, artificial intelligence