Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.
Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.
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DOI：
10.1016/j.heliyon.2022.e10166
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发表时间：
2022-08
期刊：
影响因子：
4
通讯作者：
Taouli, Bachir
中科院分区：
文献类型：
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作者：
Carbonell, Guillermo;Del Valle, Diane Marie;Gonzalez-Kozlova, Edgar;Marinelli, Brett;Klein, Emma;El Homsi, Maria;Stocker, Daniel;Chung, Michael;Bernheim, Adam;Simons, Nicole W.;Xiang, Jiani;Nirenberg, Sharon;Kovatch, Patricia;Lewis, Sara;Merad, Miriam;Gnjatic, Sacha;Taouli, Bachir
尽管国际社会做出了非凡的努力来抑制传播并了解SARS-CoV-2感染背后的机制，但尚未发现直接适用于临床的可预测生物标志物。最近的研究表明，不同类型的检测对COVID-19结果的预测能力有限。在这里，我们利用胸部计算机断层扫描（CT）结合血浆细胞因子的预测能力，使用机器学习和k倍交叉验证方法预测COVID-19患者住院期间的死亡和最大严重程度。纳入了来自纽约西奈山卫生系统的患者（n = 152），这些患者在入院后5天内进行了血浆细胞因子评估和胸部CT检查。从电子病历中收集人口统计学、临床和实验室变量，包括血浆细胞因子（IL-6、IL-8和TNF-α）。我们发现，单独的CT定量在预测严重程度（AUC 0.81）方面优于死亡（AUC 0.70），而单独的细胞因子测量与严重程度（AUC 0.66）相比更好地预测死亡（AUC 0.70）。当结合使用时，胸部CT和血浆细胞因子是死亡（AUC 0.78）和最大严重程度（AUC 0.82）的良好预测因子。最后，我们提供了一个简单的评分系统（列线图），使用血浆IL-6、IL-8、TNF-α、磨玻璃样混浊（GGO）与充气肺比率和年龄作为新的指标，可用于在住院时监测患者，并帮助医生为COVID-19死亡风险高的患者做出关键决策和考虑。放射学;胸部CT;细胞因子; COVID-19; SARS-CoV-2。
Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19. Radiology; Chest CT; Cytokines; COVID-19; SARS-CoV-2.
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影响因子：
2.1
作者：
Avila RS;Fain SB;Hatt C;Armato SG 3rd;Mulshine JL;Gierada D;Silva M;Lynch DA;Hoffman EA;Ranallo FN;Mayo JR;Yankelevitz D;Estepar RSJ;Subramaniam R;Henschke CI;Guimaraes A;Sullivan DC
通讯作者：
Sullivan DC
影响因子：
5.9
作者：
Lanza, Ezio;Muglia, Riccardo;Balzarini, Luca
通讯作者：
Balzarini, Luca
影响因子：
16.6
作者：
Feng Z;Yu Q;Yao S;Luo L;Zhou W;Mao X;Li J;Duan J;Yan Z;Yang M;Tan H;Ma M;Li T;Yi D;Mi Z;Zhao H;Jiang Y;He Z;Li H;Nie W;Liu Y;Zhao J;Luo M;Liu X;Rong P;Wang W
通讯作者：
Wang W
影响因子：
82.9
作者：
Del Valle DM;Kim-Schulze S;Huang HH;Beckmann ND;Nirenberg S;Wang B;Lavin Y;Swartz TH;Madduri D;Stock A;Marron TU;Xie H;Patel M;Tuballes K;Van Oekelen O;Rahman A;Kovatch P;Aberg JA;Schadt E;Jagannath S;Mazumdar M;Charney AW;Firpo-Betancourt A;Mendu DR;Jhang J;Reich D;Sigel K;Cordon-Cardo C;Feldmann M;Parekh S;Merad M;Gnjatic S
通讯作者：
Gnjatic S
DOI：
10.1007/s11547-020-01291-y
发表时间：
2021-03
期刊：
La Radiologia medica
影响因子：
--
作者：
Caruso D;Polici M;Zerunian M;Pucciarelli F;Polidori T;Guido G;Rucci C;Bracci B;Muscogiuri E;De Dominicis C;Laghi A
通讯作者：
Laghi A