Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States.
Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.
Eur J Radiol. 2021 Dec;145:110031. doi: 10.1016/j.ejrad.2021.110031. Epub 2021 Nov 15.
To assess prognostic value of body composition parameters measured at CT to predict risk of hospitalization in patients with COVID-19 infection.
177 patients with SARS-CoV-2 infection and with abdominopelvic CT were included in this retrospective IRB approved two-institution study. Patients were stratified based on disease severity as outpatients (no hospital admission) and patients who were hospitalized (inpatients). Two readers blinded to the clinical outcome segmented axial CT images at the L3 vertebral body level for visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), muscle adipose tissue (MAT), muscle mass (MM). VAT to total adipose tissue ratio (VAT/TAT), MAT/MM ratio, and muscle index (MI) at L3 were computed. These measures, along with detailed clinical risk factors, were compared in patients stratified by severity. Various logistic regression clinical and clinical + imaging models were compared to discriminate inpatients from outpatients.
There were 76 outpatients (43%) and 101 inpatients. Male gender (p = 0.013), age (p = 0.0003), hypertension (p = 0.0003), diabetes (p = 0.0001), history of cardiac disease (p = 0.007), VAT/TAT (p < 0.0001), and MAT/MM (p < 0.0001), but not BMI, were associated with hospitalization. A clinical model (age, gender, BMI) had AUC of 0.70. Addition of VAT/TAT to the clinical model improved the AUC to 0.73. Optimal model that included gender, BMI, race (Black), MI, VAT/TAT, as well as interaction between gender and VAT/TAT and gender and MAT/MM demonstrated the highest AUC of 0.83.
MAT/MM and VAT/TAT provides important prognostic information in predicting patients with COVID-19 who are likely to require hospitalization.
评估 CT 测量的人体成分参数对预测 COVID-19 感染患者住院风险的预后价值。
本回顾性 IRB 批准的两机构研究纳入了 177 例 SARS-CoV-2 感染伴腹盆腔 CT 的患者。根据疾病严重程度将患者分为门诊患者（无住院）和住院患者（住院患者）。两位读者对 L3 椎体水平的轴向 CT 图像进行了盲法分割，以测量内脏脂肪组织（VAT）、皮下脂肪组织（SAT）、肌脂组织（MAT）、肌肉量（MM）。计算 L3 处的 VAT 与总脂肪组织比（VAT/TAT）、MAT/MM 比和肌肉指数（MI）。比较这些指标与严重程度分层的患者的详细临床危险因素。比较了各种逻辑回归临床和临床+影像学模型，以区分门诊患者和住院患者。
有 76 名门诊患者（43%）和 101 名住院患者。男性（p=0.013）、年龄（p=0.0003）、高血压（p=0.0003）、糖尿病（p=0.0001）、心脏病史（p=0.007）、VAT/TAT（p<0.0001）和 MAT/MM（p<0.0001）与住院有关，但 BMI 与住院无关。临床模型（年龄、性别、BMI）的 AUC 为 0.70。将 VAT/TAT 添加到临床模型中可将 AUC 提高到 0.73。纳入性别、BMI、种族（黑人）、MI、VAT/TAT 以及性别与 VAT/TAT 和性别与 MAT/MM 之间的交互作用的最佳模型显示最高 AUC 为 0.83。
MAT/MM 和 VAT/TAT 提供了预测 COVID-19 患者需要住院的重要预后信息。
