Aims: To explore the association of obesity with the progression and outcome of coronavirus disease 2019 (COVID-19) at the acute period and 5-month follow-up from the perspectives of computed tomography (CT) imaging with artificial intelligence (AI)-based quantitative evaluation, which may help to predict the risk of obese COVID-19 patients progressing to severe and critical disease.
Materials and methods: This retrospective cohort enrolled 213 hospitalized COVID-19 patients. Patients were classified into three groups according to their body mass index (BMI): normal weight (from 18.5 to <24 kg/m2 ), overweight (from 24 to <28 kg/m2 ) and obesity (≥28 kg/m2 ).
Results: Compared with normal-weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination (lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 175.5[34.0-414.9] vs. 261.7[73.3-576.2] vs. 395.8[101.6-1135.6]; p = 0.002), and were more inclined to deterioration at the acute period. At the 5-month follow-up, the lung residual lesion was more serious (residual total lung lesions volume [cm3 ], normal weight vs. overweight vs. obesity; 4.8[0.0-27.4] vs. 10.7[0.0-55.5] vs. 30.1[9.5-91.1]; p = 0.015), and the absorption rates were lower for higher BMI patients (absorption rates of total lung lesions volume [%], normal weight vs. overweight vs. obesity; 99.6[94.0-100.0] vs. 98.9[85.2-100.0] vs. 88.5[66.5-95.2]; p = 0.013). The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration (areas under the ROC curve, 0.884 vs. 0.794, p < 0.05).
Conclusions: Obesity was associated with severe pneumonia lesions on CT and adverse clinical outcomes. The AI-based model with combinational use of clinical and CT parameters had incremental prognostic value over the clinical parameters alone.
Keywords: AI; COVID-19; CT; obesity; prognosis.
© 2022 John Wiley & Sons Ltd.