Chen Anne, Zhao Zirun, Hou Wei, Singer Adam J, Li Haifang, Duong Tim Q
Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.
Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States.
Front Med (Lausanne). 2021 Apr 29;8:661940. doi: 10.3389/fmed.2021.661940. eCollection 2021.
To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Stony Brook University Hospital (New York) and Tongji Hospital. Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, = 1,002) and testing (20%, = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. None. Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors ( < 0.001). This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration.
为了描述与死亡时间锁定相关的临床变量的时间特征，并使用临床变量建立与新型冠状病毒肺炎相关的死亡预测模型。对与死亡时间锁定相关的临床变量的时间特征进行回顾性队列研究。纽约州立大学石溪分校医院和同济医院。通过聚合酶链反应检测确诊为严重急性呼吸综合征冠状病毒2阳性的患者。纽约州立大学石溪分校医院的数据中的患者用于训练（80%，n = 1002）和测试（20%，n = 250），同济医院（中国武汉）数据中的375名患者用于测试。无。纵向临床变量作为从结局到死亡日（非幸存者）或出院日（幸存者）的天数的函数进行分析。使用最早的显著预测因子构建预测模型。使用曲线下面积（AUC）评估性能。预测模型发现乳酸脱氢酶、淋巴细胞、降钙素原、D-二聚体、C反应蛋白、呼吸频率和白细胞是死亡的早期预测因子。结局前0至9天的AUC分别为：0.99、0.96、0.94、0.90、0.82、0.75、0.73、0.77、0.79和0.73（石溪医院），以及1.0、0.86、0.88、0.96、0.91、0.62、0.67、0.50、0.63和0.57（同济医院）。相比之下，使用入院数据的预测性能较差（AUC = 0.59）。与幸存者相比，非幸存者中大多数临床变量的时间波动，表明生理和生化不稳定，明显更高（P < 0.001）。本研究确定了几个显示与死亡率相关的时间进展的临床标志物。这些变量在结局前几天准确预测了死亡，这提供了客观迹象，表明可能需要更密切的监测和干预以防止病情恶化。
