Department of Radiology, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.
Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, United States of America.
PLoS One. 2020 Jul 30;15(7):e0236618. doi: 10.1371/journal.pone.0236618. eCollection 2020.
This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment.
本研究旨在基于发病时的临床特征制定风险评分，以预测 COVID-19 患者入住重症监护病房（ICU）和死亡的风险。从 4997 名调查对象中选择了 641 名住院的实验室确诊 COVID-19 患者。我们对入组患者的人口统计学、合并症和初始就诊时的实验室检查结果进行了回顾性病历审查。主要结局为入住 ICU 和死亡。采用 logistic 回归分析确定预测这两个结局的独立临床变量。通过将数据分为 70%用于训练和 30%用于测试来验证模型。使用接受者操作特征分析（ROC）曲线下面积（AUC）评估性能准确性。预测 ICU 入住的 5 个重要变量为乳酸脱氢酶、降钙素原、脉搏血氧饱和度、吸烟史和淋巴细胞计数。预测死亡率的 7 个重要变量为心力衰竭、降钙素原、乳酸脱氢酶、慢性阻塞性肺疾病、脉搏血氧饱和度、心率和年龄。死亡率组中独特地包含心肺变量。风险评分模型在测试数据集上预测 ICU 入住的 AUC 为 0.74（[95% CI，0.63-0.85]，p = 0.001），预测死亡率的 AUC 为 0.83（[95% CI，0.73-0.92]，p<0.001），具有良好的准确性。本研究确定了预测 COVID-19 患者 ICU 入住和死亡率的关键独立临床变量。该风险评分系统可能对一线医生在时间敏感和资源有限的情况下进行临床决策有用。
