Li Han, Gu Yang, Liu Xun, Yi Xiaoling, Li Ziying, Yu Yunfang, Yu Tao, Li Li
The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China.
Department of Emergency Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, China.
Healthcare (Basel). 2022 Oct 28;10(11):2150. doi: 10.3390/healthcare10112150.
Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage.
脓毒症通常会导致急性呼吸窘迫综合征（ARDS），而ARDS会导致脓毒症患者预后不良。对脓毒症患者的ARDS进行早期预测仍然是一项临床挑战。本研究旨在开发并验证基于胸部计算机断层扫描（CT）影像组学的特征，用于早期预测脓毒症患者的ARDS并评估个体严重程度。在这项双前瞻性观察队列研究中，将开发一种深度学习模型，即脓毒症诱导的急性呼吸窘迫综合征（SI-ARDS）预测神经网络，以从脓毒症患者的胸部CT中提取影像组学特征。数据集将从这些回顾性和前瞻性队列中收集，包括在2015年5月1日至2022年5月30日期间被诊断为符合脓毒症-3定义的400名患者。回顾性队列中的160名患者将被选为发现组以重建模型，回顾性队列中的40名患者将被选为测试组进行内部验证。此外，来自两家医院的前瞻性队列中的200名患者将被选为验证组进行外部验证。将收集与胸部CT、临床信息、免疫相关炎症指标和随访相关的数据。主要结果是开发并验证该模型，预测SI-ARDS的院内发病率。最后，将使用受试者操作特征（ROC）曲线下面积（AUC）、敏感性和特异性，通过内部和外部验证来评估模型性能。目前的研究表明，早期识别和分类SI-ARDS对于改善预后和疾病管理至关重要。胸部CT已被视为识别ARDS的有用诊断工具。然而，当特征性影像学表现清晰呈现时，诊断和治疗的延迟仍无法避免。在这项双前瞻性队列研究中，我们希望开发一种结合影像组学特征和临床特征的新型模型，以便在早期阶段为患者提供易于使用的SI-ARDS发生情况和严重程度的个体化预测。
