He Jialin, Song Caiping, Liu En, Liu Xi, Wu Hao, Lin Hui, Liu Yuliang, Li Qi, Xu Zhi, Ren XiaoBao, Zhang Cheng, Zhang Wenjing, Duan Wei, Tian Yongfeng, Li Ping, Hu Mingdong, Yang Shiming, Xu Yu
Huo-Shen-Shan Hospital, Wuhan, China.
Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.
Front Med (Lausanne). 2021 Oct 18;8:706380. doi: 10.3389/fmed.2021.706380. eCollection 2021.
This study aimed to establish and validate the nomograms to predict the mortality risk of patients with coronavirus disease 2019 (COVID-19) using routine clinical indicators. This retrospective study included a development cohort enrolled 2,119 hospitalized patients with COVID-19 and a validation cohort included 1,504 patients with COVID-19. The demographics, clinical manifestations, vital signs, and laboratory tests of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct the two prognostic nomograms. The nomogram 1 was a full model to include nine factors identified in the multivariate logistic regression and nomogram 2 was built by selecting four factors from nine to perform as a reduced model. The nomogram 1 and nomogram 2 showed better performance in discrimination and calibration than the Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) score in training. In validation, nomogram 1 performed better than nomogram 2 for calibration. We recommend the application of nomogram 1 in general hospitals which provide robust prognostic performance though more cumbersome; nomogram 2 in the out-patient, emergency department, and mobile cabin hospitals, which depend on less laboratory examinations to make the assessment more convenient. Both the nomograms can help the clinicians to identify the patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.
本研究旨在建立并验证列线图，以利用常规临床指标预测2019冠状病毒病（COVID-19）患者的死亡风险。这项回顾性研究包括一个纳入2119例住院COVID-19患者的开发队列和一个纳入1504例COVID-19患者的验证队列。记录了患者入院时的人口统计学、临床表现、生命体征和实验室检查结果以及院内死亡结局。通过向前逐步多因素逻辑回归分析确定与死亡相关的独立因素，并用于构建两个预后列线图。列线图1是一个完整模型，纳入了多因素逻辑回归中确定的9个因素，列线图2是通过从9个因素中选择4个因素构建的简化模型。在训练中，列线图1和列线图2在区分度和校准方面的表现优于多叶浸润、淋巴细胞减少、细菌合并感染、吸烟史、高血压和年龄（MuLBSTA）评分。在验证中，列线图1在校准方面的表现优于列线图2。我们建议在综合医院应用列线图1，其预后性能强大但较为繁琐；在门诊、急诊科和方舱医院应用列线图2，其依赖较少的实验室检查，使评估更方便。这两个列线图都可以帮助临床医生在入院时利用常规临床指标识别有死亡风险的患者，这可能会降低COVID-19的总体死亡率。
