Yin Zhengrong, Zhou Mei, Xu Juanjuan, Wang Kai, Hao Xingjie, Tan Xueyun, Li Hui, Wang Fen, Dai Chengguqiu, Ma Guanzhou, Wang Zhihui, Duan Limin, Jin Yang
Key Laboratory of Pulmonary Diseases of National Health Commission, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
State Key Laboratory of Environmental Health (Incubating), Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Med (Lausanne). 2022 Jul 4;9:816314. doi: 10.3389/fmed.2022.816314. eCollection 2022.
We intended to establish a novel critical illness prediction system combining baseline risk factors with dynamic laboratory tests for patients with coronavirus disease 2019 (COVID-19).
We evaluated patients with COVID-19 admitted to Wuhan West Union Hospital between 12 January and 25 February 2020. The data of patients were collected, and the illness severity was assessed.
Among 1,150 enrolled patients, 296 (25.7%) patients developed into critical illness. A baseline nomogram model consists of seven variables including age [odds ratio (OR), 1.028; 95% confidence interval (CI), 1.004-1.052], sequential organ failure assessment (SOFA) score (OR, 4.367; 95% CI, 3.230-5.903), neutrophil-to-lymphocyte ratio (NLR; OR, 1.094; 95% CI, 1.024-1.168), D-dimer (OR, 1.476; 95% CI, 1.107-1.968), lactate dehydrogenase (LDH; OR, 1.004; 95% CI, 1.001-1.006), international normalised ratio (INR; OR, 1.027; 95% CI, 0.999-1.055), and pneumonia area interpreted from computed tomography (CT) images (medium vs. small [OR, 4.358; 95% CI, 2.188-8.678], and large vs. small [OR, 9.567; 95% CI, 3.982-22.986]) were established to predict the risk for critical illness at admission. The differentiating power of this nomogram scoring system was perfect with an area under the curve (AUC) of 0.960 (95% CI, 0.941-0.972) in the training set and an AUC of 0.958 (95% CI, 0.936-0.980) in the testing set. In addition, a linear mixed model (LMM) based on dynamic change of seven variables consisting of SOFA score (value, 2; increase per day [I/d], +0.49), NLR (value, 10.61; I/d, +2.07), C-reactive protein (CRP; value, 46.9 mg/L; I/d, +4.95), glucose (value, 7.83 mmol/L; I/d, +0.2), D-dimer (value, 6.08 μg/L; I/d, +0.28), LDH (value, 461 U/L; I/d, +13.95), and blood urea nitrogen (BUN value, 6.51 mmol/L; I/d, +0.55) were established to assist in predicting occurrence time of critical illness onset during hospitalization.
The two-checkpoint system could assist in accurately and dynamically predicting critical illness and timely adjusting the treatment regimen for patients with COVID-19.
我们旨在建立一种新型的危重症预测系统，该系统将基线风险因素与动态实验室检查相结合，用于预测2019冠状病毒病（COVID-19）患者的病情。
我们对2020年1月12日至2月25日期间收治于武汉协和医院西区的COVID-19患者进行了评估。收集患者的数据，并评估疾病严重程度。
在1150例登记患者中，296例（25.7%）发展为危重症。建立了一个基线列线图模型，该模型由七个变量组成，包括年龄[比值比（OR），1.028；95%置信区间（CI），1.004 - 1.052]、序贯器官衰竭评估（SOFA）评分（OR，4.367；95% CI，3.230 - 5.903）、中性粒细胞与淋巴细胞比值（NLR；OR，1.094；95% CI，1.024 - 1.168）、D - 二聚体（OR，1.476；95% CI，1.107 - 1.968）、乳酸脱氢酶（LDH；OR，1.004；95% CI，1.001 - 1.006）、国际标准化比值（INR；OR，1.027；95% CI，0.999 - 1.055）以及根据计算机断层扫描（CT）图像解读的肺炎面积（中等面积与小面积相比[OR，4.358；95% CI，2.188 - 8.678]，大面积与小面积相比[OR，9.567；95% CI，3.982 - 22.986]），用于预测入院时发展为危重症的风险。该列线图评分系统的区分能力良好，在训练集中曲线下面积（AUC）为0.960（95% CI，0.941 - 0.972），在测试集中AUC为0.958（95% CI，0.936 - 0.980）。此外，基于由SOFA评分（值为2；每日增加量[I/d]，+0.49）、NLR（值为10.61；I/d，+2.07）、C反应蛋白（CRP；值为46.9 mg/L；I/d，+4.95）、血糖（值为7.83 mmol/L；I/d，+0.2）、D - 二聚体（值为6.08 μg/L；I/d，+0.28）、LDH（值为461 U/L；I/d，+13.95）和血尿素氮（BUN值为6.51 mmol/L；I/d，+0.55）组成的七个变量的动态变化建立了线性混合模型（LMM），以协助预测住院期间危重症发病的发生时间。
双检查点系统可协助准确、动态地预测COVID-19患者的危重症情况，并及时调整治疗方案。
