The association of obesity with the progression and outcome of COVID‐19: The insight from an artificial‐intelligence‐based imaging quantitative analysis on computed tomography. Issue 4 (1st February 2022)

• Record Type: Journal Article
• Authors: Lu, Xiaoting; Cui, Zhenhai; Ma, Xiang; Pan, Feng; Li, Lingli; Wang, Jiazheng; Sun, Peng; Li, Huiqing; Yang, Lian; Liang, Bo
• Is Part Of: Diabetes/metabolism research and reviews. Volume 38:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-01
• Subjects: AI -- COVID‐19 -- CT -- obesity -- prognosis; Diabetes -- Periodicals; Metabolism -- Periodicals; 616.642
• DOI: 10.1002/dmrr.3519
• Languages: English
• ISSNs: 1520-7552

Abstract

Abstract: 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/m 2 ), overweight (from 24 to <28 kg/m 2 ) and obesity (≥28 kg/m 2 ). Results: Compared with normal‐weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination (lung lesions volume [cm 3 ], 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 [cm 3 ], 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.