PDF(Pubmed)
Abstract：
UNASSIGNED: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information.
UNASSIGNED: Multicenter retrospective observational cohort study.
UNASSIGNED: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles.
UNASSIGNED: Coronavirus Disease 2019-confirmed and hospitalized adult patients.
UNASSIGNED: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score.
UNASSIGNED: The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.
UNASSIGNED: Multicenter retrospective observational cohort study.
UNASSIGNED: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles.
UNASSIGNED: Coronavirus Disease 2019-confirmed and hospitalized adult patients.
UNASSIGNED: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score.
UNASSIGNED: The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.
摘要：
未经评估：需要准确的预后评分来预测COVID-19感染成人的死亡率，以了解谁将从住院和更密集的支持和护理中受益最大。我们的目标是开发和验证用于患者分诊的两步评分系统，并使用易于收集的个人信息来识别死亡率风险相对较低的患者。
UNASSIGNED：多中心回顾性观察性队列研究。
UNASSIGNED：弗吉尼亚联邦大学的四个健康中心，乔治敦大学,佛罗里达大学，和加州大学，洛杉矶.
未经批准：2019年冠状病毒病确诊和住院的成年患者。
UNASSIGNED：我们纳入了来自弗吉尼亚联邦大学（VCU）的1,673名参与者作为推导队列。在重复缺失数据填补后，使用多变量逻辑模型和变量选择程序确定住院死亡的危险因素。开发了两步风险评分，以识别较低的患者，中度,和更高的死亡风险。第一步选择增加年龄，不止一种预先存在的合并症，心率>100次/分钟，呼吸频率≥30次呼吸/分钟，和SpO2<93%进入预测模型。除了年龄和SpO2，第二步使用血尿素氮，中性粒细胞绝对计数,C反应蛋白,血小板计数,和中性粒细胞与淋巴细胞比率作为预测因子。C-statisticsreflectedverygooddistinctionwithinternalvalidationatVCU(0.83,95%CI0.79-0.88)andexternalvalidationattheotherthreehealthsystems(range,0.79-0.85）。还推导了一步模型进行比较。总的来说,两步风险评分的表现优于一步风险评分.
UNASSIGNED：广泛使用的两步评分系统，COVID-19患者分诊的护理点数据，在实践中是一种潜在的节省时间和成本的工具。
UNASSIGNED：多中心回顾性观察性队列研究。
UNASSIGNED：弗吉尼亚联邦大学的四个健康中心，乔治敦大学,佛罗里达大学，和加州大学，洛杉矶.
未经批准：2019年冠状病毒病确诊和住院的成年患者。
UNASSIGNED：我们纳入了来自弗吉尼亚联邦大学（VCU）的1,673名参与者作为推导队列。在重复缺失数据填补后，使用多变量逻辑模型和变量选择程序确定住院死亡的危险因素。开发了两步风险评分，以识别较低的患者，中度,和更高的死亡风险。第一步选择增加年龄，不止一种预先存在的合并症，心率>100次/分钟，呼吸频率≥30次呼吸/分钟，和SpO2<93%进入预测模型。除了年龄和SpO2，第二步使用血尿素氮，中性粒细胞绝对计数,C反应蛋白,血小板计数,和中性粒细胞与淋巴细胞比率作为预测因子。C-statisticsreflectedverygooddistinctionwithinternalvalidationatVCU(0.83,95%CI0.79-0.88)andexternalvalidationattheotherthreehealthsystems(range,0.79-0.85）。还推导了一步模型进行比较。总的来说,两步风险评分的表现优于一步风险评分.
UNASSIGNED：广泛使用的两步评分系统，COVID-19患者分诊的护理点数据，在实践中是一种潜在的节省时间和成本的工具。
