Development and Validation of a Nomogram for Predicting the Risk of Coronavirus-Associated Acute Respiratory Distress Syndrome: A Retrospective Cohort Study.
Development and Validation of a Nomogram for Predicting the Risk of Coronavirus-Associated Acute Respiratory Distress Syndrome: A Retrospective Cohort Study.
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DOI：
10.2147/idr.s348278
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发表时间：
2022
影响因子：
3.9
通讯作者：
Liu, Jialin
中科院分区：
文献类型：
--
作者：
Zhang, Li;Xu, Jing;Qi, Xiaoling;Tao, Zheying;Yang, Zhitao;Chen, Wei;Wang, Xiaoli;Pan, Tingting;Dai, Yunqi;Tian, Rui;Chen, Yang;Tang, Bin;Liu, Zhaojun;Tan, Ruoming;Qu, Hongping;Yu, Yue;Liu, Jialin
自2019年12月在武汉爆发冠状病毒病（COVID-19）以来，它已在全球范围内迅速传播。我们的目的是建立并验证列线图，以预测冠状病毒相关的急性呼吸窘迫综合征（CARDS）的概率。在这项单中心回顾性研究中，在华中科技大学同济医院（中国武汉）使用严重急性呼吸综合征冠状病毒 2 型逆转录聚合酶链反应阳性检测招募了 261 名 COVID-19 患者。这些患者被随机分为训练队列（75%）和验证队列（25%）。列线图中包含的因素是根据训练队列使用单变量和多变量逻辑回归分析确定的。受试者工作特征曲线下面积 (AUC)、一致性指数 (C-index)、校准曲线和决策曲线分析 (DCA) 用于评估列线图在训练和验证队列中的效率。使用列线图确定独立的预测因素，包括空腹血糖、血小板、D-二聚体和 cTnI。在训练队列中，AUC 和一致性指数为 0.93。同样，在验证队列中，列线图仍然显示出很大的区别（AUC：0.92）和更好的校准。校准图还显示出卡片的预测概率和实际概率之间高度一致。此外，DCA 证明列线图在临床上是有益的。根据实验室测试结果，我们建立了 COVID-19 患者急性呼吸窘迫综合征的预测模型。该模型表现出良好的性能，可用于临床早期识别卡片。上海交通大学医学院附属瑞金医院伦理委员会（编号：（2020）临论-34号）。
Since the outbreak of coronavirus disease (COVID-19) in December 2019 in Wuhan, it has spread rapidly worldwide. We aimed to establish and validate a nomogram that predicts the probability of coronavirus-associated acute respiratory distress syndrome (CARDS). In this single-centre, retrospective study, 261 patients with COVID-19 were recruited using positive reverse transcription–polymerase chain reaction tests for severe acute respiratory syndrome coronavirus 2 in Tongji Hospital at Huazhong University of Science and Technology (Wuhan, China). These patients were randomly distributed into the training cohort (75%) and the validation cohort (25%). The factors included in the nomogram were determined using univariate and multivariate logistic regression analyses based on the training cohort. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency of the nomogram in the training and validation cohorts. Independent predictive factors, including fasting plasma glucose, platelet, D-dimer, and cTnI, were determined using the nomogram. In the training cohort, the AUC and concordance index were 0.93. Similarly, in the validation cohort, the nomogram still showed great distinction (AUC: 0.92) and better calibration. The calibration plot also showed a high degree of agreement between the predicted and actual probabilities of CARDS. In addition, the DCA proved that the nomogram was clinically beneficial. Based on the results of laboratory tests, we established a predictive model for acute respiratory distress syndrome in patients with COVID-19. This model shows good performance and can be used clinically to identify CARDS early. Ethics committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (No.:(2020) Linlun-34th).