Gerhards Catharina, Haselmann Verena, Schaible Samuel F, Ast Volker, Kittel Maximilian, Thiel Manfred, Hertel Alexander, Schoenberg Stefan O, Neumaier Michael, Froelich Matthias F
Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany.
Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
Microorganisms. 2023 Jul 3;11(7):1740. doi: 10.3390/microorganisms11071740.
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 ( = 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.
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.
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.
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.
在新冠病毒（SARS-CoV-2）大流行的第一波期间，严重病程和高住院率普遍存在。因此，我们旨在研究整合诊断是否有助于利用2020年至2021年期间住院的COVID-19患者（ = 52）的关键数据和材料来识别易感染患者。相应地，我们研究了实验室生物标志物的潜力，特别是动态细胞衰变标志物游离DNA以及从胸部CT中提取的放射组学特征。
以重症监护病房（ICU）住院时间为初始目标，对线性回归进行了单独的向前和向后特征选择。进行了三倍交叉验证，并减少了共线参数。该模型采用逻辑回归方法进行调整，并在一个未经训练的验证子集中进行验证，以避免过度拟合。
将患者分为“需要/不需要入住ICU”的适应性整合模型包括六个放射组学和七个实验室生物标志物。放射组学模型的准确率为0.54，cfDNA模型为0.47，常规实验室模型为0.74，组合模型为0.87，AUC为0.91。
组合模型的表现优于单个模型。因此，整合放射组学和实验室数据显示出协同潜力，有助于COVID-19患者的临床决策。在需要对更大队列（包括感染其他SARS-CoV-2变体的患者）进行评估的情况下，所确定的参数可能有助于COVID-19患者的分诊。
