PDF(Pubmed)
Abstract：
BACKGROUND: Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT.
METHODS: Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting.
RESULTS: The adapted integrated model classifying patients into \"ICU/no ICU demand\" comprises six radiomics and seven laboratory biomarkers. The models\' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91.
CONCLUSIONS: The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.
METHODS: Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting.
RESULTS: The adapted integrated model classifying patients into \"ICU/no ICU demand\" comprises six radiomics and seven laboratory biomarkers. The models\' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91.
CONCLUSIONS: The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.
摘要：
背景：在第一次SARS-CoV-2大流行期间，严重的病程和高住院率普遍存在。因此，我们的目的是使用从2020年至2021年期间住院的COVID-19患者(n=52)获得的关键数据和材料,研究综合诊断是否有助于识别弱势患者.因此，我们调查了实验室生物标志物的潜力，特别是从胸部CT提取的动态细胞衰变标记无细胞DNA和影像组学特征。
方法：分别进行正向和反向特征选择，以重症监护病房（ICU）为初始目标进行线性回归。进行了三折交叉验证，共线参数减少。该模型适用于逻辑回归方法，并在验证幼稚子集中进行验证，以避免过拟合。
结果：将患者分类为“ICU/无ICU需求”的适应性整合模型包括6个影像组学和7个实验室生物标志物。影像组学的模型精度为0.54，cfDNA为0.47，常规实验室为0.74，和0.87的组合模型的AUC为0.91。
结论：组合模型的性能优于单个模型。因此，整合影像组学和实验室数据显示，在帮助COVID-19患者的临床决策方面具有协同潜力。根据对更大群体进行评估的需要，包括其他SARS-CoV-2变种的患者,确定的参数可能有助于COVID-19患者的分诊。
方法：分别进行正向和反向特征选择，以重症监护病房（ICU）为初始目标进行线性回归。进行了三折交叉验证，共线参数减少。该模型适用于逻辑回归方法，并在验证幼稚子集中进行验证，以避免过拟合。
结果：将患者分类为“ICU/无ICU需求”的适应性整合模型包括6个影像组学和7个实验室生物标志物。影像组学的模型精度为0.54，cfDNA为0.47，常规实验室为0.74，和0.87的组合模型的AUC为0.91。
结论：组合模型的性能优于单个模型。因此，整合影像组学和实验室数据显示，在帮助COVID-19患者的临床决策方面具有协同潜力。根据对更大群体进行评估的需要，包括其他SARS-CoV-2变种的患者,确定的参数可能有助于COVID-19患者的分诊。
