tetano
Editor, Senior Moderator
Diabetes Metab Res Rev
. 2022 Jan 21;e3519.
doi: 10.1002/dmrr.3519. Online ahead of print.
The association of obesity with the progression and outcome of COVID-19: the insight from an artificial intelligence (AI) - based imaging quantitative analysis on computed tomography
Xiaoting Lu[SUP] 1 2 [/SUP], Zhenhai Cui[SUP] 3 4 [/SUP], Xiang Ma[SUP] 1 2 [/SUP], Feng Pan[SUP] 1 2 [/SUP], Lingli Li[SUP] 1 2 [/SUP], Jiazheng Wang[SUP] 5 [/SUP], Peng Sun[SUP] 6 [/SUP], Huiqing Li[SUP] 3 4 [/SUP], Lian Yang[SUP] 1 2 [/SUP], Bo Liang[SUP] 1 2 [/SUP]
Affiliations
Abstract
Aim: 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[SUP]2[/SUP] ), overweight (from 24 to < 28 kg/m[SUP]2[/SUP] ), and obesity (≥ 28 kg/m[SUP]2[/SUP] ).
Results: Compared with normal weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination [lung lesions volume (cm[SUP]3[/SUP] ), 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 = .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[SUP]3[/SUP] ), 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 = .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 = .013]. The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration [AUC (areas under the ROC curve), 0.884 vs 0.794, P < .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. This article is protected by copyright. All rights reserved.
Keywords: AI; COVID-19; CT; obesity; prognosis.
. 2022 Jan 21;e3519.
doi: 10.1002/dmrr.3519. Online ahead of print.
The association of obesity with the progression and outcome of COVID-19: the insight from an artificial intelligence (AI) - based imaging quantitative analysis on computed tomography
Xiaoting Lu[SUP] 1 2 [/SUP], Zhenhai Cui[SUP] 3 4 [/SUP], Xiang Ma[SUP] 1 2 [/SUP], Feng Pan[SUP] 1 2 [/SUP], Lingli Li[SUP] 1 2 [/SUP], Jiazheng Wang[SUP] 5 [/SUP], Peng Sun[SUP] 6 [/SUP], Huiqing Li[SUP] 3 4 [/SUP], Lian Yang[SUP] 1 2 [/SUP], Bo Liang[SUP] 1 2 [/SUP]
Affiliations
- PMID: 35062046
- DOI: 10.1002/dmrr.3519
Abstract
Aim: 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[SUP]2[/SUP] ), overweight (from 24 to < 28 kg/m[SUP]2[/SUP] ), and obesity (≥ 28 kg/m[SUP]2[/SUP] ).
Results: Compared with normal weight patients, patients with higher BMI were associated with more lung involvements in lung CT examination [lung lesions volume (cm[SUP]3[/SUP] ), 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 = .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[SUP]3[/SUP] ), 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 = .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 = .013]. The clinical-plus-AI parameter model was superior to the clinical-only parameter model in the prediction of disease deterioration [AUC (areas under the ROC curve), 0.884 vs 0.794, P < .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. This article is protected by copyright. All rights reserved.
Keywords: AI; COVID-19; CT; obesity; prognosis.