Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy.
Eur Radiol. 2023 Nov;33(11):7756-7768. doi: 10.1007/s00330-023-09702-0. Epub 2023 May 11.
To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.
In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.
The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).
Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.
In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.
• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.
评估 COVID-19 患者入院时胸部 CT 获得的机会性生物标志物在高危患者表型中的价值。
在这项多中心回顾性研究中，分析了 1845 例在入院后 72 小时内进行胸部 CT 检查的连续 COVID-19 患者。每个中心在入院后 30 天和 80 天收集临床和结局数据。排除结局未知的患者。胸部 CT 在一个核心实验室进行分析，并提取机会性数据，包括动脉粥样硬化特征（根据 Agatston 方法的钙评分）、肝脂肪变性（≤40 HU）、肌少症（脊柱旁肌肉 F<31.3 HU，M<37.5 HU）和骨质疏松症（D12 骨衰减<134 HU）。采用方差分析评估治疗和结局的差异。使用多变量二元逻辑回归获得预测模型，并使用 DeLong 检验比较其 AUC。
最终纳入 1669 例患者（年龄 67.5[58.5-77.4]岁），主要为男性 1105/1669，66.2%）和血氧饱和度降低（92%[88-95%]）。肺炎严重程度、高 Agatston 评分、肌少症、肝脂肪变性和骨质疏松症在接受更积极治疗、入住 ICU 和院内死亡的患者中更为常见（均 p<0.05）。包括临床和 CT 变量的多变量模型与仅包括临床变量的模型相比，提高了预测非重症肺炎的能力（AUC 0.801 与 0.789；p=0.0198），预测患者死亡（AUC 0.815 与 0.800；p=0.001）。
胸部 CT 获得的机会性生物标志物可改善 COVID-19 高危患者的特征。
在 COVID-19 患者中，从胸部 CT 提取的心血管代谢风险的机会性生物标志物可改善患者的风险分层。
在 COVID-19 患者中，可从胸部 CT 定量提取与患者合并症相关的多种信息，与氧疗严重程度、入住 ICU 和死亡相关。
在一个由 1669 例 SARS-CoV2 感染患者组成的大队列中，基于胸部 CT 衍生的多参数机会性生物标志物的预测模型优于仅基于临床变量的模型。
从胸部 CT 获得的心血管代谢合并症的机会性生物标志物可改善 COVID-19 患者的风险分层，即使缺乏确定亚临床和先前未知情况的详细临床数据和实验室检查。
