Tang Ming, Yu Xia-Xia, Huang Jia, Gao Jun-Ling, Cen Fu-Lan, Xiao Qi, Fu Shou-Zhi, Yang Yang, Xiong Bo, Pan Yong-Jun, Liu Ying-Xia, Feng Yong-Wen, Li Jin-Xiu, Liu Yong
Department of Critical Care Medicine, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China.
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China.
World J Clin Cases. 2021 May 6;9(13):2994-3007. doi: 10.12998/wjcc.v9.i13.2994.
The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial.
To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care.
This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model.
Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO)/(FiO × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; = 0.011), PaO/FiO ratio (OR 17.570; 95%CI: 1.117-276.383; = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good.
The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.
2019年冠状病毒病（COVID-19）广泛传播，导致了高发病率和高死亡率。因此，对危重症患者进行早期风险识别仍然至关重要。
制定入院时的预测规则，以识别可能需要重症监护病房（ICU）护理的COVID-19患者。
这项回顾性研究纳入了2020年1月19日至2020年3月14日期间在深圳市第三人民医院通过逆转录-聚合酶链反应确诊为COVID-19的361例患者。应用多因素逻辑回归建立预测模型。基于2019年12月至2020年3月期间武汉亚洲总医院126例患者的数据集，通过受试者工作特征曲线下面积（AUROC）、拟合优度以及包括敏感度、特异度和精确度的性能矩阵，对预测模型的性能进行外部验证和评估。还使用了列线图来直观展示该模型。
在推导数据集和验证数据集中的患者中，分别有38例和9例患者（分别占10.5%和2.54%）发展为重症COVID-19。在单因素分析中，年龄、性别（男性）、吸烟者、体重指数（BMI）、发病至入院时间（>5天）、乏力、干咳、咳痰、气短、乏力以及氧合指数<18（脉搏血氧饱和度，SpO）/（吸入氧分数，FiO×呼吸频率，RR）等21个参数与重症COVID-19呈正相关。在多因素逻辑回归分析中，仅发现包括BMI [比值比（OR）3.939；95%置信区间（CI）：1.409 - 11.015；P = 0.009]、发病至入院时间（≥5天）（OR 7.107；95%CI：1.449 - 34.849；P = 0.016）、发热（OR 6.794；95%CI：1.401 - 32.951；P = 0.017）、查尔森指数（OR 2.917；95%CI：1.279 - 6.654；P = 0.011）、动脉血氧分压/吸入氧分数比值（OR 17.570；95%CI：1.117 - 276.383；P = 0.041）以及中性粒细胞/淋巴细胞比值（OR 3.574；95%CI：1.048 - 12.191；P = 0.042）在内的6个参数是COVID-19的独立预测因素。这些因素被发现是确诊为COVID-19的重症患者的显著危险因素。两个数据集的AUROC分别为0.941（95%CI：0.901 - 0.981）和0.936（95%CI：0.886 - 0.987）。校准特性良好。
所提出的预测模型在ICU中对COVID-19严重程度预测方面具有巨大潜力。它有助于ICU临床医生为目标人群及时做出决策。
